Principles and Methods in Landscape Ecology: An Agenda for the Second Millennium 3030966100, 9783030966102

Citation preview

Landscape Series

Almo Farina

Principles and Methods in Landscape Ecology An Agenda for the Second Millennium Third Edition

Landscape Series Volume 31

Series Editors Christine Fürst, Martin Luther University Halle-Wittenberg, Halle (Saale), Sachsen-Anhalt, Germany Cristian Echeverria, Universidad de Concepción, Concepción, Chile Henry N. N. Bulley, BMCC, City University of New York, New York, NY, USA

Editorial Board Members Buyanbaatar Avirmed, School of Agroecology, Mongolian University of Life Sciences, Ulaanbaatar, Mongolia Yazidhi Bamutaze, Dept of Geo Geo-Info & Climatic Sci, Makerere University, Kampala, Uganda Bolormaa Batsuuri, National University of Mongolia, Ulaanbaatar, Mongolia Mahamadou Belem, Nazi Boni University, Bobo Dioulasso, Burkina Faso Emiru Birhane, Dept. Land Resources Management, Mekelle University, London, UK Danilo Boscolo, FFCLRP, Departamento de Biologia, Universidade de Sao Paolo, Ribeirao Preto, São Paulo, Brazil Jiquan Chen, Center for Global Change & Earth Observa, Michigan State University, East Lansing, MI, USA Nicola Clerici, Department of Biology, Universidad del Rosario, Bogota, Colombia Marc Deconchat, National Research Institute for Agriculture, Castanet, France Andrés Etter, Fac. de Estudios Ambientales y Rurales, Pontificia Universidad Javeriana, Bogotá DC, Colombia Pawan K. Joshi, School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India Alexander Khoroshev, Dept. Physical Geography & Landscape Sc., Lomonosov Moscow State University, Moscow, Russia Felix Kienast, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland Ramesh Krishnamurthy, Department Landscape Level Planning, Wildlife Institute of India, Chandrabani, Dehradun, Uttarakhand, India Quang Bao Le, International Center for Agricultural Research in the Dry Areas, Cairo, Egypt Yu-Pin Lin, Dept of Bioenvi Systems Engineering, National Taiwan University, Taipei, Taiwan Benjamin Kofi Nyarko, Dept of Geography & Regional Planning, University of Cape Coast, Cape Coast, Ghana Henrique Pereira, German Centre for Integrative Biodiversity Research (iDiv), Martin Luther University Halle-Wittenberg, Leipzig, Sachsen, Germany Alexander Prishchepov, Dept of Geosci & Natural Resource Mgmt, University of Copenhagen, Copenhagen, Denmark Robert M. Scheller, North Carolina State University, Raleigh, NC, USA Kalev Sepp, Inst. Agricultural & Environ. Sciences, Estonian University of Life Sciences, Tartu, Estonia Anton Shkaruba, Inst. of Agricultural & Environ Sciences, Estonian University of Life Sciences, Tartu, Estonia Janet Silbernagel Balster, Silvernail Studio for Geodesign, LLC, Black Earth, WI, USA Ileana Stupariu, Dept. Regional Geography and Environment, University of Bucharest, Bucharest, Romania Raymond Tutu, College of Humanities, Delaware State University, DOVER, DE, USA Teiji Watanabe, Fac of Environmental Earth Sci, A-301, Hokkaido University, Sapporo, Hokkaido, Japan Wei-Ning Xiang, University of North Carolina at Charlotte, Charlotte, NC, USA Qing Zhao, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

Springer’s innovative Landscape Series is committed to publishing high quality manuscripts that approach the concept of landscape and land systems from a broad range of perspectives and disciplines. Encouraging contributions that are scientifically-grounded, solutions-oriented and introduce innovative concepts, the series attracts outstanding research from the natural and social sciences, and from the humanities and the arts. It also provides a leading forum for publications from interdisciplinary and transdisciplinary teams across the globe. The Landscape Series particularly welcomes contributions around several globally significant areas for landscape research, which are anyhow non-exclusive: • Climate and global change impacts on landscapes and ecosystems including mitigation and adaptation strategies • Human Dimensions of Global Change • Biodiversity and ecosystem processes linked to ecosystems, landscapes and regions • Biogeography • Ecosystem and landscape services including mapping, assessment and modelling • Land System Science • Regional ecology (including bioregional theory & application) • Human-Environment Interactions and Social-Ecological Systems & Frameworks (SESF) - including theories, practice and modelling Volumes in the series can be authored or edited works, cohesively connected around these and other related topics and tied to global or regional initiatives. Ultimately, the Series aims to facilitate the application of landscape research and land system science to practice in a changing world, and to advance the contributions of landscape theory and research and land system science to the broader scholarly community.

More information about this series at https://link.springer.com/bookseries/6211

Almo Farina

Principles and Methods in Landscape Ecology An Agenda for the Second Millennium Third Edition

Almo Farina Department of Pure and Applied Sciences The University of Urbino Urbino, Italy

ISSN 1572-7742 ISSN 1875-1210 (electronic) Landscape Series ISBN 978-3-030-96610-2 ISBN 978-3-030-96611-9 (eBook) https://doi.org/10.1007/978-3-030-96611-9 1st edition: © Springer ScienceþBusiness Media B.V. 1998 2nd edition: © Springer ScienceþBusiness Media B.V. 2006 © Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The advancements of the scientific knowledge require continuous efforts to convert these progresses into social, economic, and political actions. Nowadays, as direct consequence of an unprecedent human interference on the functioning of natural systems, a series of environmental crises driven by climatic changes shake the ecological resilience of ecosystems and undermine the stability of economies of entire regions. Human societies, increasingly interconnected, have a growing need to react soon as possible to these environmental challenges that reverberate on the social and economic functioning. The recent SARS-CoV-2 responsible for a pandemic of respiratory illness, called COVID-19, is an example of the consequences of such globalized assets. Landscape ecology aims to integrate and unify scattered and distant knowledge, playing the role of an epistemic bridge between sciences and humanities, favoring the development and the implementation of policies for a more appropriate management of natural resources. However, landscape ecology to be aligned with the times requires a constant revision of theories and operational models. In accordance with this perspective, this edition of Principles and Methods in Landscape Ecology offers an updating of the previous editions and introduces new ecosemiotic concepts and innovative managing procedures. For instance, new concepts like the ecoscape have been introduced in the landscape narrative to reinforce its ecosemiotic background. The ecoscape, proposed as a central paradigm in landscape ecology, is considered strategic to better understanding the environmental complexity and the ecological role of the species and their abiotic and biotic interactions. In particular, the holistic vision of the landscape has been extended behind the traditional visual perception to the acoustic, smelling, tactile, and thermal perception. Each of these ecoscapes returns complex species-specific ecologically integrated mosaics which concur to better understanding the environmental complexity. A particular emphasis has been also reserved to the use of the isoscapes, a spatially explicit prediction of elemental isotopes utilized to map the distribution of species at a broad geographical scale. Timescapes are proposed as a temporal v

vi

Preface

dimension of the landscape where specific processes emerge according to selected intervals of time. A new chapter has been reserved to the synthetic description of the most common categories of landscapes selected according to a gradient of human intrusion. From this analysis emerge the universality and the efficiency of the landscape approach to investigate functions, to understand roles, and to assess the level of ecological resilience and fragility of the environmental systems. To reconciliate nature and humanity, traditional and innovative management procedures have been widely discussed. In particular, from tropical and temperate regions, agro-forestry models, where forest cover is associated to cultivations, have been proposed as a strategy to simultaneously preserve biodiversity, human culture, and environmental identity. This book represents a repository for a large collection of theories, models, and perspectives inspired by different sciences like ecology, semiotics, complex systems, and cybernetics, utilized in the real world to improve quality and sustainability of human life. The epistemological metamorphosis of landscape ecology toward a science of complexity, beginning in the 1990s thanks to the seminal contribution of scholars like Zev Naveh in Europe and Richard Forman in the USA, has been accelerated in this edition. In fact, upon completion of the previous editions, the book has been further enriched with new ecological, semiotical, and social perspectives representing a cultural “passepartout” for approaching and interpreting the complex scenarios created by an unstoppable human intrusion. The book has relevant ingredients for better understanding the challenges that human societies face due to fragmentation of natural ecosystems, the consequent erosion of biodiversity, and the risks of irreversible effects at the level of the ecological complexity produced by climate change. Finally, to increase consistency of the topics described, a rearrangement of the sequence of chapters has been made, and particular effort has been reserved to improve the accessibility of concepts and terms to assure the heritage from the previous editions of a high didactic profile for a worldwide audience.

Acknowledgment I owe a particular debt of thanks to my friend and colleague Tim Mullet (Ecologist, Kenai Fjords National Park, U.S. National Park Service) for providing me with important comments and useful suggestions in many parts of this edition, helping also to improve the style and the understanding of chapters.

Urbino, Italy

Almo Farina

Contents

1

2

Principles and Methods in Landscape Ecology: An Agenda for the Second Millennium . . . . . . . . . . . . . . . . . . . . . . 1.1 Landscape Ecology: An Ecological Discipline in Progress . . . . . 1.2 The Contribution of Different Disciplines to the Creation of the Landscape Ecology Paradigm . . . . . . . . . . . . . . . . . . . . . 1.3 An Ontology for the Landscape (A Gallery) . . . . . . . . . . . . . . . 1.4 The Epistemological Approach to the Landscape . . . . . . . . . . . . 1.4.1 The Dual Nature of Landscape . . . . . . . . . . . . . . . . . 1.4.2 The Role of Landscape . . . . . . . . . . . . . . . . . . . . . . . 1.5 The Description of Landscape . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 The Geographical Landscape . . . . . . . . . . . . . . . . . . . 1.5.2 The “Ecological” Landscape . . . . . . . . . . . . . . . . . . . 1.5.3 The Cognitive Landscape . . . . . . . . . . . . . . . . . . . . . 1.5.4 Behavior and Landscape . . . . . . . . . . . . . . . . . . . . . . 1.5.5 Semiotic Landscape . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 5 8 10 10 12 12 13 18 31 34 35

Ecoscape vs. Landscape: Riding a Transition . . . . . . . . . . . . . . . . 2.1 Introducing the Ecoscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Subjectivity and Objectivity . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 An Epistemological Classification of the Umwelt . . . . . . . . . . 2.4 Timingscape: The Temporal Patches . . . . . . . . . . . . . . . . . . . 2.4.1 Physical Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Geological Time . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Biological Time . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.4 The Semiotic Time . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.5 Ecological Time . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.6 Landscape Time . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 The Semioscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Ecological Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

43 43 44 45 46 48 48 48 49 51 51 54 57

. . . . . . . . . . . . .

1 1

vii

viii

Contents

2.7 2.8 2.9 2.10

3

The Eco-Field Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Sensoryscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Animal Movements in the Landscape . . . . . . . . . . . . . . . . . . . . Visionscape and the Aesthetic Dimension of the Vivoscape . . . . 2.10.1 Topographic Prominence . . . . . . . . . . . . . . . . . . . . . 2.10.2 Landscape Aesthetics as Ecological Indicators . . . . . . 2.11 The Psychological Landscape . . . . . . . . . . . . . . . . . . . . . . . . . 2.12 Mystery in Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.13 The Thermalscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.14 The Odorscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.15 The Touchscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.16 The Soundscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.16.2 The Classification of Environmental Sounds . . . . . . . . 2.16.3 The Soundscape and the Ecoacoustics Theory . . . . . . 2.16.4 The Importance of Soundscape in the Landscape Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.16.5 Ontological Aspects of the Soundscape . . . . . . . . . . . 2.16.6 Some Theoretical Hypothesis on Ambient Acoustics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.16.7 Acoustic Community . . . . . . . . . . . . . . . . . . . . . . . . 2.16.8 Acoustic Habitats . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.16.9 The Ecoacoustic Events . . . . . . . . . . . . . . . . . . . . . . 2.16.10 The Acoustic Eco-Field . . . . . . . . . . . . . . . . . . . . . . 2.16.11 Noise and Acoustic Pollution of a Soundscape . . . . . . 2.16.12 Choruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.17 The Isoscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58 63 64 65 69 69 72 73 73 77 85 86 86 87 88

Theories and Models Incorporated in Landscape Ecology . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 The Emergence of the Complexity . . . . . . . . . . . . . . . 3.3 Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Intentional and Unintentional Information . . . . . . . . . 3.3.2 Private and Public Information . . . . . . . . . . . . . . . . . 3.3.3 Abiotic and Biotic Information . . . . . . . . . . . . . . . . . 3.3.4 The Ecosemiotics of Probabilistic Information . . . . . . 3.3.5 Meaning and Information . . . . . . . . . . . . . . . . . . . . . 3.3.6 Information as an Universal Property . . . . . . . . . . . . . 3.3.7 Information as a Measure of Probability . . . . . . . . . . . 3.3.8 Information-Processing Performance of Systems . . . . . 3.4 Cognition and Autopoiesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 The Hierarchy Theory and the Structure of the Landscape . . . . . 3.5.1 Vertical Structure . . . . . . . . . . . . . . . . . . . . . . . . . . .

111 111 112 115 119 122 122 122 124 124 126 127 128 130 131 132

90 90 91 92 93 93 94 95 95 96 98

Contents

3.5.2 Horizontal Structure . . . . . . . . . . . . . . . . . . . . . . . . The Percolation Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Metapopulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.2 Dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.3 Examples of Metapopulation Structure . . . . . . . . . . . 3.7.4 Metapopulation, Conservation Biology, and Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 The Source-Sink Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.2 Pseudo-Sinks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.3 Ecological Traps . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.4 Seasonal Source-Sink Habitats . . . . . . . . . . . . . . . . 3.8.5 Ecological Maladaptation . . . . . . . . . . . . . . . . . . . . 3.8.6 Source-Sink Dynamic and Conservation Issues . . . . . 3.8.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . 3.9 The Theory of Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9.1 Concluding Comments . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 3.7

4

5

Scaling Patterns and Processes Across Landscapes . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Landscape Organization and Scaling Approach . . . . . . . . . . . . 4.3 Some Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Grain Size, Extent, and Scaling . . . . . . . . . . . . . . . . . . . . . . . 4.5 Changing the Importance of the Parameters at Different Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Scaling the Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Change in Perception Scale . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 The Multiscale Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Moving Across Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10 Assessing Landscape Scale of Analysis . . . . . . . . . . . . . . . . . 4.11 Examples of Scales in Landscape and in Ecologically Related Disciplines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.11.1 Scaling the Quaternary Landscape . . . . . . . . . . . . . . 4.11.2 Scaling Patterns: The Catchment Scale . . . . . . . . . . . 4.11.3 Scaling Abiotic Processes: Hydrological Processes and Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.11.4 Scaling Evidences in Animals . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emerging Processes in the Landscape . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix

. . . . . .

133 134 137 137 139 139

. . . . . . . . . . . .

140 140 140 141 142 142 143 143 145 145 150 150

. . . . .

157 157 158 160 160

. . . . . .

161 161 162 162 164 166

. 167 . 167 . 169 . 169 . 170 . 172 . . . .

177 177 179 179

x

Contents

5.2.2 Snow Cover, an Example of Abiotic Disturbance . . . . 5.2.3 Gap Disturbance in Forest . . . . . . . . . . . . . . . . . . . . . 5.2.4 Fire Disturbance in Landscapes . . . . . . . . . . . . . . . . . 5.2.5 Pathogen Disturbance in Forest Landscape . . . . . . . . . 5.2.6 Animal Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.7 Human Disturbances . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Fragmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Scale and Patterns of Fragmentation . . . . . . . . . . . . . 5.3.3 Community Composition and Diversity in Fragments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Species, Guilds, and Fragmentation . . . . . . . . . . . . . . 5.3.5 Habitat Fragmentation and Extinction . . . . . . . . . . . . 5.3.6 Nest Predation and Fragmentation . . . . . . . . . . . . . . . 5.3.7 Island Size and Isolation: A Key to Understand Fragmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.8 Habitat Fragmentation and Animal Behavior . . . . . . . 5.3.9 Measuring the Effects of Fragmentation . . . . . . . . . . . 5.3.10 The Complexity and Unpredictability of Fragmented Landscape . . . . . . . . . . . . . . . . . . . . . . . 5.4 Connectivity, Connectedness, and Corridors . . . . . . . . . . . . . . . 5.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Corridors: Structure and Functions . . . . . . . . . . . . . . . 5.5 Soil Landscape and Movement of Water and Nutrients Across Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Soil Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 The Role of Riparian Vegetation in Nutrient Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.4 Origin, Composition, and Flux of Dissolved Organic Carbon in a Small Watershed . . . . . . . . . . . . 5.5.5 Leaf Litter Movements in the Landscape . . . . . . . . . . 5.5.6 Spatial Patterns of Soil Nutrients . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Emerging Patterns in the Landscape . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Landscape Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Scale and Ecological Neighborhoods . . . . . . . . . . . . . 6.2.3 Disturbance and Heterogeneity . . . . . . . . . . . . . . . . . 6.2.4 Heterogeneity and Animals . . . . . . . . . . . . . . . . . . . . 6.2.5 Spatial Heterogeneity and Prey–Predator Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.6 Foraging Efficiency and Heterogeneity . . . . . . . . . . . .

181 183 184 187 187 188 191 191 194 196 200 203 205 206 207 207 209 210 210 212 215 215 215 219 220 221 221 222 233 233 234 234 236 238 240 243 243

Contents

7

8

xi

6.2.7 Heterogeneity and Resource Use by Birds . . . . . . . . . 6.2.8 Quantify Spatial Heterogeneity . . . . . . . . . . . . . . . . . 6.3 Ecotones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 The Importance and Role of Ecotones . . . . . . . . . . . . 6.3.3 An Historical View of Ecotone Paradigm . . . . . . . . . . 6.3.4 Difficulties Studying Ecotones . . . . . . . . . . . . . . . . . . 6.3.5 Spatiotemporal Scales and Hierarchy of Ecotones . . . . 6.3.6 Ecotone Classification . . . . . . . . . . . . . . . . . . . . . . . . 6.3.7 Structural and Functional Character of the Ecotones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.8 External Controls in the Creation and Maintenance of Ecotones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.9 Internal Controls in the Creation and Maintenance of the Ecotones . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.10 Characters of the Ecotones . . . . . . . . . . . . . . . . . . . . 6.3.11 The Role of Ecotones in the Landscape . . . . . . . . . . . 6.3.12 The Role of Ecotones in Generating and Maintaining Diversity . . . . . . . . . . . . . . . . . . . . . . . . 6.3.13 Climatic Changes and Ecotones . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

245 246 246 246 247 249 249 250 251

Principles of Landscape Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Landscape Ontogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Stability in Landscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Self-Organizing Mechanisms and Landscapes . . . . . . . . . . . . . . 7.5 Landscape and Soil-Shaping Factors . . . . . . . . . . . . . . . . . . . . 7.6 Landscape Changes in Human Perturbed Areas . . . . . . . . . . . . . 7.6.1 Agriculture Intensification . . . . . . . . . . . . . . . . . . . . . 7.6.2 Agriculture Abandonment . . . . . . . . . . . . . . . . . . . . . 7.6.3 Fire Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.4 Deforestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.5 Livestock Grazing . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.6 Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Patterns in Landscape Changes: Some Examples . . . . . . . . . . . . 7.8 Mediterranean Landscapes as an Example of Perturbation-Dependent Homeorethic Systems . . . . . . . . . . . . . 7.8.1 Patterns and Processes in Land Abandonment . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

269 269 272 274 274 275 276 276 278 278 279 281 282 283

252 253 254 255 258 259 260 261

286 291 295

Principles for Landscape Conservation, Management, and Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303

xii

Contents

8.2

Landscape Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Landscape Indicators . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Predictive Landscape Models . . . . . . . . . . . . . . . . . . 8.3 Principles for Landscape Management . . . . . . . . . . . . . . . . . . . 8.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 The Importance of Watershed-Scale Management . . . . 8.3.3 The Role of Keystone Species in Landscape Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Nature Conservation and Landscape Ecology . . . . . . . . . . . . . . 8.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.2 Landscape Principles for Nature Reserves . . . . . . . . . 8.4.3 Disturbance Regime and Reserve Design . . . . . . . . . . 8.4.4 Inter-Refuge Corridors . . . . . . . . . . . . . . . . . . . . . . . 8.4.5 Hedgerows System to Conserve Biodiversity in Rural Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.6 Greenways and Safety in Urban Landscape . . . . . . . . 8.5 Conservation in Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.2 Conserving Focal Species . . . . . . . . . . . . . . . . . . . . . 8.5.3 Conserving Dispersing Vectors . . . . . . . . . . . . . . . . . 8.5.4 Conservation in Agricultural Landscape . . . . . . . . . . . 8.5.5 Conservation of Fragmented Habitats . . . . . . . . . . . . . 8.5.6 Conserving Large Carnivores . . . . . . . . . . . . . . . . . . 8.5.7 The Conservation of Western Palearctic Stopover Migratory Birds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Landscape Restoration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Landscape Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Hierarchical Structure of System, Biodiversity Conservation, and Ecological Debt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.9 Spatially Explicit Modeling Approach Applied to Animal Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.10 The “Landscape Species” Approach . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Human-Dependent Landscapes Around the World – An Ecological Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Urban Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Main Characters . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Extension and Human Population . . . . . . . . . . . . . . 9.2.3 Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.4 Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.5 Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.6 Level of Sustainability . . . . . . . . . . . . . . . . . . . . . . 9.3 Farming Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . .

304 306 306 306 306 308 308 309 309 310 310 313 313 314 315 315 316 316 317 317 322 323 324 325 325 327 329 330 339 339 340 341 341 342 342 343 343 345

Contents

10

xiii

9.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 Some Farming Landscape Characters . . . . . . . . . . . . 9.3.3 Farming and Reclamation in Tropics . . . . . . . . . . . . 9.3.4 Modern Versus Traditional Farming Systems . . . . . . 9.3.5 Agroforestry in the Tropics . . . . . . . . . . . . . . . . . . . 9.4 The Cultural Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.1 Characteristics of a Cultural Landscape . . . . . . . . . . 9.4.2 Interaction Between Natural and Cultural Landscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.3 The Fragility of the Cultural Landscapes . . . . . . . . . 9.4.4 The Cultural Keystone Species . . . . . . . . . . . . . . . . 9.4.5 Ethnographic Landscape . . . . . . . . . . . . . . . . . . . . . 9.4.6 Two Examples of Cultural Landscape . . . . . . . . . . . 9.5 Freshwater Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5.2 Stream and River Landscapes . . . . . . . . . . . . . . . . . 9.5.3 Ponds and Lakes . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5.4 Wetlands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5.5 Mangrove Forests . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 Mining and Energy Landscape . . . . . . . . . . . . . . . . . . . . . . . . 9.6.1 Mining Landscape . . . . . . . . . . . . . . . . . . . . . . . . . 9.6.2 Energy Resources Landscape . . . . . . . . . . . . . . . . . 9.7 The Hybridscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7.2 Spatial Patterns and Resources in Hybridscape . . . . . 9.7.3 Complexity in Hybridscape . . . . . . . . . . . . . . . . . . . 9.7.4 Uncertainty in Hybridscape . . . . . . . . . . . . . . . . . . . 9.7.5 Information and Meaning in Hybridscape . . . . . . . . . 9.7.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8 Therapeutic Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . .

345 346 347 349 350 356 356

. . . . . . . . . . . . . . . . . . . . . . .

358 359 360 361 361 364 364 365 367 370 372 373 373 373 377 377 379 379 380 381 382 383 386

Methods in Landscape Ecology . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Metrics in Landscape Ecology . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Nonspatial Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Spatial Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.3 Patch Shape Metrics . . . . . . . . . . . . . . . . . . . . . . . . 10.2.4 Distance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.5 Texture Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.6 The Semivariance . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.7 Boundaries Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.8 Fragmentation Metrics . . . . . . . . . . . . . . . . . . . . . . 10.3 The Fractal Geometry Approach . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . .

401 401 403 405 407 409 413 413 415 416 417 417 417

xiv

Contents

10.3.2 10.3.3 10.3.4 10.3.5

The Fractal Dimension of the Edges . . . . . . . . . . . . The Fractal Dimension of Patches . . . . . . . . . . . . . . Semivariance and Fractal Analysis . . . . . . . . . . . . . . Examples of Application of Fractal to Animal Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 The Geographic Information Systems . . . . . . . . . . . . . . . . . . . 10.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.2 The Representation of the Spatial Information . . . . . 10.4.3 Map Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.4 Procedures for Cartographic Handling and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.5 Capturing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.6 Some Cartographic Modeling Procedures . . . . . . . . . 10.4.7 Commands in GIS . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.8 GIS and Remote Sensing . . . . . . . . . . . . . . . . . . . . 10.4.9 Scaling in GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suggested Readings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . .

. 420 . 421 . 426 . . . . .

427 430 430 432 432

. . . . . . .

433 433 434 434 435 435 435

. . . . . . . . . . .

441 441 442 442 443 443 444 445 445 446 446

Chapter 1

Principles and Methods in Landscape Ecology: An Agenda for the Second Millennium

Synthesis Landscape ecology has a long history in scientific research and humanities with an epistemology rich in different interpretations and meanings that often produces not coincident syntheses. A landscape can be considered a domain, a system, or a unit. Apparently heterogeneous and fragmented, the land mosaic literally connects not only environmental patches of different composition, morphology, and functions but reunifies distant disciplines and theoretical perspectives. The landscape is an entity that can be approached according to a geographical, ecological, or cognitive perspective. The geographical perspective is the result of the physical description of the land and of the distribution of rivers, lakes, glaciers, and other geographical forms. The ecological perspective considers a landscape as the environmental context for every living. According to a species-specific cognitive perspective, a landscape may be considered in turn a latent entity, a sensed entity, or an interpreted entity and source of semiotic agents. Finally, from a human perspective, landscapes are cultural, social, economic, contemplative, and aesthetic patchworks.

1.1

Landscape Ecology: An Ecological Discipline in Progress

Ecological theory has recently pointed out the potential roles that multiple interrelated disciplines have in understanding the many facets of ecological systems (Klink et al., 2002). Space is recognized as a new frontier of ecology (Kareiva, 1994) with the landscape representing one of the main components of this “space” (Thompson et al., 2001). The focus of landscape ecology is the study of patterns and processes that occur across terrestrial systems in geographical and semiotical space. As such, the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Farina, Principles and Methods in Landscape Ecology, Landscape Series 31, https://doi.org/10.1007/978-3-030-96611-9_1

1

2

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Fig. 1.1 Alexander von Humboldt (1769–1859), portrait by Friedrich Georg Witsch. (Berlin State Museum)

arrangement of abiotic objects distributed over land combines with the network of interactive, and often interdependent, behaviors produced by biological organisms to create complex ecological processes. These interactions play out over a variety of spatial scales. The multi-faceted arena of landscape ecology has subsequently given rise to many scientific disciplines and humanities (Li, 2000; Farina, 2004). Unfortunately, the framework of landscape ecology and the many disciplined branches it has generated has often been associated with an uncertain epistemology, confusing semantics, and many unresolved questions surrounding the plasticity and variety of ecosystems and their components (O’Neill, 2001). It is now time to shift this restrictive and confused paradigm into a new framework that better represents the complexity and spatial arrangement of nature. Landscape ecology is one of the youngest branches of ecological study. The roots of this discipline lie deep in geography, botany, hydrology, biosemiotics, ecosemiotics, and land management. Two hundred years ago, the German geographer and scholar, Alexander von Humboldt (Fig. 1.1), regarded the landscape as “the total character of a region.” The term “landscape ecology” was coined by the German biogeographer Carl Troll toward the end of the 1930s. Troll hoped that a

1.1 Landscape Ecology: An Ecological Discipline in Progress

3

new science could be developed that combined the spatial “horizontal” approach of geographers with the functional “vertical” approach of ecologists. Landscape ecology was born in Western Europe as a human-related science (Naveh & Lieberman, 1984, 1994) that would expand after World War II to countries in central and Eastern Europe (Schreiber, 1990) and later to the Americas and Asia (Forman, 1990). In 1986, the field of landscape ecology was introduced by Zev Naveh and by Frank Golley to a large audience of ecologists at the 4th International Congress of Ecology in Syracuse, NY. Since then, the presence of landscape ecology sessions in international ecology congresses has become customary (Farina, 1999). The International Association of Landscape Ecology (IALE) (www.landscape-ecology.org) has since assured a continuum of international congresses and symposia. Because of the scope, complexity, and multidimensionality of research questions, landscape ecology has assumed the definitive characteristics of a distinct, unique, dynamic, and integrated global science. Landscape ecology is resoundingly accepted by scientists for its considerable and innovative contributions to ecological theory and practice (Risser et al., 1984; Forman & Godron, 1986; Turner, 1989; Farina, 1993, 1998, 2000; Wiens et al., 1993; Forman, 1995; Moss, 2000; Turner et al., 2001). Landscape ecology is defined by the International Association of Landscape Ecology (2008) as “the study of the interactions between the temporal and spatial aspects of a landscape and its ora, fauna and cultural components.” The landscape, as a defined and measurable component, consists of a mosaic of ecosystems and elements that exist across the conditions of space. Landscape paradigms are often utilized to explain complex phenomena like the transmission of diseases, for instance, the recent COVID-19 pandemic (Agnoletti et al., 2020), species distributions (Vasudev et al., 2015) and habitat use (Chapin et al., 1998), and plant-landscape relationships (Hernandez-Stefanoni, 2005). For instance, landscape configuration has been found to be one of the relevant factors affecting the transmission of hantavirus in deer mice (Peromyscus maniculatus) (Langlois et al., 2001). Landscape-based, epidemiological models have incorporated the demography of wildlife populations with their relationships to the spatial structure of land mosaics (Langlois et al., 2001). Similarly, the principles of landscape ecology have also been used to interpret the phenological phases of plants in the context of their relationship with landscape features (Ahas & Aasa, 2001). These landscape-scale studies have enhanced the response of health and human services to disease and expanded our understanding of population ecology and assessment of plant responses to land use change (Barnett & Stohlgren, 2001). Since its initiation, the development of landscape ecology has been a progressive, dynamic, and global process (Wu & Hobbs, 2002) that has crossed and fertilized many fields of science including geography, botany, zoology, animal behavior, semiotics, cognitive ecology, and landscape architecture. More recently, the concepts of landscape ecology has also given rise to the newly emerging fields of ecoacoustics and soundscape ecology (Farina, 2014a, b; Sueur & Farina, 2015; Gage, 2017). Due to the functional scale of landscape-based research, subjects are implicitly rooted in socioeconomic and ecological processes. Such an

4

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

interconnected, macroscale-based approach allows for the focused experimentation and understanding of processes and relationships between the environment and organisms across an explicit space (land mosaic). These concepts are still largely undeveloped 30 years later (Wiens, 1992a). More recently, special attention has been paid to the environmental complexity as a general theory in which landscape ecology can offer a very important contribution. The complexity issue today is at the center of theoretical and empirical attention (Kauffman, 1993; Merry, 1995; Cilliers, 1998; Lewin, 1999; Bossomaier & Green, 2000; Manson, 2001; Weng et al., 1999, Fisher & Pruitt, 2020).

1.2

The Contribution of Different Disciplines to the Creation of the Landscape Ecology Paradigm

Landscape ecology is one of the most promising ecologically related disciplines because it is highly differentiated due to its multidimensionality of applied and theoretical approaches. Yet, at its core, there remains a common “soul” based on the finite dimension of physical space and the interrelatedness of study subjects. Applying the variable of space in ecological theory is an important component in determining the distribution and diversity of life. What unveil as a result are the ecological patterns and processes that have common forcing functions. As such, the ecological phenomena playing out across landscapes may be considered to be one of the most in uential evolutionary drivers that shape our world. Therefore, introducing the context of space in the realm of ecology is fundamental to a more accurate description of reality (Silbernagel, 2003). The organization of ecological systems according to a nested hierarchy (Allen & Starr, 1982; O’Neill et al., 1986) has strongly contributed to the link between different paradigms and theories that extend the concept of scale to the landscape dimension. The issue of scale in the context of landscapes often appears in scientific literature ranging from soil science (Buol et al., 1989) to current perspectives in geoecology (Huggett, 1995) and geomorphology (Malanson, 1993). The theory of island biogeography (MacArthur & Wilson, 1967) and the focus on ecological geography (MacArthur, 1972) are two fundamental theories that have contributed to the theoretical basis and development of the landscape ecology. Contemporarily, new concepts such as fractal geometry (Mandelbrot, 1975) have been introduced in the ecological narrative (Milne, 1991, 1992, 1995; Kenkel & Walker, 1993; Johnson et al., 1995; Milne et al., 2000) to investigate the complexity of nature. Such complexity, especially in living systems, has long deterred many landscape ecologists from researching ecosystems along marine-terrestrial interfaces like coasts and marshes. Novel concepts grounded in landscape ecology concerning heterogeneity (Kolasa & Pickett, 1991) and the role of disturbance regimes (Pickett & White, 1985) in ecological processes have advanced our knowledge on fundamental paradigms like

1.3 An Ontology for the Landscape (A Gallery)

5

ecotones (Hansen & di Castri, 1992; Naiman & Decamps, 1990; Gosz, 1993; Risser, 1995), habitat connectivity (Merriam, 1984), metapopulation models (Gilpin & Hanski, 1991; Hanski & Gilpin, 1991, 1997; Hanski, 1999), and the percolation theory (Stauffer, 1985; Ziff, 1986). Additionally, the context of landscape heterogeneity recognizes that there exists a patchwork of habitat quality that lends support to the source-sink paradigm (Pulliam, 1988, 1996). While landscape ecology enhances the support of these aforementioned theories, as well as other fields like information theory (Stonier, 1990, 1996), biosemiosis (Hoffmeyer, 1997; Kull, 1998a, b; Nöth, 2005), autopoiesis (Maturana & Varela, 1980), and eco-semiotics (Maran, 2020), more recent advancements in ecological thought and empirical evidence have given rise to landscape-based research fields such as soundscape ecology (Farina, 2014a, b) and ecoacoustics (Sueur & Farina, 2015). These latter fields widen the breadth of conceptualizing in describing landscapes as a complex, self-regulating system.

1.3

An Ontology for the Landscape (A Gallery)

Landscape can be considered a level of functional complexity of aquatic and terrestrial environments where matter, energy, information, and semiosis are considered according to their spatial dimension (Risser et al., 1984). Landscapes may be considered in accordance to a multiplicity of simultaneous perspectives that varies by region and geographical orientation where physical units are different for some organisms than others. Humans may experience this same difference simply based on administrative boundaries or a scenic vista. Semiotically, the ecological ow of information is expressed holistically by the landscape creating the perfect conditions for producing a novel union between nature and humanities. Such a combined semiosis can positively in uence environmental policies and promote well-being in human societies (Hoffmeyer, 1996; Barrett et al., 2009; Ausonio, 2015). Since landscapes are conceptually recognized both by ecologists and humanities scholars (Forman & Godron, 1986; Farina, 1998, 2021; Nassauer, 1995; Turner et al., 2001; Haber, 2004), landscapes serve as a concept and scale to which the natural sciences and humanities are linked. Subsequently, humans and natural processes are finally considered as inseparable components of the Earth’s ecological complexity, launching future development in compliance with environmental sustainability (Lindstrom et al., 2011). A landscape is composed of physical and cognitive objects that are delimited by geographical coherences with strong material and symbolic relationships. In particular, the landscape is a mosaic of patches composed of plant and animal assemblages where its shape and spatial arrangement are the result of the “ecological memory” of threats and disturbance events that occur across different scales of time and space (Pickett & White, 1985; Laundré et al., 2001, 2010). The landscape is a holistic domain in which different and often con icting perspectives collide in the arena of human development and habitat restoration, sustainable resource use and human

6

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

well-being, and the land rights of humans over wildlife. These con icts arise more simply because they are all integrated cohabitants of landscapes. Unfortunately, such landscape-related con icts are only part of the growing impact of global climate change and con icts over ecological health and resiliency with economic policies and political motivations. The concept of “landscape” in ecology is still too young to provide a thorough and accurate definition. We will later clarify the reason for this “benevolence” in light of the complex framework from which the landscape dimension pertains. In order to fulfill the definition of “landscape,” we have to accept these premises of complexity and respect a wide core of interdisciplinary experiences in geography, geoecology, geobotany, general ecology, behavioral ecology, landscape architecture and planning, anthropology, archeology, bio- and ecosemiosis, ecological coding, environmental psychology, aesthetic, ecoacoustics, and many more. It is the multidimensionality and interdisciplinary aspects of the study of landscape that allow for the convergence of these fields to find the landscape as a common meeting place. For this reason, there are several definitions of landscape from different cultural and scientific perspectives to review: “the total character of a region” (von Humboldt, 1849); “landscapes will deal with their totality as physical, ecological and. geographical entities, integrating all natural and human (“caused”) patterns and processes . . . (Naveh, 1987); “landscape as a heterogeneous land area composed of a cluster of interacting ecosystems that is repeated in similar form throughout (Forman & Godron, 1986); a particular configuration of topography, vegetation cover, land use and settlement pattern which delimits some coherence of natural and cultural processes and activities” (Green et al., 1996); “a piece of land which we perceive comprehensively around us, without looking closely at single components, and which looks familiar to us (Haber, 2004).”

We find this last definition to be broader than the others and more suitable to define the landscape as a living entity “perceived” by all other living organisms. This opens a promising field of new research and hypotheses on the importance of spatial arrangement of patterns and processes for the life of individual organisms, populations, and communities and for the totality of functioning ecosystems. Recently, the Council of Europe Landscape Convention defines the landscape as “an area, as perceived by people, whose character is the result of the action and interaction of natural and/or human factors.” It also provides that each party shall undertake “to recognise landscapes in law as an essential component of people’s surroundings, an expression of the diversity of their shared cultural and natural heritage, and a foundation of their identity” (Council of Europe, 2000). The augmentation of human intrusion has penetrated every part of our planet resulting in the contamination of many ecological processes that not only significantly reduce their effectiveness and relevance but also contribute to impacts at the scale of the biosphere (Crutzen & Stoermer, 2000). Most landscape ecology deals with human-modified ecosystems characterized by a land mosaic well perceived at the human-perceptual scale. Such an emphasis on this subject is inevitable due to widespread distribution of human populations across the earth. However, principles

1.3 An Ontology for the Landscape (A Gallery)

7

Fig. 1.2 A land mosaic of a farm resulting from human cultural land use processes in Northern Apennines, Italy. (Credit Farina)

of landscape ecology can also be applied to a broad range of ecological processes of pristine areas, such as rainforests and arctic environments, where spatial relationships are not well defined and heterogeneity is more finely grained. Landscape ecologists typically study the complexity of systems in the context of how organisms interact with their patchily sensed environment. This approach provides a frame of reference that allows landscape variables to be more accurately defined and understood within the semiosis of organismal responses (Turner et al., 1995). A more anthropocentric approach deprives the scientific method from conceptualizing systems complexity and species dependency because the scale of landscape in uence and perception differs significantly from that of a human and that of a beetle (Wiens & Milne, 1989). Hence, when the organism is human, the landscape is a broad-scaled area composed of a mosaic of patches, ecotopes, and distinct cultural elements (Fig. 1.2). However, when, for instance, we are dealing with the beetle landscape, we are required to reduce our physical and biological point of view to that of a beetle. For instance, Fig. 1.3 portrays a small lake where secondary succession and water dynamics have created a patchy landscape that is easily within the realm of human perception. If we were to look into the insect realm of perception, only a portion of the landscape would be adequately considered. In this format, landscape ecology offers an extraordinary opportunity to carry out new epistemological and empirical experiences. Such an endeavor requires the contribution of different disciplines to understand the complexity of nature. The field of landscape ecology possesses many ecological branches of study that make it capable of investigating the multiple scales and semiotic in uences that affect a variety of organisms. However, it is important to clarify all these perspectives into one robust unitarian theory of “landscape” in order to reduce confusion and

8

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Fig. 1.3 The panoramic view of the small lake of Agnolo (Northern Italy) where we distinguish riverine woodland grass ecotone and free water. In this case, the patchiness is the result of a natural process of ooding alternating with periods of water scarcity and algae eutrophication. (Credit Farina)

contradiction. This book’s latest edition increases the breadth of “landscape” and devotes its focus on the epistemological approach to illustrate foundational landscape theories and fundamental semiotic tenets.

1.4

The Epistemological Approach to the Landscape

Due to its complex and multifaceted roots, the ecological study of landscapes may be approached in several ways (Naveh & Lieberman, 1984; Forman & Godron, 1986; Forman, 1995; Zonneveld, 1995; Farina, 1998, 2000, 2006, 2021). However, it is time to reconciliate the different approaches that have represented the evolution of landscape ecology over the years. Historically, this discipline was born at the level of human perception, and the first descriptions of principal processes were strictly linked to human life and to the structural modification of land cover. More recently, landscape ecology has advanced to study of the spatial arrangement of patterns and processes dealing with soil, vegetation, animals, and humanity. A formidable development in

1.4 The Epistemological Approach to the Landscape

9

philosophy and practice has emerged. This is especially true in North America, where scientists conduct geographic and experimental investigations on what effect spatial configuration has among land mosaics (Wu & Hobbs, 2002). However, despite the tremendous progresses in the empirical field, landscape ecology shows a seemingly permanent fragility on the side of scientific theory. During the last 10 years, new tools and a number of investigations have clarified many aspects of the relationships between geographically perceived patterns and ecological processes, but very few attempts have been done to increase the theoretical framework (Wiens, 1992b; Antrop, 2001; Tress & Tress, 2001; Haber, 2004) (Fig. 1.4). For this reason, we consider to be beneficial to use well-established ecological principles like the General System Theory (von Bertalanffy, 1969), the theory of complexity, and the information framework (Farina, 2004; Benbya & McKelvey, 2006) to centralize the theoretical framework of the landscape ecology. Under these principles, scientific investigations and experiments can be approached with a foundation of ecological tenets that drive the scientific method and where results can build upon a scaffolding of theory intended to explain natural phenomena. Below, we will address our vision of the landscape according to different epistemological perspectives. We recognize at least three operational approaches: The dual nature of landscape The role of landscape The description of landscape

Theory LUP LE

Tools Landscape types Application field Change and history Methods Structural aspects Themes 0

5

10

15

20

25

30

Fig. 1.4 Importance (%) of main concept groups noted in the Journal of Landscape Ecology (LE: 1987–1999) and in Landscape and Urban Planning (LUP: 1986–1999). (From Antrop, 2001, with permission)

10

1.4.1

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

The Dual Nature of Landscape

A landscape can be considered as a material and immaterial entity in which organisms integrate both aspects of its nature. The material components are represented by the physical context of abiotic (rocks, soil, water, atmosphere) and biotic objects (microbes, plants, animals). The immaterial component is expressed by information (Stonier, 1990, 1996) that nourishes semiotic webs that can appear with different levels of organization. The immaterial component is responsible for any cognitive reaction to a landscape’s arrangement. In the following pages and chapters, we will try to separately maintain the word “landscape” to embody the combination of material and immaterial properties in nature and the word “mosaic” to represent the material properties alone. Unfortunately, this promise cannot be wholly maintained everywhere due to the impossibility to distinguish this holistic vision with those visions of authors that are directly quoted.

1.4.2

The Role of Landscape

In order to solve the problem of linking together the material and immaterial components of the landscape, we have to consider the landscape at least under three different perspectives: landscape as a domain, landscape as a system, and landscape as a unit. This categorical perspective supports the aforementioned concepts that the complexity of a landscape does not have a unique address but a “family” of possible paradigms. More importantly, such complexity is not per se self-explaining so it is necessary to use theories, paradigms, and models to evaluate and explain just a small piece of complexity at a time before understanding the landscape in its entirety.

1.4.2.1

Landscape as a Domain

The spatial and temporal domain in ecology is the universe in which a process evolves or is maintained by internal and external forces (Farina, 2004; Estes et al., 2018). If we consider the landscape as a “gestalt” entity, composed of physical and conceptual components, the landscape domain is the field of existence of all the rules, processes, and related patterns. The landscape domain possesses within it subdomains that can be further aggregated in distinct families of meta-domains. The deconstruction of the landscape domain into its nested categories enables us to describe the finer-scale complexity of meta-domains and subdomains as an attempt to interpret the larger-scale complexity held by the landscape domain. We describe at least three hierarchical levels of subdomains. Level 1 shares the competencies between the physical and the conceptual realms (e.g., geography, ecological rules). Level 2 represents the macro-processes (e.g., economics, religion,

1.4 The Epistemological Approach to the Landscape

11

government, culture, environment). Level 3 is shared by the disciplines (e.g., ecology, geology, ethology, management, social sciences). Every level contributes to explaining certain components of the overall landscape complexity. This vision could seem too far from the everyday empirical scientist, but such a perspective must be considered as one of the most important epistemological references.

1.4.2.2

Landscape as System

The vision of landscape as a system of elements connected to each other by energy, matter, or information is very close to the vision obtained with adopting the ecosystem approach (Tansley, 1935). However, the distinctiveness of the landscape system from the ecosystem primarily consists of the geographically explicit dimensions in which the first is embedded. When a landscape is considered as a system, we must recognize the presence of elements connected by energetic, informative, and semiotic mechanisms that create that system. For instance, such systems can be represented by geomorphological entities like sand dunes, cliffs, and river islands or by the patchwork of plant communities. The relationships between these different landscape components create a mosaic-like system that have a great in uence on landscape functioning at spatial scales often relevant for management (Oehri et al., 2020). Consistent with this structure are the physical properties of how elements are composed (size, shape, internal composition) and their geographical position (distance from others, rate of exchange of chemical and biological components).

1.4.2.3

Landscape as a Unit

Defining a unit means that such an entity is distinguished from the background. A landscape is considered a unit when it is possible to delimit borders, to describe characters, and to assign a distinct function inside a matrix of distinct landscapes. In such a way, the spatial scale of reference should be large enough to distinguish the units from the surroundings. The landscape units are characterized by emergent autopoietic properties (sensu Maturana & Varela, 1980). In fact, units are autonomous systems with the capacity to self-regulate and auto-maintain. This assumption seems quite questionable for an entity composed of the myriad of items that define a landscape, but the self-organization of such a unit is a matter of fact. For instance, a city, despite its internal heterogeneity, can be considered a landscape unit with specific characteristics and with unique functional processes. At the same time, a coffee plantation with all the infrastructures and logistics may be considered a landscape unit if considered in the context of a complex agroecological landscape (Perfecto et al., 2019). There are several empirical indications that the landscape (as a unit) possesses many unique functional actions or processes, such as feedback and autocatalytic mechanisms. The ecotope can be considered one of the simplest landscape units. An ecotope, as a functional and independent unit, can be structured by natural as well as human processes. An olive orchard, for instance, could be

12

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

considered an example of an ecotope. If we were to enlarge the spatial scale beyond the orchard, we can assume, perhaps, that the orchard ecotope lies within the Tuscany landscape which is a unit inside the mosaic of landscapes throughout Italy (Sereni, 1961). The unicity and heterogeneity of the Tuscany landscape is the result of internal processes among smaller landscape units based on regional climate, land tenure, cultural and social processes, hydrology, etc. (Vos & Stortelder, 1992). Moving across a broad range of spatial scales, we can distinguish macroregions, like northeast Italy, central Italy, southern Italy, and so on.

1.5

The Description of Landscape

Landscape can be described according to geographic, ecological, or cognitive perspectives. All these perspectives are considered in the literature, but there is often confusion and overlapping of terms. To reduce such confusion but adhere to these three descriptive views, we have introduced the term “geographical landscape” to describe a system represented by physical forms and their spatial relationships. We termed “ecological landscape” a space dominated by ecological processes and their dynamics. We subsequently use the term “cognitive landscape” as a set of objects perceived according to the subjective reality surrounding every individual organism.

1.5.1

The Geographical Landscape

The geographical landscape refers to land forms and related processes like landslides, waterfalls, erosion zones, and forests patches depicted as heterogenous areas where the evolution of land forms and their biological inhabitants follow a specific storytelling based on the natural variation of disturbance regimes and human intervention (Fig. 1.5). Landscape is synonymous with territory, region, plain, hilly, or mountain range. This is the most popular identification of the landscape, and according to this perspective, the distribution of developed areas, the organization of cities, and logistic infrastructures are included as well. A good example of the geographical landscape perspective is evident with the contrast between the land characteristics of the Alps mountain range landscape and the Padanian (Po valley) lowland landscape in Northern Italy. In the former case, the topographic complexity of the land creates several climatic, soil, and vegetative constraints that have in uenced a variety of different habitats only a few kilometers apart. In the latter case, we need to travel several kilometers to find the same level of variety where habitat differences are dispersed across a broader spatial scale. If we were to compare the Padanian valley to the Great Plains of the central United States, the space between vegetative and habitat differences are dispersed even further from

1.5 The Description of Landscape

13

Fig. 1.5 Example of a hypothetical landscape classified according to a hierarchy of hydrological regions, geomorphology, and vegetation structure. (From Canters et al., 1991, with permission)

one another. Such spatially oriented differences across the planet’s surface are justifiably segmented over geographical scales. Such observable, and even measurable, differences of the geographical landscape allows ecologists to classify land characteristics according to their geographic location which provides an advantage for comparative studies and analyses. The geographical-based as such, Meeus (1995) suggests 30 categories of landscape throughout Europe according to multiple criteria in which morphology, climate, and land use create distinct units based on geographic orientation (Fig. 1.6).

1.5.2

The “Ecological” Landscape

We assume that the landscape represents the abiotic and biotic context in which organisms are living. In this context, the landscape can be characterized through simulating a fixed world where organisms could be present or absent. To describe the landscape, we assume that the patterns of how the land is configured available for all species present and that the emergent properties of the land mosaic are also shared by all the organisms according to the species-specific sensorial capacity and habitat preferences. Vegetation is a significant landscape component that creates the layout

14

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Fig. 1.6 Distribution of pan-European landscape types. (From Meeus, 1995, with permission). (1) Arctic tundra. (2) Forest tundra. (3) Boreal swamp. (4) Northern taiga. (5) Central taiga. (6) Southern taiga. (7) Subtaiga. (8) Nordic highlands. (9) Mountains. (10) Atlantic bocage. (11) Atlantic semi-bocage. (12) Mediterranean semi-bocage. (13) Atlantic open fields. (14) Continental open fields. (15) Aquitaine open fields. (16) Former open fields. (17) Collective open fields. (18) Mediterranean open land. (19) Cultura promiscua. (20) Montado/Dehesa. (21) Delta. (22) Huerta. (23) Polder. (24) Kampen. (25) Poland’s stripfields. (26) Puszta. (27) Steppe. (28) Semi-desert. (29) Sandy-desert. (30) Terraces

and template on which other organisms find their “habitat.” Several studies in landscape ecology implicitly deal with this approach, in particular, under a land management perspective. Vegetation creates the major layout, the template on which other organisms find their “habitat.” Several researches in landscape ecology implicitly deal with this approach, in particular under a land management perspective. The concept of the landscape conceived as an ecological entity is a vision especially emphasized by the American school of landscape ecology (Farina, 1993). Under this view, spatial patterns are recognized to have a relevant role in ecosystem processes. This sets the stage for perceiving an ecological landscape as an assemblage of patches of different evolutionary origins, histories, inter-relational dynamics, and species compositions. Heterogeneity plays a pivotal and dominant role affecting ecological processes and the distribution and abundance of plants and animals (Kolasa & Pickett, 1991). This represents the spatially explicit extension of

1.5 The Description of Landscape

15

the ecosystem concept (Turner, 1989; Forman & Godron, 1986; Forman, 1995). Accordingly, a landscape can be considered to be the spatial composition of species’ habitats in which the dimension, shape, and distribution of abiotic and biotic components are unequally distributed across space creating a matrix of habitat quality and species occupancy (Gutzwiller & Anderson, 1992; Bissonette, 1997). Classification represents a relevant procedure to study land mosaics, especially under the human perspective. In fact, this approach is generally carried out by landscape ecologists interested in studying the interaction between human activity and the landscape. It is particularly useful for planning the boundaries of natural land reserves and, in general, as competent guidance to many types of land management. There are no precise rules to classification. As such, the rules for one classification scheme often change with that of another according to the purposes, temporal and spatial scale of investigation, and available financial resources. Important sources of information for developing land classifications rely heavily on remote sensing data such as aerial photographs, satellite digital images, and infrared imagery. More traditional land-information sources include analog cadastral maps, topographic maps, geologic survey maps, hydrologic and watershed maps, and soil maps. Many of these sources are now digitally georeferenced in space and time. Landscape ecologists compile and harmonize this material within Geographic Information Systems (GIS), enabling the production of different types of thematic maps, as well as spatial analyses addressing a variety of landscape-related questions. Landscape classification schemes present a way to understand geographic diversity in a way that is explanatory. Within many classification schemes, there is an underlying tenet that landscapes possess a patchwork of landscape components that collectively contribute to the whole. Based on this assumption, we consider patchiness to be a common component of an ecological landscape which allows us to introduce at least five distinct typologies of patches: structural patches, functional patches, habitat patches, corridors, and resources patches. Especially in European literature, patches are considered as holistic units and distinguished into physiotopes and ecotopes.

1.5.2.1

Structural Patches

Structural patches are generally composed by a soil type overlaid with vegetation type.

1.5.2.2

Functional Patches

Functional patches are represented by a homogeneous area distinguished by a specific function within the landscape or by a physical descriptor such as altitude, temperature, moisture, and light penetration.

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

16

1.5.2.3

Habitat Patches

Habitat patches are related to the inter- and intraspecies relationships between plants and animals. In this context, habitat patches may be defined as an area with distinct plant and animal communities that are dispersed across a discrete spatial matrix of interacting individuals based on the functional role of ecological niches that are constrained by the availability of preferred resources (Ostfeld, 1992). Different groups of organisms can share the same habitat patch.

1.5.2.4

Corridor Patches

Corridor patches are land units utilized by species to move from one territory to another. Although the definition of corridors and their use is controversial, a corridor patch may be defined as a portion of the land mosaic that is used by an organism to move, explore, disperse, and/or migrate. Often, the corridor concept is conceived as a narrow strip of land connecting one habitat patch to another. Generally, we associate corridors to a special feature that is generally considered outside the habitat occupied by an organism but serves a role in their life history.

1.5.2.5

Resource Patches

Resource patches are predominantly related to animal ecology. In this case, the presence and availability of resources distributed over space within a landscape can be described as a combination of resource patches where each patch is considered an essential part for an animal’s life history. Resource patches are discrete portions of an animal’s habitat in which food, nesting sites, roosting locations, or refuges are available.

1.5.2.6

Physiotope

Physiotope may be defined as a spatial unit characterized by relatively homogeneous abiotic factors. Generally, a physiotope is classified using descriptors like geology, aspect, and slope. The physiotope is the basis for further “landscape” classification according to land formations and arrangements (Vos & Stortelder, 1992). Soils physiotopes are also classifiable, the elementary unit is the pedon, and polypedon is considered as a grouping of contiguous pedons. The bond between different polypedons may be sharp or gradual. The physiotope may be considered a pedon plus other edaphic and microclimatic characters.

1.5 The Description of Landscape

1.5.2.7

17

Ecotope

Ecotope is considered to be a unit wherein structural patches, functional patches, habitat patches, corridors, resource patches, and physiotopes all combined to represent a single entity of interrelated components (Tansley, 1939). Much like landscape classification, ecotope classification is subjective according to the question(s) at hand or based on a specific function of interest. The ecotope concept can be used with an anthropocentric perspective and bound according to human perception. In reality, this concept can be applied more in general to a classification of the landscape that includes plant and animals although many classifications are proposed (as reported by Naveh & Lieberman, 1984; Woodmansee, 1990; Zonneveld, 1995). In many human-modified landscapes, the spatial arrangement of vegetation and land uses are so bound by human activity that the land mosaic is indisputably patchy and where the contrast between patches is so high that it is difficult to imagine a spatial arrangement of the patches outside the context of human in uence. Take, for instance, the patchwork of agricultural fields and hedgerows and the organized sprawl of suburban areas contrasted with the edges of forests, fields, and wetlands. Plants, animals, and microorganisms appear to have no other choice than to occupy these human-created mosaic patches, yet in some cases particular species have adapted quite well to inhabit these seemingly unnatural landscapes. For instance, invasive plants like dandelion species (Taraxacum sp.) and animals like the Norway rat (Rattus norvegicus) have become quite widespread, and problematic, occupants of human-modified landscapes in the United States. As we have demonstrated, landscape classifications are subject driven and scale dependent. Chorology is the study of places and regions based on the scale and subject of interest (usually human) across the globe. In our case, we can introduce our chorological focus to consider landscapes as the subject of interest where spatial scale drives organization methodologies. With this in mind, the landscape of any geographic location can be organized according to a hierarchical scale of chorological site clusters. The higher level (coarse-scale) clusters are represented by land systems, regions, ecoregions, climatic zones, etc. At the lowest level (finestscale) clusters, we find the physiotope of geological layers, sedimentation, and soils. It should be noted that this manner of organization is highly dependent on the scale for which the clusters may be organized. For instance, the high-level cluster of climatic zones when measuring the impacts of climate change is often scaled globally. Yet micro-climates for small mammals at the lower level of clustering are also apparent. The anthropocentric classification adopted by different authors according to the human perception can be easily converted when the landscape concept is used to describe vegetation and animal patterns. Instead of “dogmatic” con icts in landscape ecology, we often find many convergences. The chorological hierarchy of site clusters continues to emphasize an anthropocentric landscape that is systematically designed to describe, and perhaps assume,

18

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

the different processes that have created and structured landscape features. A further possibility of understanding the hierarchical structure of a landscape is to distinguish microchores, mesochores, and macrochore. These terms that pertain a geographical narrative are overlapping in part with patches and ecotopes terms. An olive orchard may be considered as an ecotope, and the combination of olive orchard + alfa-alfa field + woodlot may be also considered a microchore which corresponds to a farm in the Northern Apennines, in Italy. A sequence of farms of this type, the so-called cultura mista appenninica (Sereni, 1961) composes a mesochore that could represent a parish. The combination of mesochore creates a macrochore. This classification is functional to human use but has also a relevant ecological meaning, offering a lot of information for applications. This land classification links human socioeconomic structure to the environmental resource allocation. However, this model works well in a rural perspective; moving to urban and industrial landscape, the hierarchical factors change, losing the “ecological” (sensu stricto) feedback. The information enclosed in this last adopted classification can be measured using the species diversity (sensu Whittaker, 1977). The alfa diversity can measure the microchore complexity, the beta diversity measures the mesochore, the gamma diversity measures the macrochore, the delta diversity measures the complexity of a region, and, finally, the epsilon diversity measures the complexity between regions (Naveh, 1994). An example of levels and type of species diversity is presented by Wiens (1989). This author has measured the diversity of birds according to different area aggregation. It is clear that these choices are arbitrary and depend on the goal of the study, but it is universally accepted that different levels of the ecological hierarchy possess a different information and play different ecological and functional roles.

1.5.3

The Cognitive Landscape

Most landscape ecology studies are based on the description of patterns and processes that organisms are able to perceive using bio- and remote (extra-soma) sensing. When we think about a biological entity positioned at the center of the “real world,” the organism reality is based solely on what its senses can detect and its neurological capacity to process sensory input. This means that each individual has its own “perceived reality” that is partially, but not entirely, separated from “true reality”. von Uexküll (1940) firstly called this subjective world “Umwelt.” According to this vision, organisms have three possibilities of how they can interact with their surroundings (see also Ohta, 2001). 1. Latent landscape (LL) – no interactions with a landscape. 2. Sensed landscape (SL) – the landscape is perceived by a direct scanning of senses if sensory data.

1.5 The Description of Landscape

19

3. Interpreted landscape (IL) – the information obtained by SL is elaborated by cognitive processes that transform the landscape into an interactive environment (Farina et al., 2005). The latent landscape can be considered as the portion of landscape that is outside the perceptive capacity of species. For instance, ultrasounds are not perceived by human hearing, and they have no effect on our life, but still exist for other species, eventually. In the same way, infrared radiation is invisible to human sight. Because of this phenomenon, LL is species specific, creating a perceptive barrier between species. In this way, carnivorous bats and birds can share the same habitat and prey (e.g., insects) by adopting different foraging strategies, one with echolocation and supersonic hearing abilities and the other with diurnal sight or with olfactory cues. The true reality of the universe has created a conceivably unlimited source of new resources. As a result, evolution by means of natural selection has given rise to billions of different species for over 3 billion years since life began. It is this diversity of sensory data and genetic variation that allows new sensorial capacities to generate novel environmental characters and, in turn, perpetuates the evolutionary processes and dynamics of the ecosphere. The sensed landscape is an emergent function resulting from the way organisms decodify their surroundings using somatic sensors. Evolutionary adaptive mechanisms are implicated in this process and are driven by genetic processes. Later, we will discuss in greater detail about this perceptive modality that represents the direct and central link between the external and the internal world of every organism. Finally, the interpreted landscape is the representation of an organism’s “real world” (the interpreted reality) after a perception process has been applied, usually by a species’ cultural (learned or instinctive) filter. The interpreted landscape incorporates an organism’s genome, its experience, and its application of what it has learned. Organisms that acquire, process, and respond to information through learning apply cultural mechanisms that ensure their environments are predictable on the scale of their abilities and understanding of threats and location of resources. Such cultural filtering of environmental stimuli is transmitted to future generations, thus creating an accumulation of knowledge and enhancing genomic and cultural dynamics. Considering the dramatic explosion of human knowledge, it is easy to predict the complexity and breadth of the interpreted landscape for humanity. Throughout human evolution, IL has changed according to the human cultural background and by individual responses from personal experience (see, f.i., Eisler et al., 2003). Although IL, in this context, pertains mainly to humans, it can also be extended to many other organisms where experience and learning is used to interact with innate behaviors. For instance, young migrating birds have difficulty selecting suitable stopover habitats but correct this challenge as they get older through learning (Németh & Moore, 2007). Corvids and rodents can problem-solve cognitive puzzles to obtain food rewards (Heinrich & Bugnyear, 2005; O’Connor et al., 2014), and chimpanzees (Pan paniscus) pass down a number of collectively useful skills to their offspring (Gruineisen et al., 2017).

20

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Understanding the functional attributes that create the structure of landscapes is not automatic but requires information about how organisms perceive and react to landscape structure (Collinge & Palmer, 2002). Based on this concept, an additional hypothesis of landscape classification can be done where we can assume that humans, plants, and animals all have a different perception of the environment. Starting from this standpoint, we can order our interest in landscape processes according to one of these three organismal groups. A further distinction could be done for virus, bacteria, fungi, algae, and protozoa. Plant perception must be more generally considered as the range of its sensitivity to information from its surrounding environment and its capacity to incorporate it into a life strategy (Gagliano et al., 2014). This approach takes into account the direct connection to plants’ ability to adapt, colonize, and survive in response to natural and human-related stress. Plants, on this front, are sensible to soil structure and nutrient content, humidity, subterranean, and surface competition with other plants for nutrients, water, light, wind exposure, and herbivory (Chamovitz, 2020). Plant behavior consists of the rapid modification of their morphological traits like root systems, branch exposition, leaf dimension and size, etc. through natural selection in order to gain adaptive advantages. The animal perception of a landscape depends on the different adaptation that different species adopt to track resources and to improve adaptation. Different sensory tools return different landscapes where animal size is a relevant discriminant factor (Fig. 1.7). The human perspective includes perception, values, and culture, all strictly connected by biosemiotic processes (Nassauer, 1995). According to this perspective, landscape is desegregated and grouped again according to functional entities that have a meaning for the human life. The approaches utilized by plants, animals, and humans thrive in the world are not in con ict with each other. Each organism’s cognitive perspective explores an ensemble of patterns and processes that, in the last analysis, are parts of the whole biological and ecological complex system where each one plays a role. There are more commonalities than differences among these groups with regard to space and the spatial arrangement of processes and patterns. Although these concepts are not always apparent in landscape ecology studies, landscape ecologists are quite aware of the significant and unique interactions between the human dimension and nature and how the actions resulting from human perceptions of the land have altered the Earth unlike any organism to exist. However, we are also more aware now than ever about the profound interdependence of these systems that humans are indelibly a part of. Today, the latent, sensed, and interpreted perceptions and their subsequent actions (or non-action) have a common theoretical basis in biosemiotics. Such concepts can then explain the landscape closure relationship between LL, SL, and IL which can be applied differently to plants, animals, and humans. This approach is important because every form of life uses, in some extension, a cognitive process to interpret and respond to information from LL, SL, and IL. As such, all organisms have in common autopoietic characters that create either a separation between the internal

1.5 The Description of Landscape

21

Fig. 1.7 Three examples of organism-centered landscapes: (a) hedgehog (Erinaceus europaeus), (b) fox (Vulpes vulpes), (c) wolf (Canis lupus). Grain and extent appear species specific. In the Northern Apennines, the hedgehog landscape is represented by prairies and shrubs bordered by woods (the size of area is approximately 300  300 m). The fox landscape is represented by prairies, fields, and dense and open woodlands (the size area is approximately 3  3 km). The wolf landscape embraces a catena range and is represented by prairies and pastures, woodlands of different types, clearing, and edges (the size of the living area is approximately more than 10  10 km)

and the external world (see for more details Maturana & Varela, 1980) or a continuity with it.

1.5.3.1

Anthropocentrism, Complexity, and the Ecosphere

The human dimension of landscape is probably one of the more intriguing fields due to the overlap and deeply interwoven relationships between biological, ecological, and cultural components of humanity. This dimension is related to processes having a broad temporal and spatial scale that spans thousands of years and has affected the entire planet. The “biological” dimension of humans may be compared with similar dimensions applied to nonhuman animals, but the cultural component of humanity is unique and distinct. The peerless attributes of human culture often make it the most significant interacting human component with the environment. This is especially true when we consider the long-perceived anthropocentric ideology of man over nature in many human cultures. Such an attitude of dominance, combined with extraordinary technologies, has given humans the capacity to destroy entire ecosystems, in uence evolutionary processes, and drive species to extinction. This level of

22

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

human-environmental interaction has changed the course of Earth’s history as we have now entered the Anthropocene Epoch (Crutzen & Stoermer, 2000). The complexity of the environment is so high that the landscape approach presents only a partial understanding (i.e., a model) to the holistic interconnectedness of nature. Landscape ecology does not have the capacity to explain all the processes within a given system. Yet, it undoubtedly can understand these processes more holistically better than other ecologies as the complexity created by the interrelationships between different processes and patterns are the primary focus of investigation. The common denominator of landscape ecology is the spatial dimension of ecological processes and their importance to other spatial and nonspatial processes in accordance with neighboring characters. Furthermore, the main strength of landscape ecology is the transfer of information across different families of processes that occur at different spatial and temporal scales inside a spatial context. However, there is inherent risk to consider in the field of landscape ecology because an exclusive anthropocentric point of view is very high at the moment. This narrow perspective can produce a dogmatic discipline deprived of theoretical and experimental verifications that is not applicable to the true philosophies of ecological sciences. Perhaps redeeming to the human-centered views of landscape ecology is that spatial dimensions of ecological processes are still recognized as extremely important. This allows us to progress from an ecosystemic, topological vision of ecological functioning to a chorological approach in which the real world is studied for its spatiality with a more holistic vision and intention. On the other hand, simplifying the science of landscape ecology as “ecology at broad scale” is a reductive view that fails to account for the emerging functions of ecological systems across spatial scales. In its simplest form, we can consider the Earth to be composed of land systems, water systems, and air systems. Plants, animals, and humans are all components of these systems that can be placed within a spatial context of landscapes, seascapes, and, more general, airscapes. The ecological processes interacting within and between all three of these “-scapes” collectively make up an “eco-scape” (see the seminal work of Backhaus & Muringi, 2006). This premise is extremely important to understand the choice of arguments and their position in this book. Landscape ecology may be considered a new science, especially for people addressing the human perspective in an ecological realm, or may be considered an advanced approach of ecology if geobotanical and animal perspectives are selected. In the first case, the study of human-related processes accompanied by processes created by other abiotic or biotic entities (e.g., microbes, plants, animals) originate from a more sophisticate pseudo-virtual context that is greatly appreciated by scientists working in disciplines like anthropology, sociology, environmental psychology, and architecture. The latter forms the foundation for understanding the realm of plant and animal and abiotic ecologies that are sought by soundscape ecologists, behavioral ecologists, environmental chemists, and bio- and eco-semioticians. Both fields of interest attempt to understand the complexity of the environment at some assigned level, in our case, within a spatial context.

1.5 The Description of Landscape

23

Whatever the point of view being taken, these studies provide us a clearer picture of the ecoscapes and the dynamics of our planet. The human and animal perspectives have some common points when the scale of perception of animals is close to that of human. In this case, all “benefits” and “disturbances” produced by humans that shape and/or control landscapes are shared by some groups of animals (Getzner, 2020). A simple example of this would be the clearing of a forest for an alfalfa field. Cutting down 100 hectares of old growth forest would certainly be a disturbance for species that thrive in old-growth conditions, while other species that thrive along edge habitats would benefit from such activities. However, the way the landscape is cleared will likely have differential effects on species. The landscape design carried out by experienced landscape architects and planners can change the availability of resources to favor, for instance, some organisms on which humans have more interest (f.i., mammals, birds, or butter ies). Figure 1.8 is an example on the way to create more complexity and edges in a restored rural landscape in the United Kingdom (Forestry Commission, 1991). Many high scenic landscapes are very attractive for tourists as well as for wildlife (see Schmid, 2001; Mooser et al., 2018). Some caution should be introduced about this approach because they oftentimes neglect ecological criteria and models. The benefits are shared between humans and the animals adapted to live with them. Unfortunately, several species are not tolerant to human environments and have very different habitat preferences that otherwise reduce their survivorship in human-altered environments. In fact, such environmental architecture is unrealistic, improbably, or even impossible, when there is a request to restore a system that has been drastically altered by human activity. This is typical for the restoration of old-growth in the clear-cut forests of the Pacific Northwest (Franklin & Johnson, 2012) or the reclamation actions for Appalachian forest systems destroyed by mountain-top removal mining in eastern United States (Geredien, 2009). In order direct landscape planning, we need a better understanding of the specieshabitat relationship. For species like the northern spotted owl (Strix occidentalis caurina), Indiana bat (Myotis sodalis), and gopher tortoise (Gopherus polyphemus), a significant amount of effort has been made to establish these species’ habitat requirements and where those habitats are distributed in the landscape. In this case, deep knowledge about the ecology of species and the application of robust models work together in an explicit way that goes beyond considering only physical and biological characters. Rather, the spatial arrangement of habitat patches, corridors placements, habitat connectivity, resource availability, and risks to human disturbance are all incorporated into modeling the landscape these animals require to thrive and recover from their decline to extinction. Landscape management is not entirely intuitive and often requires a nonhuman viewpoint to even slightly understand its complexity. There is also another limit to managing the landscape according to a human perspective: without a knowledge of population dynamics spanning the life cycle of

24

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Fig. 1.8 Structuring the landscape consists not only in changing the scenery value but also in creating or vanishing structures with spatial and ecological attributes. In this case the activity was devoted to increase the spatial complexity of a clearing. (From the UK, Forestry Commission, 1991, with permission)

a species, we could create unintentional sink habitats (sensu Pulliam, 1988), accelerating a gradual decline in the vulnerable species and a decrease of the functional biodiversity. To improve the human-related approach, it is necessary, without a doubt, to obtain a strong interaction between geobotanical and animal perspectives

1.5 The Description of Landscape

25

Fig. 1.9 In Oregon, forested landscape changes according to private and public ownership type indicating how different silviculture paradigms affect forest fragmentation. Black areas represent conifer stands, while white areas indicate other types of woodland. (From Spies et al., 1994, with permission)

that can be incorporated into common practice. Such an approach could be extremely important to predict the survivorship chances of many species and entire ecosystems. Figure 1.9 shows a scenario of private versus public logging of a forest landscape in Oregon between 1972 and 1988 (Spies et al., 1994). Private logging has produced a dramatic decline of conifer forests due to clear cutting or a lack of selective cutting that has proven to preserve healthier ecosystems. The more progressive silviculture methods in public forests have reduced fragmentation while keeping the matrix. For instance, recently Messier et al. (2019) have discussed a novel approach toward the integration of functionality of species-traits into a functional complex network (Fig. 1.10).

26

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Fig. 1.10 Example of three spatial scales of possible silvicultural intervention: (i) stand, (ii) forest ownership, and (iii) forested landscape. (Messier et al., 2019, with permission)

1.5.3.2

Spacing: The Perception of the Landscape

Space may be considered as “The final frontier for ecological theory” (Kareiva, 1994). We refer to “spacing” as the actions of an organism’s response to its geographic perception of the neighboring environment (the landscape) where some spatial dimension is delineated from an organism’s behavior (plant, animal, or human). For example, many species of animals establish territories based on the availability and distribution of plant communities. This not only creates spacing between animal territories, but it is also dictated by the way plants are spaced (clustered or dispersed) based on soils, chemistry, geology, and hydrology (e.g., Pockman & Small, 2010). The spacing or spatial arrangement of objects in nature is a common perception of countless organisms, from individuals to populations, communities, and metacommunities. Organisms react to external stimuli created by their living and non-living environment from which they integrate such information internally. This internal integration activates a process that evaluates environmental information with an individual’s biological demand to optimize the procurement of resources and management of their energy budget, avoid predation, and maintain homeostasis. Spacing can be considered the ecological limiting factor of an organism’s life because the distribution of resources is not uniform and there is significant competition among individuals and between species. This concept is central in landscape ecology; see later the chapters devoted to heterogeneity, fragmentation, metapopulation, etc.

1.5 The Description of Landscape

1.5.3.3

27

Spatial Resolution

Grain size and extent are the two parameters used in a space evaluation. Grain is the minimum area at which an organism perceives and responds to the patch structure of a landscape (Kotliar & Wiens, 1990). Extent is the coarsest scale of spatial heterogeneity at which organisms react. An organism must consider the differences in resource availability at different spatial and temporal scales. The scales at which resource availability is considered vary marginally between individuals but vary significantly between species and among taxonomic hierarchies. Pearson (1991) found that field sparrows (Spizella pusilla) would feed closer to each other within a large spatial enclosure, but white-throated sparrows (Zonotrichia albicollis) spread out using the entire available space (Fig. 1.11). Spacing largely depends on resource availability. Plants react to resource availability by arranging in a finite and predictable pattern. The African acacia savanna,

Fig. 1.11 Field sparrows (Spizella pusilla) can forage close to each other, while white-throated Sparrows (Zonotrichia albicollis) maintain a high inter-individual distance during food search path, from an experiment of Pearson. (Modified from Pearson, 1991, with permission)

28

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

for example, has a restricted spatial arrangement affected by the availability of soil water for the root system. In Europe, this patterning is observed in the Spanish “Dehesa” (Olea & Migeul-Ayanz, 2006) and by the equivalent Portuguese “Montado,” a savannah-like agroforestry-pastoral ecosystem dominated by evergreen oaks, Quercus suber and Q. rotundifolia (Pinto-Correia et al., 2011). Environmental conditions can also affect the spacing of animals. For instance, in winter during the warmer days, bird ocks are relaxed, and individuals are dispersed in the environment but during cold days individuals coexist close to one another concentrated in areas where resources are abundant. Animals have a great sensibility to their environment especially regarding antipredator behavior, intraspecific competition, or homing. There are species, like wolverine (Gulo gulo), that are solitary foragers and show strong territorial behavior. European robin (Erithacus rubecula) patrols and defends a very distinct area, fighting against any other conspecific individual that doesn’t respect the ownership. The defended patch in many cases has an intrinsic quality determined not only by a mosaic of fields, edge-rows, and woodlots but also by the competition pressure, the abundance of other individuals, and the attractiveness of the area at a larger scale (Farina, 1993). Public information consists of the visual, acoustic, or chemical cues emitted by other individuals and species. The sensory organs serve as the receptors of public information that is neurologically processed for learning from the behavior of other intra- or interspecific individuals about resource location and availability. With this mechanism, the evaluation of habitat quality is assessed through a semiotic mechanism of interpretation of signals coming from other species independently by the structural composition of habitats. This forces us to reconsider the role of geobotanical configuration, land-use, and landscape patterns that drive the occupancy and use of habitat patches (see Smith et al., 2001). So, in conclusion, spacing is not determined only by external cues and habitat availability but also by physiological constraints and behaviors, and this produces a more complicated scenario to be considered.

1.5.3.4

Space and Memory

Learning and remembering spatial patterns has been demonstrated in different groups of animals like hummingbirds (Sutherland & Gass, 1995) and sheep (Dumont & Peptit, 1998; Dumont & Hill, 2001) living in a heterogeneous environment. The episodic memory refers to the ability of an organism to encode and recall unique, past experiences. Some birds like tits (Haftorn, 1956) and jays (Clayton & Dickinson, 1998) have the capacity to recover the food stored in holes or in other temporary refuges after some time. This capacity requires a cognitive map of the surroundings stored in a memory localized in the mammal and birds in hippocampus (Bingman & Able, 2002) that allows individuals to provide information about the “what” and “when” events (Griffiths et al., 1999; Griffiths & Clayton, 2001). The

1.5 The Description of Landscape

29

study of this memory could open new perspectives to better understand the “cognitive landscape” in nonhuman animals.

1.5.3.5

Cognitive Theories of Landscape Perception: Embodiment and Affordance

In this section, we will describe relevant elements linked to the perception of surroundings like space, embodiment, safety, aesthetic, amenity, scenery, etc. that represent a miscellanea of concepts that have in common cognitive processes. This cultural contamination is very useful to develop landscape ecology as a science applied to land management in areas in which human intrusion alters the resilient properties of the ecosystems and human-wildlife cognitive interaction (Goumas et al., 2020). Embodiment refers to the role of the body in cognitive processes. Embodiment requires structural coupling between the system and the environment (Riegler, 2002). There are two different senses of embodiment: the act of embodying and the state of being embodied. The act of embodying assumes that the body changes with time according to its functional states, while the state of being embodied represents the moment an animal’s body is in a specific functional state. Affordance refers to the opportunities for action that objects, events, and places provide an animal. The opposite is represented by the effectivities that are the acts an animal uses to realize a specific function. Effectivities change according to the status of an animal, because these are the properties of the animal. Tools, like a stick or a weapon, extend the effectivities of a tool-making animal. This capacity is significant for humans who use tools to make use of more affordances than other animals. Once a tool is made and put to use, the organism immediately increases its affordances, while the tool serves as the single most important mechanism for its effectivities, almost becoming an extension of the body itself (Fig. 1.12). The profound relationship between an organism and their environment involves a complementary interchange where an organism’s behavior is, in effect, a product of the environment it is exposed. Moose (Alces alces), for example, browse heavily on willow (Salix sp.) and position their spatial distribution near willow stands. The way moose browses willow stems stimulates the plants to grow additional stems, leaves, and owering bodies, thus expanding the plant’s distribution and providing more food for a larger moose population. From individual animals to entire communities, there remains an intimate connection between animals and the environmental where affordance and effectivities are part of the same that functions to maintain perceptual homeostasis (see Weems, 1999). The potential for understanding animal behavior with this concept is encouraging because it cuts across the traditional organismsenvironment dichotomy (Weems, 1999). This is extremely important in developing a robust theory of organismic-centered landscape. This last concept means that organisms react to changing patterns according to the appearance of a gradient that could be a variation in electromagnetic wave as well as a change in distribution of surrounding organisms.

30

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Fig. 1.12 Affordance means the interaction between organism and the surrounding. In case A, Storch (Ciconia ciconia) uses an urban lamp as support to nest. In case B, a Pond skater (Gerris sp.) uses the surficial water tension as terrestrial mammals uses the ground. Every object has a different affordance according to the organisms that enter into contact

1.5.3.6

Cultural Entity

Culture is defined as “the customary beliefs, social forms, and material traits of a racial, religious, or social group” but also “the integrated pattern of human knowledge, belief, and behavior that depends upon the capacity for learning and transmitting knowledge to succeeding generations” (Merriam-Webster Dictionary). A landscape can be considered according to this perspective, as a unique container of land forms shaped by traditional human activity, reservoir of believes, and values. Later the cultural aspect of the landscape will be discussed more in details.

1.5 The Description of Landscape

31

The landscape can be considered the place in which culture has been developed and dynamically adapted to environmental constraints. A landscape can be assigned functions and values according to the culture evolved in a localized area.

1.5.3.7

Contemplative and Aesthetic Entity

The landscape dimension is a definitive field of many different competences according to how certain perspectives are utilized. It is for this reason that landscapes can take on seemingly multidimensions based on the myriad of possibilities for analysis and assessment that create a variety of “realities” that in turn require tradeoff strategies to avoid con icting uses (Gómez-Sal et al., 2003). This perspective is difficult to be distinguished from the cultural concept of the landscape and could be considered a subdivision of the former. However, landscapes considered as scenery have long inspired the minds of poets, writers, and painters. The artistic expression of landscapes has attempted to represent the beauty of nature, its strength, and, in some cases, its “cruelty,” as a way to depict a more transcendental perspective that has a universal value for people. Famous works of literature, like J.R.R. Tolkien’s Lord of the Rings, have also used landscapes to describe imaginative worlds of fiction but with descriptions related closely with our (the reader’s) own relationship and experience with the land.

1.5.4

Behavior and Landscape

Behavioral ecology includes a number of study interests common to landscape ecology because of their spatial contexts, including animal movements, dispersal, and migration (Gagliardo et al., 2001). Unfortunately, few connections between behavioral ecology and landscape ecology are presently established because there is difficulty finding a common scale of interaction (Lima & Zollner, 1996; Bennet, 1996). This challenge may be understood and diffused by the concept of perceptual range. Perceptual range is defined as the distance from which a particular landscape element can be perceived and interpreted. This represents the “species-specific window” of the “greater landscape.” This concept goes back to our discussion of a species’ perception of a landscape where the landscape of a beetle is much different than that of an eagle (Wiens & Milne, 1989). This property of perception and reality plays a fundamental role in the survivorship of individuals. A species that has a low perceptual range often has lower ability to find and acquire resources because of its limited environment, higher rate of competition, and a high risk of mortality. In the case of habitat heterogeneity and low perceptual range, a species can also experience a high risk of predation. Compared with a species with a high perceptual range, the landscape of resources is greater and thus enhances survivorship.

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

32

There are many drivers and limiting factors, referred to as forcing functions that not only cause animals to behave in certain ways but also directs their use of their surroundings within a landscape. Recently, Suraci et al. (2019) have experimentally demonstrated the effect of human intrusion (voice playback) on the behavioral ecology of large, medium size, and small mammals. They found that large carnivores avoided sites where human voices were forecasted and moved with more circumspection when hearing humans (Fig. 1.13). Jetz et al. (2004) have demonstrated the importance of behavior, body size, and home range in mammals using a metabolic ecology approach. These results suggest that the spatial domain of an animal is important to maintaining its own homeostasis. The innate drive for survival forces animals to optimize their selection of resources

(d)

-1

0.12 0.08

Cautiouness Movement speed

80 0

0.00

40

0.04

120

Avoidance Distance to nearest speaker (m)

160

0.16

(a)

Control

Control

Human

Human

Control

Human

Human

1.5 1.1 0.8 0.4

(e) 0.0

Opossum foraging efficiency Food patch discovery rate (days )

3 2 1

Skunk activity level Detections per week Control

0

5.2 3.5 1.8 0.0

Bobcat diurnal activity Time of detections in hours from middle of night

7.0

7.0

(b)

Control

Human

Playback treatment

0.4

0.6

0.8 Human

0.2

Small mammal foraging intensity Proportion of food patches visited Control

0.0

0.20 0.14

0.07

0.00

Deer mouse space use Average area used (ha)

0.27

(c)

Control

Human

Playback treatment

Fig. 1.13 Human intrusion, in this case a net of fear of humans has landscape-scale impacts on wildlife across multiple trophic levels. (a) Fear of humans affects mountain movement behavior. (b) Fear of humans suppresses medium-sized carnivore behavior. (c) Suppression of larger predators induced by fear of humans benefits small mammals. (d–e) Conceptual illustrations of the landscapescale effects of fear of humans on wildlife communities. (Suraci et al., 2019, with permission)

1.5 The Description of Landscape

33

and habitats. Unfortunately, the landscape is not occupied in isolation and the behavior and spacing of other individuals and species create additional positive and negative selective pressures. Reed and Dobson (1993), for instance, have considered the role of conspecific attraction in habitat selection. They found that the presence of individuals in a patch can be an attractor for other conspecifics, despite the similarities between resources in empty patches. This has important implications in conservation strategies for the reintroduction of some endangered species. For instance, the critically endangered Mississippi Gopher Frog (Lithobates sevosa) is a communal species that has only been known to exist in colonies. Reintroduction attempts have not been successful when only a single individual survives (US Fish and Wildlife Service, 2015). The lack of conspecifics in a suitable patch can dramatically depress the survivorship of the reintroduced individuals. Conspecific attraction can therefore drive patch selection and movements of some animals. For many species, individuals tend to settle in a patch occupied by other conspecific (Smith & Peacock, 1990, for a review). Conversely, Pierce et al. (2000) have argued that mountain lions (Puma concolor) are spatially distributed according to prey availability and that territorial overlap is reduced by reciprocal avoidance of other individuals. Similar intraspecific individual avoidance has also been explained by Gross et al.’s (1995) nearest-neighbor rule. Many of these types of animals utilize corridors to pass through one habitat patch to another while avoiding conspecifics and human disturbance. Some species extensively use corridors that have recognizable structures like hedgerows, but in many cases corridors are perceived by animals through a species-specific simultaneous use of visual, acoustic, and smelling cues that are all integrated components to the landscape. The animal occupancy of a landscape can also vary based on phenology. Many species of birds concentrate in great numbers during the breeding season then disperse away from ocks to outside the breeding season to forage and roost. For instance, locust colonies in sub-Sahara Africa are widely dispersed until the right conditions arise, and dispersed colonies of a few thousand become plagues of millions that black out the sky and blanket the landscape (Topaz et al., 2012). By using spatial explicit models and the source-sink principle, we can assume that animals select the more suitable patches among the ones available in the landscape. Animal movements and patch selection are determined by many internal and external cues using spatial memory, cognitive maps, and conspecific attraction. A new unexplored opportunity to understand the perceiving capacity of animals is offered by the human-altered landscapes in which animals have to face new landscape configurations, in many cases, representing true novelties and different from the one in which they evolved. For instance, forest animals are now living in a more fragmented landscape, and their “maladaptation” (see Blondel et al., 1992) to the new patterns can be used as a tool to investigate the evolutive processes. The use of behavioral traits like biomarkers could introduce new perspectives in the investigation of the effects of landscape structure on species. It is the case of the uctuating asymmetry utilized to investigate the effects of minor development

34

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

accidents (van Valen, 1962). Fluctuating asymmetry refers to small, random deviations away from perfect symmetry of bilateral traits. High uctuating asymmetry is the result of environmental stressors. This indicator has been utilized by Lens et al. (2002) to evaluate the persistence of birds in fragmented rainforest in southeast Kenya. They used the uctuating asymmetry and mobility as indicators of the effects of fragmentation on selected species. In that case, uctuating asymmetry was measured in tarsus length. Small nondirectional differences in the development of the left and right side of bilateral traits are considered to be an indicator of developmental stability of a population into a specific habitat. Fluctuating asymmetry was found to be correlated to forest deterioration due to fragmentation. The comparison of uctuating asymmetry from museum samples of birds living at the time of continuous forest confirmed the hypothesis of the effect of habitat fragmentation and change in a secondary life trait (De Coster et al., 2013).

1.5.5

Semiotic Landscape

Semiotic landscape represents the mosaic built by the totality of signals that an individual can perceive and decode as meaningful or useless (f.i., Kasanga, 2015). It could be considered quite different from the cognitive landscape that represents the landscape created by a mental perception. For instance, the effect of linear perspective in painting is an example of visual construct of a 3D world from a 2D dimension realized by cognitive mechanisms, a painting technique introduced by the Florentine artist and architect Leon Battista Alberti in De Pictura book in renaissance time (Alberti, 2012). The semiotic landscape hypothesis can be used to explain the distribution pattern of foraging activity in birds as seen in tits by Naef-Daenzer (2000) that demonstrates the use of resources preceded by a general survey of the foraging area (Fig. 1.14). If time invested in the previous foraging act is higher than the last, tits will change the searching tree. In other words, the efficiency is evaluated in terms of time invested in searching for food. Individuals start to exploit the nearest tree around the nest then move further away according to the reduction of prey availability. In honeybees, the retinal image ow represents its perceived surroundings. Experiments carried out by Esch et al. (2001) clarify the mechanisms of surrounding perception in this species. The landscape grain, experimentally varied by using a black-white mosaic, seems to be the key cues used by bees to store information and distance from their surroundings. The fact that an ontogenetic learning mechanism is adopted by honeybees has also been observed using a harmonic radar. At first experience outside the hive, individuals perform short exploration ights, but a few days are enough to train the individual to be accustomed to the surroundings (Capaldi et al., 2000).

References

35

Fig. 1.14 Distribution of prey availability (a) and location density of radiotracked tits (b) around the next (#371). The circles represent the trees diameter and the color from gray to black, high prey density, and radiolocations, respectively. (From Naef-Daenzer, 2000, with permission)

References Agnoletti, M., Manganelli, S., & Piras, F. (2020). Covid-19 and rural landscape: The case of Italy. Landscape and Urban Planning, 204, 103955. Ahas, R., & Aasa, A. (2001). Impact of landscape features on spring phenological phases of maple and bird cherry in Estonia. Landscape Ecology, 16, 437–451. Alberti, L. B. (2012). De Pictura, a cura di L. Bertolini. Edizioni Polistampa. Allen, T. H. F., & Starr, T. B. (1982). Hierarchy, perspectives for ecological complexity. The University of Chicago Press. Antrop, M. (2001). The language of landscape ecologists and planners. A comparative content analysis of concepts used in landscape ecology. Landscape and Urban Planning, 55, 163–173. Ausonio. (2015). La Mosella e altre poesie. Fabbri Centauria.

36

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Backhaus, G., & Muringi, J. (2006). Ecoscapes. Geographical patternings of relations. Lexington Books. Barnett, D. T., & Stohlgren, T. J. (2001). Aspen persistence near the National Elk Refuge and Gros Ventre Valley elk feedgrounds of Wyoming, USA: Landscape. Ecology, 16, 569–580. Barrett, T. L., Farina, A., & Barrett, G. W. (2009). Aesthetic landscapes: An emergent component in sustaining societies. Landscape Ecology, 24, 1029–1035. Benbya, H., & McKelvey, B. (2006). Toward a complexity theory of information systems development. Information Technology & People, 19(1), 12–34. Bennett, A. T. D. (1996). Do animals have cognitive maps? The Journal of Experimental Biology, 199, 219–224. Bingman, V. P., & Able, K. P. (2002). Maps in birds: Representational mechanisms and neural bases. Current Opinion in Neurobiology, 12, 745–750. Bissonette, J. A. (Ed.). (1997). Wildlife and landscape ecology (pp. 32–69). Springer. Blondel, J., Perret, P., Maistre, M., & Dias, P. C. (1992). Do arlequin Mediterranean environments function as source-sink for Blue Tits (Parus caeruleus L.)? Landscape Ecology, 6, 212–219. Bossomaier, T. R. J., & Green, D. G. (Eds.). (2000). Complex systems. Cambridge University Press. Buol, S., Hole, F. D., & McCracken, R. J. (1989). Soil genesis and classification (3rd ed.). Iowa State University Press. Canters, K. J., den Herder, C. P., de Veer, A. A., Veelenturf, P. W. M., & de Waal, R. W. (1991). Landscape-ecological mapping of the Netherlands. Landscape Ecology, 5, 145–162. Capaldi, E. A., Smith, A. D., Osborne, J., Fahrbach, S. E., Farris, S. M., Reynolds, D. R., Edwards, A. S., Martin, A., Robinson, G. E., Poppy, G. M., & Riley, J. R. (2000). Ontogeny of orientation ight in the honeybee revealed by harmonic radar. Nature, 403, 537–540. Chamovitz, D. (2020). What a plant knows: A field guide to the senses: Updated and expanded edition. Scientific American/Farrar, Straus and Giroux. Chapin, T. G., Harrison, D. J., & Katnik, D. D. (1998). In uence of landscape pattern on habitat use by American marten in an industrial forest. Conservation Biology, 12(6), 1327–1337. Cilliers, P. (1998). Complexity & postmodernism. Routledge. Clayton, N. S., & Dickinson, A. (1998). Episodic-like memory during cache recovery by Schub Jays. Nature, 395(6699), 272–274. Collinge, S. K., & Palmer, T. M. (2002). The in uence of patch shape and boundary contrast on insect response to fragmentation in California grasslands. Landscape Ecology, 17, 647–656. Council of Europe. (2000). The European Landscape Convention (STE no. 176). Crutzen, P., & Stoermer, E. (2000). The Anthropocene, global change. IGBP Newsletter, 41, 17–18. De Coster, G., Van Dongen, S., Malaki, P., Muchane, M., Alcántara-Exposito, A., Matheve, H., & Lens, L. (2013). Fluctuating asymmetry and environmental stress: Understanding the role of trait history. PLoS One, 8(3), e57966. Dumont, B., & Hill, D. R. C. (2001). Multi-agent simulation of group foraging in sheep: Effects of spatial memory, on specific attraction and plot size. Ecological Modelling, 141, 201–215. Dumont, B., & Petit, M. (1998). Spatial memory of sheep at pasture. Applied Animal Behaviour Science, 60, 43–53. Eisler, A. D., Eisler, H., & Yoshida, M. (2003). Perception of human ecology: cross-cultural and gender comparisons. Journal of Environmental Psychology, 23(1), 89–101. Esch, H. E., Zhang, S., Srinivaasan, M. V., & Tautz, J. (2001). Honeybee dances communicate distance measured by optic ow. Nature, 411, 581–583. Estes, L., Elsen, P. R., Treuer, T., Ahmed, L., Caylor, K., Chang, J., . . . Ellis, E. C. (2018). The spatial and temporal domains of modern ecology. Nature Ecology & Evolution, 2(5), 819–826. Farina, A. (1993). L’ecologia dei sistemi ambientali. CLEUP. Farina, A. (1998). Principles and methods in landscape ecology. Chapman & Hall. Farina, A. (1999). Perspectives in ecology. In A glance from the VII international congress of ecology. Backhuys Publishers. Farina, A. (2000). Landscape ecology in action. Kluwer Academic Publishers. Farina, A. (2004). Verso una scienza del paesaggio. Perdisa Editore.

References

37

Farina, A. (2006). Principles and methods in landscape ecology: Towards a science of the landscape (Vol. 3). Springer Science & Business Media. Farina, A. (2014a). A biosemiotics perspective of the resource criterion: Toward a general theopry of resources. Biosemiotics, 5, 17–32. Farina, A. (2014b). Soundscape ecology. Principles, patterns, methods and applications. Springer. Farina, A. (2021). Semiotic landscape ecology: A novel approach to reconcile environment and humanities (Elements in Environmental Humanities). Cambridge University Press. Farina, A., Bogaert, J., & Schipani, I. (2005). Cognitive landscape and information: new perspectives to investigate the ecological complexity. BioSystems, 79(1-3), 235–240. Fisher, D. N., & Pruitt, J. N. (2020). Insights from the study of complex systems for the ecology and evolution of animal populations. Current Zoology, 66(1), 1–14. Forestry Commission. (1991). Community woodland design. HMSO Publication Centre. Forman, R. T. T. (1990). The beginnings of landscape ecology in America. In I. S. Zonneveld & R. T. T. Forman (Eds.), Changing landscapes: An ecological perspective (pp. 35–41). Springer. Forman, R. T. T. (1995). Land mosaics. The ecology of landscapes and regions. Cambridge Academic Press. Forman, R. T. T., & Godron, M. (1986). Landscape ecology. Wiley. Franklin, J. F., & Johnson, K. N. (2012). A restoration framework for federal forests in the Pacific Northwest. Journal of Forestry, 110(8), 429–439. Gage, S. H. (2017). Ecoacoustics challenges. In A. Farina & S. T. Gage (Eds.), Ecoacoustics: The ecological role of sounds (pp. 313–320). Wiley. Gagliano, M., Renton, M., Depczynski, M., & Mancuso, S. (2014). Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia, 175(1), 63–72. Gagliardo, A., Odetti, F., & Ioalé, P. (2001). Relevance of visual cues for orientation at familiar sites by homing pigeons: An experiment in a circular arena. Proceedings of the Royal Society of London, 268, 2065–2070. Geredien, R. (2009). Assessing the extent of mountaintop removal in Appalachia. Technical Report for Appalachian Voices, Boone, NC. Getzner, M. (2020). Visitors’ preferences for landscape conservation in Alpine environments: Differences across regions, conservation programmes, and socio-economic groups. Landscape Research, 45(4), 503–519. Gilpin, M., & Hanski, I. (Eds.). (1991). Metapopulation dynamics: Empirical and theoretical investigations. Academic Press. Gómez-Sal, A., Belmontes, J. A., & Nicolau, J. M. (2003). Assessing landscape values: a proposal for a multidimensional conceptual model. Ecological modelling, 168(3), 319–341. Gosz, J. R. (1993). Ecotone hierarchies. Ecological Applications, 3, 369–376. Goumas, M., Lee, V. E., Boogert, N. J., Kelley, L. A., & Thornton, A. (2020). The role of animal cognition in human-wildlife interactions. Frontiers in Psychology, 11, 3019. Green, B. H., Simmons, E. A., & Woltjer, I. (1996). Landscape conservation. Some steps towards developing a new conservation dimension. A draft report of the IUCN-CESP landscape Conservation Working Group. Department of Agriculture, Horticulture and Environment, Wye College, Ashford, Kent, UK. Griffiths, D. P., & Clayton, N. S. (2001). Testing episodic memory in animals: A new approach. Physiology & Behavior, 73, 755–762. Griffiths, D. P., Dickinson, A., & Clayton, N. (1999). Episodic memory: What can animals remember about their past. Trends in Cognitive Sciences, 3(2), 74–80. Gross, J. E., Zank, C., Hobb, N. T., & Spalinger, D. E. (1995). Movement cues for herbivores in spatially heterogeneous environments: Responses to small scale pattern. Landscape Ecology, 10, 209–217. Grueneisen, S., Duguid, S., Saur, H., & Tomasello, M. (2017). Children, chimpanzees, and bonobos adjust the visibility of their actions for cooperators and competitors. Scientific Reports, 7(1), 1–10.

38

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Gutzwiller, K. J., & Anderson, S. H. (1992). Interception of moving organisms: in uences of patch shape, size, and orientation on community structure. Landscape Ecology, 6(4), 293–303. Haber, W. (2004). Landscape ecology as a bridge from ecosystems to human ecology. Ecological Research, 19, 99–106. Haftorn, S. (1956). Contribution to the food biology of tits especially about storing of surplus food. In Part IV: A comparative analysis of Parus atricapillus L: P. cristatus and P. ater L. Det Kongelige Norske Videnskaters Selskabs Skrifter 4 (pp. 1–54). Hansen, A. J., & Di Castri, F. (1992). Landscape boundaries. Springer. Hanski, I. (1999). Metapopulation ecology. Oxford University Press. Hanski, I., & Gilpin, M. (1991). Metapopulation dynamics: Brief history and conceptual domain. Biological Journal of the Linnean Society, 42, 3–16. Hanski, I., & Gilpin, M. (Eds.). (1997). Metapopulation biology. Ecology, genetics, and evolution. Academic Press. Heinrich, B., & Bugnyar, T. (2005). Testing problem solving in ravens: String-pulling to reach food. Ethology, 111(10), 962–976. Hernandez-Stefanoni, J. L. (2005). Relationships between landscape patterns and species richness of trees, shrubs and vines in a tropical forest. Plant Ecology, 179(1), 53–65. Hoffmeyer, J. (1996). Signs of meaning in the universe. Indiana University Press. Hoffmeyer, J. (1997). Biosemiotics: Toward a new synthesis in biology. European Journal for Semiotic Studies, 9, 355–376. Huggett, R. J. (1995). Geoecology. An evolutionary approach. Routledge. IALE. (2008). www.Landscape-ecology.org. Accessed 28 Mar 2020. Jetz, W., Carbone, C., Fulford, J., & Brown, J. H. (2004). The scaling of animal space use. Science, 306, 266–268. Johnson, G. D., Tempelman, A., & Patil, G. P. (1995). Fractal based methods in ecology: A review for analysis at multiple spatial scales. Coenoses, 10, 123–131. Kareiva, P. (1994). Space: The final frontier for ecological theory. Ecology, 95, 1. Kasanga, L. A. (2015). Semiotic landscape, code choice and exclusion. In Conflict, exclusion and dissent in the linguistic landscape (pp. 123–144). Palgrave Macmillan. Kauffman, S. (1993). The origin of order. Oxford University Press. Kenkel, N. C., & Walker, D. J. (1993). Fractals and ecology. Abstracta Botanica, 17, 53–70. Klink, H. J., Potschin, M., Tress, B., Tress, G., Volk, M., & Steinhardt, U. (2002). Landscape and landscape ecology. In O. Bastian & U. Steinhardt (Eds.), Development and perspective of landscape ecology (pp. 1–47). Kluwer Academic Publishers. Kolasa, J., & Pickett, S. T. A. (1991). Ecological heterogeneity. Springer. Kotliar, N. B., & Wiens, J. A. (1990). Multiple scales of patchiness and patch structure: A hierarchical framework for the study of heterogeneity. Oikos, 59, 253–260. Kull, K. (1998a). Semiotic ecology: Different natures in the semiosphere. Sign Systems Studies, 26, 344–371. Kull, K. (1998b). On semiosis, umwelt, and semiosphere. Semiotica, 120(3/4), 299–310. Langlois, J. P., Fahrig, L., Merriam, G., & Artsob, H. (2001). Landscape structure in uences continental distribution of hantavirus in deer mice. Landscape Ecology, 16, 255–266. Laundré, J. W., Hernández, L., & Altendorf, K. B. (2001). Wolves, elk, and bison: Reestablishing the “landscape of fear” in Yellowstone National Park, USA. Canadian Journal of Zoology, 79(8), 1401–1409. Laundré, J. W., Hernández, L., & Ripple, W. J. (2010). The landscape of fear: Ecological implications of being afraid. The Open Ecology Journal, 3(1), 1. Lens, L., Van Dongen, S., Norris, K., Githiru, M., & Matthysen, E. (2002). Avian persistence in fragmented rainforest. Science, 298, 1236–1238. Lewin, R. (1999). Complexity: Life at the edge of chaos (2nd ed.). Chicago University Press. Li, B.-L. (2000). Why is the holistic approach becoming so important in landscape ecology? Landscape and Urban Planning, 50, 27–41.

References

39

Lima, S. L., & Zollner, P. A. (1996). Towards a behavioral ecology of ecological landscapes. TREE, 11, 131–135. Lindstrom, K., Kull, K., & Palang, H. (2011). Semiotic study of landscapes: An overview from semiology to ecosemiotics. Sign System Studies, 39(2), 12–36. MacArthur, R. H. (1972). Geographical ecology, patterns in the distribution of species. Princeton University Press. MacArthur, R. H., & Wilson, E. O. (1967). The theory of island biogeography. Princeton University Press. Malanson, G. P. (1993). Riparian landscapes. Cambridge University Press. Mandelbrot, B. B. (1975). Les objects fractals: Forme, hasard et dimension. Flammarion. Manson, S. M. (2001). Simplifying complexity: A review of complexity theory. Geoforum, 32, 405–414. Maran, T. (2020). Ecosemiotics: The study of signs in changing ecologies (Elements in environmental humanities). Cambridge University Press. Maturana, H. R., & Varela, J. F. (1980). Autopoiesis and cognition. The realization of the living. Rediel Publishing Company. Meeus, J. H. A. (1995). Pan-European landscapes. Landscape and Urban Planning, 31, 57–79. Merriam, G. (1984). Connectivity: A fundamental ecological characteristic of landscape pattern. In J. Brandt & P. Agger (Eds.), Proceedings of the first international seminar on methodology in landscape ecological research and planning (pp. 5–15). International Association for Landscape Ecology, Roskilde University Centre. Merry, U. (1995). Coping with uncertainty: Insights from the new sciences of chaos, selforganization, and complexity. Praeger Publishers. Messier, C., Bauhus, J., Doyon, F., Maure, F., Sousa-Silva, R., Nolet, P., . . . Puettmann, K. (2019). The functional complex network approach to foster forest resilience to global changes. Forest Ecosystems, 6(1), 1–16. Milne, B. T. (1991). Lessons from applying fractals models to landscape patterns. In M. G. Turner & R. H. Gardner (Eds.), Quantitative methods in landscape ecology (pp. 199–235). Springer. Milne, B. T. (1992). Spatial aggregation and neutral models in fractal landscapes. American Naturalist, 139, 32–57. Milne, B. T. (1995). The application of fractal geometry in wildlife biology. In J. A. Bissonette (Ed.), Wildlife and landscape ecology (pp. 32–69). Springer. Milne, B. T., Johnson, A. R., & Matyk, S. (2000). Clara T: Instructional software for fractal pattern generation and analysis. In J. M. Klopatek & R. H. Gardner (Eds.), Landscape ecological analysis. Issues and applications (pp. 304–332). Springer. Mooser, A., Anfuso, G., Mestanza, C., & Williams, A. T. (2018). Management implications for the most attractive scenic sites along the Andalusia Coast (SW Spain). Sustainability, 10(5), 1328. Moss, M. R. (2000). Interdisciplinarity, landscape ecology and the “transformation of agricultural landscapes”. Landscape Ecology, 15, 303–311. Naef-Daenzer, B. (2000). Patch time allocation and patch sampling by foraging great and blue tits. Animal Behavior, 59, 989–999. Naiman, R. J., & Decamps, H. (Eds.). (1990). The ecology and management of aquatic-terrestrial ecotones. MAB, UNESCO. Nassauer, J. I. (1995). Culture and changing landscape structure. Landscape Ecology, 10, 229–237. Naveh, Z. (1987). Biocybernetic and thermodynamic perspectives of landscape functions and land use patterns. Landscape Ecology, 1, 75–83. Naveh, Z. (1994). From biodiversity to ecodiversity: A landscape-ecology approach to conservation and restoration. Restoration Ecology, 2, 180–189. Naveh, Z., & Lieberman, A. S. (1984). Landscape ecology. Theory and application. Springer. Naveh, Z., & Lieberman, A. S. (1994). Landscape ecology. Theory and application (2nd ed.). Springer. Németh, Z., & Moore, F. R. (2007). Unfamiliar stopover sites and the value of social information during migration. Journal of Ornithology, 148(2), 369–376.

40

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Nöth, W. (2005). Semiotics for biologists. Journal of. Biosemiotics, 1, 183–198. O’Neill, R. V. (2001). Is it time to bury the ecosystem concepts? (With full military honors, of course!). Ecology, 82(12), 3275–3284. O’Neill, R. V., DeAngelis, D. L., Waide, J. B., & Allen, T. F. H. (1986). A hierarchical concept of ecosystems. Princeton University Press. O’Connor, A. M., Burton, T. J., Leamey, C. A., & Sawatari, A. (2014). The use of the puzzle box as a means of assessing the efficacy of environmental enrichment. JoVE (Journal of Visualized Experiments), 94, e52225. Oehri, J., Schmid, B., Schaepman-Strub, G., & Niklaus, P. A. (2020). Terrestrial land-cover type richness is positively linked to landscape-level functioning. Nature Communications, 11(1), 1–10. Ohta, H. (2001). A phenomenological approach to natural landscape cognition. Journal of Environmental Psychology, 21, 387–403. Olea, L., & San Miguel-Ayanz, A. (2006, April). The Spanish dehesa. A traditional Mediterranean silvopastoral system linking production and nature conservation. In 21st General Meeting of the European Grassland Federation, Badajoz (Spain). Ostfeld, R. S. (1992). Small-mammal herbivores in a patchy environment: Individual strategies and population responses. In Effects of resource distribution on animal-plant interaction. Academic Press. Pearson, S. M. (1991). Food patches and the spacing of individual foragers. Auk, 108, 355–362. Perfecto, I., Jiménez-Soto, M. E., & Vandermeer, J. (2019). Coffee landscapes shaping the Anthropocene: Forced simplification on a complex agroecological landscape. Current Anthropology, 60(S20), S236–S250. Pickett, S. T. A., & White, P. S. (1985). The ecology of natural disturbance and patch dynamics. Academic. Pierce, B. M., Bleich, V. C., & Bowyer, R. T. (2000). Social organization of mountain lions: Does a land-tenure system regulate population size? Ecology, 2000, 1533–1543. Pinto-Correia, T., Ribeiro, N., & Sá-Sousa, P. (2011). Introducing the montado, the cork and holm oak agroforestry system of Southern Portugal. Agroforestry Systems, 82(2), 99. Pockman, W. T., & Small, E. E. (2010). The in uence of spatial patterns of soil moisture on the grass and shrub responses to a summer rainstorm in a Chihuahuan desert ecotone. Ecosystems, 13(4), 511–525. Pulliam, R. (1988). Sources-sinks, and population regulation. American Naturalist, 132, 652–661. Pulliam, R. (1996). Sources and sinks: Empirical evidence and population consequences. Trends in Ecology & Evolution, 11(8), 326–330. Reed, J. M., & Dobson, A. P. (1993). Behavioral constraints and conservation biology: Conspecific attraction and recruitment. TREE, 8(7), 253–256. Riegler, A. (2002). When is a cognitive system embodied? Cognitive Systems Research, 3, 339–348. Risser, P. G. (1995). The status of the science examining ecotones. Bioscience, 45(5), 318–325. Risser, P. G., Karr, J. R., & Forman, R. T. T. (1984). Landscape ecology. Directions and approaches. Illinois Natural History Survey Special Publication number 2. Schmid, W. A. (2001). The emerging role of visual resource assessment and visualisation in landscape planning in Switzerland. Landscape and Urban Planning, 54, 213–221. Schreiber, K.-F. (1990). The history of landscape ecology in Europe. In I. S. Zonneveld & R. T. T. Forman (Eds.), Changing landscapes: An ecological perspective (pp. 21–33). Springer. Sereni, E. W. (1961). Storia del paesaggio agrario italiano. Editori Laterza. Silbernagel, J. (2003). Spatial theory in early conservation design: Examples from Aldo leopold’s work. Landscape Ecology, 18, 635–646. Smith, A. T., & Peacock, M. M. (1990). Conspecific attraction and the determination of metapopulation colonization rate. Conservation Biology, 4, 320–323. Smith, R. D., Ruxton, G. D., & Cresswell, W. (2001). Patch choice decisions of wild blackbirds: The role of preharvest public information. Animal Behavior, 61, 1113–1124.

References

41

Spies, T., Ripple, W. J., & Bradshaw, G. A. (1994). Dynamics and pattern of a managed coniferous forest landscape in Oregon. Ecological Applications, 4, 555–568. Stauffer, D. (1985). Introduction of percolation theory. Taylor & Francis. Stonier, T. (1990). Information and the internal structure of the universe. An exploration into information physics. Springer. Stonier, T. (1996). Information as a basic property of the universe. Biosystems, 38, 135–140. Sueur, J., & Farina, A. (2015). Ecoacoustics: The ecological investigation and interpretation of environmental sound. Biosemiotics, 8, 493–502. https://doi.org/10.1007/s12304-015-9248-x Suraci, J. P., Clinchy, M., Zanette, L. Y., & Wilmers, C. C. (2019). Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecology Letters, 22(10), 1578–1586. Sutherland, G., & Gass, C. L. (1995). Learning and remembering of spatial patterns by hummingbirds. Animal Behavior, 995, 1273–1286. Tansley, A. G. (1935). The use and abuse of vegetation concepts and terms. Ecology, 16(3), 284–307. Tansley, A. G. (1939). The British Isles and their vegetation (rep. 1949 in 2 vols). CUP. Thompson, J. N., Reichman, O. J., Morin, P. J., Polis, G. A., Power, M. E., Sterner, R. W., Couch, C. A., Cough, L., Holt, R., Hooper, D. U., Keesing, F., Lovell, C. R., Milne, B. T., Molles, M. C., Roberts, D. W., & Strauss, S. Y. (2001). Frontiers of ecology. Bioscience, 51, 15–24. Topaz, C. M., D’Orsogna, M. R., Edelstein-Keshet, L., & Bernoff, A. J. (2012). Locust dynamics: Behavioral phase change and swarming. PLoS Computational Biology. https://doi.org/10.1371/ journal.pcbi.1002642 Tress, B., & Tress, G. (2001). Capitalising on multiplicity: A transdisciplinary systems approach to landscape research. Landscape and Urban Planning, 57, 143–157. Turner, M. G. (1989). Landscape ecology: The effect of pattern on process. Annual Review of Ecology and Systematics, 20, 171–197. Turner, M. G., Gardner, R. H., & O’Neill, R. V. (1995). Ecological dynamics at broad scales. Bioscience S–29. Turner, M. G., Gardner, R. H., & O’Neill, R. V. (2001). Landscape ecology in theory and practice. Pattern and process. Springer. U.S. Fish and Wildlife Service. (2015). Dusky gopher frog (Rana sevosa) recovery plan (p. 86). Atlanta. v. Humboldt, A. (1849). Ansichten der Natur. 3rd revised and augmented edition, 2 volumes, J.G. Cotta’scher Verlag, Stuttgart und Tübingen. Stuttgart/Tübingen: J.G. Cotta’scher Verlag. van Valen, L. (1962). A study of uctuating asymmetry. Evolution, 16(2), 125–141. Vasudev, D., Fletcher, R. J., Jr., Goswami, V. R., & Krishnadas, M. (2015). From dispersal constraints to landscape connectivity: Lessons from species distribution modeling. Ecography, 38(10), 967–978. von Bertalanffy, L. (1969). General system theory. Braziller. von Uexkull, J. 1940 (1982). The theory of meaning. Semiotica, 42(1), 25–82. Vos, W., & Stortelder, A. (1992). Vanishing Tuscan landscapes. Pudoc Scientific Publishers. Weems, C. F. (1999). Conceptual and methodological issues in examining behavioral development through environment-organismic interactions. New Ideas in Psychology, 17, 149–164. Weng, G., Bhalla, U. S., & Iyengar, R. (1999). Complexity in biological signaling systems. Science, 284, 92–96. Whittaker, R. H. (1977). Evolution of species diversity in land communities. Evolutionary Biology, 10, 1–67. Wiens, J. A. (1989). The ecology of bird communities. Cambridge University Press. Wiens, J. A. (1992a). What is landscape ecology, really? Landscape Ecology, 7(3), 149–150. Wiens, J. A. (1992b). Ecology 2000: An essay on future directions in ecology. Ecological Society of America Bulletin, 73(3), 165–170. Wiens, J. A., & Milne, B. T. (1989). “Scaling of landscapes” in landscape ecology, or landscape ecology from a beetle’s perspective. Landscape Ecology, 3, 87–96.

42

1 Principles and Methods in Landscape Ecology: An Agenda for the. . .

Wiens, J. A., Stenseth, N. C., Van Horne, B., & Ims, R. A. (1993). Ecological mechanisms and landscape ecology. Oikos, 66, 369–380. Woodmansee, R. G. (1990). Biogeochemical cycles and ecological hierarchies. In I. S. Zonneveld & R. T. T. Forman (Eds.), Changing landscapes: An ecological perspective (pp. 57–71). Springer. Wu, J., & Hobbs, R. (2002). Key issues and research priorities in landscape ecology: An idiosyncratic synthesis. Landscape Ecology, 7, 355–365. Ziff, R. (1986). Test of scaling exponents for percolation-cluster perimeters. Physical Review Letters, 56, 545–548. Zonneveld, I. S. (1995). Landscape ecology. SPB Academic Publishing.

Chapter 2

Ecoscape vs. Landscape: Riding a Transition

Synthesis Environmental fundamentals like time, senses, and signs originate as many ecoscapes (timing-scapes, sensory-scapes, semio-scapes) species specific. Timingscape is represented by a mosaic of patches shaped by phenomena that occur at a geological, biological, ecological, cultural, and semiotic time. The sensory-scapes pertain to domains that are the result of visual, aesthetic, psychological, thermal, odor, touch, and sound sensing. Semio-scape is the universe of signs that are the result of an interpreted land mosaic by individual organisms. For every organism, the contemporary use of different senses to maintain the relationship between internal and an external world has been considered a “vivoscape” (vivo is from Latin: living) considered a special case of ecoscape. The vivoscape paradigm conjugates the ecological with the semiotical processes and represents a new frontier in landscape ecology.

2.1

Introducing the Ecoscape

In Chap. 1, we have described the landscape as a mosaic of physical objects, energy gradients, semiotic agencies, and mental representations. In this section, we will discuss a novel conceptualization of the landscape by introducing to the “ecoscape” as a way to reimagine and properly redefine the term landscape, defined by Backhaus and Murungi (2006) as “the spatial configuration/pattern of events comprising an ecosystem.” For our part, we are aware of the difficulties involved in the semantic transition from landscape (everything included) to the ecoscape. Nevertheless, our attempt is to deviate from using landscape as a catch-all term and, alternatively, construct a framework of terminology and definitions that more appropriately represents the current field of landscape ecology with a clear theoretic direction for the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Farina, Principles and Methods in Landscape Ecology, Landscape Series 31, https://doi.org/10.1007/978-3-030-96611-9_2

43

44

2 Ecoscape vs. Landscape: Riding a Transition

An ecoscape is a phenomenological domain in which environmental fundamentals (like time, energy, ecological succession, human disturbance) are in actions, a conceptualization that is not frequently used in the ecological literature. When it is, it is often utilized with different meanings. For instance, Lidicker Jr (2008) proposed the use of ecosphere as a substitute for ecosystem (Odum, 1971), serving as the fourth dimension in ecology after organism, population, and communities. However, Lidicker was well aware that the word ecosphere could be an interchangeable term with landscape because of how the term “landscape” was currently being used in the literature. Recently, Farina and James (2021) have considered the emergent dimension created by the contemporary use of organisms of different levels of sensorialities and associated elaborations necessary to maintain the relationship between individual organisms and an external world with which enter in contact, as a special case of ecoscape, and called this phenomenological dimension “vivoscape” (vivo is from Latin: living). Throughout this chapter and the rest of this book, we will substitute the term landscape (under its current definition) with vivoscape or with ecoscape where the term is appropriate. We will no longer use the term landscape to refer to everything (seen and unseen), like we have described in the previous chapter. Instead, we will elaborate on the more accurate use of ecoscape/vivoscape as an all-inclusive term and landscape as a derivative.

2.2

Subjectivity and Objectivity

The perspective of the organism is at the center of the narrative of this chapter. Every organism uses perception and cognitive elaboration as a “subjective vision” of their surroundings. According to the different senses that an organism utilizes to collect information from the external world, the result is a “vision” of what their world is. We use the word vision figuratively to fit the linguistics definition of “scapes.” Practically, it is not possible to describe the “real world” outside the organismic perception. It is a frequent mistake to think that we can describe the world without an organism-centered frame of reference. Without it, we are stuck in a perennial contradiction to describe the world solely from the vision of the human organism. In other words, it’s really difficult if not impossible to describe the world of other species. It is irrational and illogical for us to believe that the “real” world we perceive is the same as that of all other taxa. Because of this common mistake of anthropomorphizing animal taxa and fitting an organism’s reality into our own worldview, it is required that we focus the separation between subjectivity and objectivity. Unfortunately, this is only a matter of approach and not a substantial distinction. In fact, subjectivity requires objectivity and vice versa (Cataldo et al., 2014; Ratner, 2002). It is more accurate to say that the world that we know and are describing is the result of our surroundings.

2.3 An Epistemological Classification of the Umwelt

2.3

45

An Epistemological Classification of the Umwelt

The surroundings perceived by individual species have been called Umwelt by von Uexküll (1982; 1926, 1934, 1940) (Fig. 2.1). The Umwelt is a complex system that cannot be approached in a synthetic or unitarian way. To even attempt to solve this very complicated puzzle and untangle its skein of relationships, we must proceed in a stepwise manner according to different epistemic elements considered from an ecosemiotic perspective. Biological, and ecological processes would not happen in the way we actually observe without the transformation of Fig. 2.1 Jakob Joahann von Uexküll (1864–1945), Estonian biologist, zoologist, and philosopher

46

2 Ecoscape vs. Landscape: Riding a Transition

environmental information into a meaning. Based on this, it seems reasonable to explain the concept of semiosis in the context of the vivoscape. Semiosis is the mental or symbolic processes from which a signal from the environment generates a meaning to a recipient organism. The vivoscape is, therefore, a repository of signals from which an organism’s action (effectivities) takes place. Ecosemiotics considers that the mechanisms of communication from semiotic processes are what drive the contacts between individuals and species and their abiotic and biotic environment. We can conceptualize the vivoscape as the embodiment of all physical and nonphysical information across space where the organisms that exist there receive, process, and react to such information and to one another, subsequently creating the complex emergent relationships we observe as ecosystems. Although it is impossible to understand this ecosemiotic complexity in its entirety, there are some very evident characteristics that can be categorized and explained. This section has been organized according to a hierarchy of perspectives based on the concepts of the ecoscape. All categories will be described in this chapter as separate visions conceived by applying a certain magnifying epistemic lens. Through this approach, we will see that the common and current use of the term “landscape” appears to, in fact, take different forms like a kaleidoscope, giving way to the more accurate, multifaceted, and holistic concept of the ecoscape. The different categories of “-scapes” we describe will be done so with precise classification, explicit definitions, and examples, where appropriate. However logical the hierarchical order each “-scape” is ranked, it still remains somewhat arbitrary. As such, we have adopted to describe the different realities embodied by the ecoscape in the descending hierarchical categories of the agentscape, observerscape, timingscape, semioscape, sensoryscape, resourcescape, and isoscape (Fig. 2.2). These selected categories of the ecoscape are based on the compilation of an extensive body of available research. The temptation to approach the Umwelt with separate disciplines is reconsidered, and the born of new concept formalized in new terms and definitions must not be considered like heresy. In fact, to understand the complexity with which the Earth landscape is wrapped is not sufficient to analyze the system at a multiple scale in time and space, but we have to make an additional effort observing the landscape according to patterns and processes present and interpreted by organisms. This semiotic framework that should be accepted by the sciences and not refused as often happens, fills the gap between different disciplines that do not have the privilege to depict the reality but only to describe by a new perspective a separate part of hidden trueness and complexity.

2.4

Timingscape: The Temporal Patches

Time is a variable that has a major impact on the functioning of an ecoscape. We can imagine landscape mosaics as a product of intrinsic timing. In fact, even the oldest of landscapes undergo change over periods of time. These can occur over short periods

2.4 Timingscape: The Temporal Patches

47

Timingscape

Semioscape Sensoryscape Visualscape

Odorscape

Soundscape

Tactilscape

Thermalscape

Resourcescape Goods

Services

Energy & Miningscape

Logisticscape

Farmingscape

Urbanscape

Forestryscape

Wildscape

Freshwaterscape

Therapeuticscape

Marinescape

Culturalscape Hybridscape

Isoscape

Fig. 2.2 The complexity of an ecoscape can be described according to a broad range of concepts and perceptive approaches. Definitively, a number of “scapes” are the functional structural and functional components of land/water-scapes

of time in the case of fires to thousands of years like succession events following glacial retreat. Time, as it exists, is not a tangible component but becomes a significant component to the evolution of landscapes that can be measured as an objective phenomenon. The landscape, as you see it today, can be viewed as the current state of the system resulting from a culmination of ecological and evolutionary change that has occurred over time. If you had the ability to stare at a landscape long enough, say, 100 years, you would observe the timing-scape. This temporal perspective of the landscape takes into account the history of the system and is necessary for scientific investigation. By doing so, the current features and characteristics present in the landscape can be accepted as indicators of how the landscape has developed its observable patterns and processes over the course of human and ecological history. Time remains one of the most discussed and controversial argument in philosophy and in sciences (Gunther & Morgado, 2004; McTaggart & McTaggart, 1927). The relationship between time and landscape is so intriguing that is not easy to synthesize. The following narrative intends to describe different categories of time and represents more a strategy for considering time than a real component to the epistemic framework.

48

2.4.1

2 Ecoscape vs. Landscape: Riding a Transition

Physical Time

After Albert Einstein introduced the theory of relativity in 1915, time became much more relevant than the invariant entity conceived by Newton in “Tempus absolutum, verum et mathematicum.” Time, in the physical sciences, is visualized as an arrow with a dimension of time (T) measured by an atomic clock (Fig. 2.3). The standard time that we use to synchronize our watches and clocks is based on the oscillation of a stronzium atom. One standard time second is based on 1015 oscillations of stronzium electrons that have been super cooled to –273
C. The precision and accuracy of this atomic clock has an uncertainty of 3.5 s every 1019 s. That means the clock is wrong by 1 s every 90 billion years. Moving from atoms to organisms, time is critical to how all living things interact and create relationships. Without time, relationships do not exist. For instance, mating behavior can occur only if female and male accept a common copulatory momentum. Plants and their pollinators must synchronize their timing for pollination to happen. Predator-prey relationships must also be synchronized in a way that maintains each opponent’s role in the ecosystem. Also, of course, a conversation between two people is not possible if the timing of speech is not synchronized.

2.4.2

Geological Time

In geology, time is the “bedrock” of the discipline, where the division in geological eras allows one to distinguish different periods of Earth’s evolution. Stratigraphy is the branch of the geology that investigates the dimension, composition, and distinction of rock and land formation from one period to another. Geological succession then leads to understanding the continuous alternation of periods dominated by tectonic activity, astronomical events, and climatic change.

2.4.3

Biological Time

In biology, time is considered as a cycle (Gould, 1987) and is expressed as a frequency or number of complete cycles per unit of time. For instance, every Later

Before

Past

Present

Future

Fig. 2.3 Two possibilities to intercept the time arrows, before-after, and a more precise collocation of the perception, past-present-future

2.4 Timingscape: The Temporal Patches

49

Fig. 2.4 It is reasonable to suppose a nested organization of the geo/bio/ecological time-scapes, where the dimension of the objects creates different timings

organ has a metabolic cycle that may be expressed by heart rate, respiratory rate, muscular activity, sleep cycle and wake up, etc. All these cycles refer to a physical time in which a metabolic event occurs. Huxley’s allometric equation (Y ¼ aXb) where Y is the dependent variable, X is the independent variable and a and b are constants, produces a universal exponent (b ¼ 0.25) demonstrating the fractal dimension of biological time (Huxley, 1932) (Fig. 2.4). Physical and biological times are intimately connected. In the biological narrative, von Uexküll and Kriszat (1957) assumed that without time, there can be no living subjects. Yet, simultaneously, without living subjects, there is no time! Heidegger (1998) defined time as a property linked to the concept of being. In this sense, we can consider that time is to be. In psychology and sociology, the perception of time is dependent on its context. For instance, people reported longer subjective session times when presented scenes of nature compared to scenes of urban environments (Berry et al., 2015; Davydenko & Peetz, 2017). Time perception also is an important matter impacting on present and future decisions (Adam, 1995; Pahl et al., 2014).

2.4.4

The Semiotic Time

In an ecoscape, we must consider several domains that operate within the system, of which the semiotic time is particularly important (Nomura & Matsuno, 2016). According to Cowley and Steffensen (2015), organisms have the capacity to integrate past events with the present to anticipate future events. According to Nomura et al. (2018), “we then notice that time is not a substance but a mode of interaction between active agents.” It is reasonable that every living has the capacity to realize a past, a present, and a future time to acquire the resources they need for their survival and fitness. For this, it is possible to distinguish an internal time perceived by all

50

2 Ecoscape vs. Landscape: Riding a Transition

living beings derived by temporal information of environmental events and an external time that is the result of environmental events perceived as a “third-person observer” of the internal time effects on each living. Time is simply a sequence of markers or punctuations that in turn are the results of a ux of information (Fig. 2.5). This means that information is necessary to locate time using tick marks that are the minimum information generator (Bateson, 1972, p. 459). Definitively, time is a series of punctuations or discontinuities where the information is at the maximum level. It is not by chance that time is measured using the oscillation of atoms that have regular frequencies. These punctuations have two main agents, the internal agent that creates its punctuation and a “third-person” observer. When two agents are working with a different internal time, can happen that becomes necessary to assume a common negotiated time (E-Series time, Nomura & Matsuno, 2016). Synchronization is very common among livings when two or more individuals share the same behavior. We can see the synchronization of behaviors between interspecific individuals in schools of fish avoiding predators, in the murmuration of bird ocks, and in vocal duetting in birds. For instance, there are over 400 species,

Rhythmic

Probabilistic

Synchronized

Cyclic Fig. 2.5 The position of the punctuations may be regular producing an emerging rhythm, probabilistic or casual, synchronized between two different agents and cyclic

2.4 Timingscape: The Temporal Patches

51

40% belonging to bird families where the two members of a pair emit vocal duets (Hall, 2009). We can observe synchronization as something common as human conversation. Condon (1970) observed that humans create a temporal cohesion in conversation in a way that alternates verbal messages so that two or more people can communicate. All these behaviors come down to timing one’s actions in response to another’s. We can think of synchronization as the capacity for organisms to produce a personal clock from many internal clocks that allow them to adjust their actions in relation to the actions of others. How all these synchronized personal clocks interact together in nature is the fundamental component of semiotic time.

2.4.5

Ecological Time

Is it possible to imagine an ecological time? If yes, what are the differences between biological time and ecological time and what type of metaphor can be adapted to describe, for instance, the landscape time? In ecology, the concept of time is encapsulated in population dynamics, for instance, the cycles of prey-predators (Krebs et al., 2001) in animal community turnover (Shimadzu et al., 2015) and in ecosystem succession for plants (Connell & Slatyer, 1977). The ecological succession concept formulated by different ecologies at the end of 1800 is an example of how time entered into the ecological realm. In this conceptualization, punctuations are represented by the change of community composition where one step is necessary for the beginning of a successive step (Cowles, 1899).

2.4.6

Landscape Time

A landscape is a heterogeneous ensemble of functioning units that operate at different spatial, temporal, and functional scale (Newman et al., 2019). A hierarchy of biological and ecological rhythms creates a timing mosaic that occurs to impart the complexity of a landscape. The temporal dimension of a landscape re ects the heterogenous nature of the land mosaic. We can image a timingscape as a mosaic of patches of different inherent aspects of time that occur in an environmental matrix (physical, biological, ecological, semiotic) from which different conditions are synchronized between elements creating further aggregations and an array of internal and external phenomena. One example of this has been observed in the cellular configuration of plants. When exposed to constant light period, the individual cells show a heterogeneous behavior, but when exposed to a light/dark cycles, cellular clocks move toward a synchronization (Nomura et al., 2018) (Fig. 2.6). There are at least three timing levels across a landscape. The first level is the biological timing from gene expression associated with all the anatomical attributes

2 Ecoscape vs. Landscape: Riding a Transition

Bioluminescence of circadian reporter

52

0

Short period

Long period

1

2

3

4

Time (day)

5

6

7

Early peak

Late peak

Fig. 2.6 The cellular circadian clocks in intact plants when submitted to constant light condition have a temporal heterogeneous activity. Under conditions of alternation of dark and light cycles, the cells show an evident synchronization. (Nomura et al., 2018, with permission)

(e.g., morphology) and physiological processes (e.g., metabolism) of each organism. A second timing level is linked to the interactions and relationships between individuals such as the courtship displays between female and male as seen in budgerigar (Melopsittacus undulatus) mating behavior (Brockway, 1964). The third level includes the synchronized timing of all components involved in a higher-level, more complex, system like a mosaic of forests and prairies. At this higher level, time is represented by punctuation of disturbance phases or cycles. For instance, a severe fire can occur in a forest only after a long period without fires and when the biomass accumulated is enough to guarantee a severe event. When we discuss disturbance (natural or human-made), the disruption of timing in a system at any of these three levels may have great consequences. Some examples range from the near-extinction event of raptors in the United States caused by DDT’s effects on physiological processes (Carson, 2002) to the global timing mismatch across millions of landscapes from human-caused climate change (East & Sankey, 2020). The timing-scape is ultimately the result of a probabilistic occurrence of events and contributory causes. Every year, summer lighting can ignite biomass in a forest, but only after a long period of dry biomass accumulation a fire becomes a major disturbance agent of a landscape, regulating the biomass pulses and at cascade the dynamics of populations and communities. Landscape timing is the result of different time markers. Probability becomes in this way an important component of the timing process at landscape scale. Some biological clocks are genetically fixed like the periodical emergence of Magicicada larval after a period of every 13 and 17 years (William & Simon, 1995). Other events are the result of a probabilistic occurrence of favorable events like climate conditions for the desert locust plagues or the foxtail seeds cycle, a weed that can wait for decennia to germinate. Irregularities face the regularity or rhythms. According to the theory of resources (Farina, 2012), resources invent the rhythms, but rhythms are time, and finally, we can say that resources “invent” time. Human intrusion in ecosystems and climate changes are both agent of time desynchronization with effects similar to the alteration of the timetable of a railway

2.4 Timingscape: The Temporal Patches

53

traffic. If the timetable of trains is not synchronized, the circulation is not possible. Similarly, if the ight control of airplanes is not synchronized, planes cannot take off or land in security in the great hubs. The same may occur in the natural systems where the fate of individual species depends by factors that operate at larger scale. Definitively, if we consider time from an ecosemiotic perspective, we can say that time is a communication process that use coding mechanisms to create a “text” that can be “read” by species. Every level of organization, from cell to the entire biomes, operates at a specific resolution in space and time. While the spatial aggregation/hierarchy has been considered for long time inside the development of landscape theories, time remains a nominal variable that has escaped the attention of landscape scholars. The reason for the scarcity of attention is mainly based on its immaterial and tangible enigmatic and intractable nature (sensu McTaggart, 1908). Why time is important in the landscape sciences is easily demonstrated by its interwoven complexity of how a landscape evolves and functions. Biotic and abiotic agents operate according to their own specific temporal resolutions. The ight of bees and the ight of condors are different in terms of physiological and anatomical mechanisms, speed, duration, and finally gravitational constraints. This seems obvious and seemingly trivial, but these factors involve important properties that isolate individual species and even entire systems that reduce from one side competition and from another side entropic confusion. McTaggart’s (1908) philosophical presentation of time considers a list of three temporal series that characterize the responses of all the organisms to environmental situations that correspond to a subjective time (A), an objective time (B), and a static non-time (C). A sequence of punctuated events along the timeline generate different time series and, definitively, different timingscapes. These punctuated events contribute to specifying a species’ actions in a system and further reduces inter- and intra-specific competition. Time series A is a sequence of connected events along the timeline running from the past to the present and from the present to the future. Time series B is a process applied by humans to use information about the present in order to place an event within a chronology of events along the timeline. Time series B is characterized by two basic positions, earlier to later, or events before present and events after present. Time series C is a map of time, without a direction but with distinct punctuated events occurring in time. Time series C is composed of a representation of time. In this case, time series C can often be visualized like the quadrant of a clock where the direction is not yet established. Nomura and Matsuno (2016) also introduced time series E which describes an individual’s means of identifying an event in time associated with their individual experience and then aligning all previous experiences in time in context with one another. Time series E is based on the meaning of an event in time in relation to an individual’s experience.

54

2 Ecoscape vs. Landscape: Riding a Transition

The concept of time in biological sciences differs from the classical concept in physics, where the latter assume time is a continuous function, and the former conceptualizes time according to its cyclical nature (e.g., glycolysis, Kreb’s cycle, reproductive cycle). Physiological time or metabolic time are two expressions of the physical realm and do not pertain to biology (Calder, 1984; Schmidt-Nielsen, 1997). Based on this premise, organisms have the capacity to create a temporal dimension in which to live, and the aggregation of organisms in populations and communities creates a greater temporal window with a diversity of temporal views. Because organisms have the capacity to integrate past events with the present ones, many can anticipate future events. Tentacled snakes (Erpeton tentaculatum), for example, are known to predict future prey movements that effectively increases their capture success (Catania, 2008). In this sense, a set of independent biotimes and ecotimes exist across the landscape to create biotimescape and ecotimescape. Biotimescapes can therefore be defined as the temporal synchronicity between organisms across a landscape, while an ecotimescape is all temporal interactions between biotic and abiotic entities. A simple example of a biotimescape can be explained by the temporal interactions between herbivores and plants. For instance, herbivores have to wait for a plant to regrow after browsing before they can browse again. Subsequently, this can be done only by behaviors that oblige herbivores to move around the landscape. Similarly, with an ecotimescape, the water and light cycle across landscapes follow a temporal process that gives rise to many geophysical events and biological activities that are spatially heterogeneous. Time can also be seen as a fundamental component of communication process. In fact, communication for many organisms may be associated with rhythmic punctuations along an ideal time axis. These punctuations may be produced by genetic or learned mechanisms where the final result is the building of a complex world that interact not simply geographically but also using time-specific communication mechanisms.

2.5

The Semioscape

The landscape can be considered as the ecosemiotic domain in which every organism selects suitable spaces (habitats) in which all the necessary resources exist to live and to reproduce. This approach, in the ecological disciplines, differentiates ecosystem model from the landscape model. In the ecosystem model, the quantities and uctuations of information, matter, energy, and organisms play the major role (Odum, 1971). In the landscape model, the dimensions, shapes, and distances among subjects represent the key cues for most ecological and cognitive activities among individual species. Habitat selection by species, and all their living functions, is performed by adopting perceptive and interpretative cognitive mechanisms that require coding and decoding procedures. Cognition in landscape science represents a central

2.5 The Semioscape

55

process of these procedures. A vivoscape is largely perceived by the senses, and, for some evolved species, the mental map of the surrounding complexity is the product of sensorial decoding where sounds, light, colors, odors, tactile, and thermal sensation are ingredients of the living world. The world is full of signals that can be decoded from an interpretant and transformed into signs. This is the conceptual basis of the semiotic or the science of language (de Saussure, 2011). However, semiotics is also a science that distinguishes organismic action into three components: the sign, the interpretant, and the object (Peirce quoted by Eco, 1975) (Fig. 2.7). Kull (1998b) defines semiosis “as a process of translation, which makes a copy of a text, suitable to replace the original text that the original text cannot be used (either spatially, or temporally, or due to the differences in text carrier or language) for the same functions.” This creates an endless chain so that “every semiosis always requires a previous semiosis.” Semiosis can be considered as “the appearance of connection between things which do not have a priori anything in common” (Kull, 1998b). Based on this premise, biosemiotics (bios ¼ life, semion ¼ sign) is a branch of semiotics that studies communication and signs in living organisms (Kull, 1999a). The term biosemiotic was first used by F. S. Rothschild in an article published by the Annals of New York Academy of Sciences (Rothschild, 1962: 777) (Kull, 1999b), although von Uexküll (1909) was the first to discuss the bridge between biology and semiotics, as reported by Sharov (1991). Marginally considered in biology and ecology, the biosemiosis can contribute to a better understanding of the cognitive

Fig. 2.7 Ferdinand de Saussurre, FR (1857–1913), Charles Sanders Peirce, US (1839–1914), the founders of the modern semiotics

56

2 Ecoscape vs. Landscape: Riding a Transition

processes of organisms that shape the landscape. We can distinguish this process within a vertical and horizontal semiosis. The vertical semiosis represents the way communication crosses the different inner parts (e.g., organs) of organisms creating a semantic closure. This idea is strongly related to the autopoiesis hypothesis. The horizontal semiosis consists of the transfer of messages from one organism to another. Until recently, semiotics and biosemiotics have been considered only related to humans and animals respectively. However, Krampen (1981, 1992) has expanded the semiosis concept to plants (see also Kull, 2000). Similarly, biosemiotics has also evolved into the concept of ecosemiotics. Ecosemiotic has been defined as the semiotics of relationships between nature and culture (Hoffmeyer, 1996; Kull, 1998a) but also as the semiotic relationship between organisms and their environment (Emmeche, 2001; Noth, 1998). Hoffmeyer (1997, 2008) argues to enlarge the ecological niche theory to include a semiotic niche in which a set of visual, acoustic, olfactory, tactile, chemical, and electromagnetic signs are considered as part of the heredity of a species that assures their chances of survivorship (semiotic fitness). Most of biosemioticians refer to the extraordinary scientific contribution of Jacob von Uexküll in the first half of the nineteenth century as the originator of biosemiotics. The biosemiotics theory relates well with the Umwelt, which represents the subjective universe perceived by organisms using a semiotic procedure and choices to carry out their actions in the environment. The ensemble of Umwelts that come into contact and interact with each other creates a new dimension of phenomena that Yuri Lotman (1984) (quoted by Kull, 1998b) defined as a “semiosphere.” In conclusion, the contribution of biosemioticians to the interpretation of the living complexity has dramatically increased during the last decennia. Although there is a disparity in approaches and paradigms, it seems that semiotics can improve biological and ecological knowledge. The dual vision of a world composed by a matrix of invariant systems and by autopoietic organisms that create and invent a semiotic matrix (variant systems) is in accordance with the complexity paradigms common in landscape ecology. The description of a landscape based on physical and ecological processes can be paired to a landscape created by ecosemiotic processes. The difference between these two processes consists primarily on the open character of the first. The energy enters, is dissipated, and is transformed into information by decodifiers. In the latter, information is not created simply by input of external energy but by the creation of new relationships between the composing systems through adaptive evolution. The landscape can also be considered a mosaic of ecological and semiotic processes. Semiosis is a highly creative system linked with the biochemistry and the evolutionary forces that dominate the ecological landscape. If we consider the novelties in the mosaic configuration, such novelties substitute the old ones by a temporal process. The new configurations are like unknown systems that can open a dialog with the old ones.

2.6 Ecological Codes

2.6

57

Ecological Codes

Information and meaning are independent processes that can only be connected using coding-decoding procedure. In fact, the transition between the information, for instance, perception of an object, and the meaning (assignment of a name and identification of the associated function or feeling) is based on genetic, neurological, and cultural codes (Barbieri, 2019). The communication between agents (observers) and agencies (landscape features) requires that the signals that emerge from landscapes are transformed into signs by an encoding process that assigns a specific meaning to the perceived information (Barbieri, 2003). The landscape can be considered as a container of information that is available for every organism in some capacity that is assured only when an encoding-decoding procedure transforms information captured by the senses into meaning. This process requires cognitive templates to be present inside the animal brain that is genetically distinct and permanently stored into accessible memories to be utilized for specific functions. Such templates are obligate components of neural codes that results when an electric or thermal scansion of the brain is made during the input of an external stimulus (Myers et al., 2015). The cognitive templates may have visual, acoustic, tactile, odorous, thermal, and electrical receptors according to the senses utilized. Each template is associated to specific potential resources that in turn are associated with a physiological need. In humans and probably in many other cognitively evolved animals (f.i., apes, crows, cetaceans, elephants), a broad category of psychological conditions (fear, curiosity, sadness, irritation, anxiety, etc.) is represented by specific cognitive templates. Such templates require external conditions to be paired with actions and consequences not only in association with landscape features (light, colors, objects in directional movements) but also by emergent behavioral responses. For instance, a large group of animals can create specific feelings (fear, curiosity) in an observer, and the spatial arrangement of prey can solicit a decision in a predator about whether or not to unleash an attack. The coding process can be a little more complex due to the further external signals that are produced in social organisms. We are referring here to the cultural codes. Cultural codes are the result of patterns created by a strict relationship between organisms that adaptatively create local rules. These rules can be changed in short time producing evident effects on the behavior of individuals. Cultural codes are necessary to assign to characters of landscape-specific quality like beauty, peacefulness, or modernity. Cultural codes are at the basis of human language where the use of symbols prevails. Language accelerated in an extraordinary way with ecosemiotic processes across a landscape humans have inhabited for a millennia, increasing the efficiency of our species to transmit information from an individual to another by using acoustic symbols. A clear example innate to many mammals, birds, and reptiles is the ecoacoustic codes that are used by a species as a diffuse mechanism to communicate information, to maintain social contacts, to navigate, and to select habitats.

58

2 Ecoscape vs. Landscape: Riding a Transition

They are based on the interpretation of songs, calls, and vibration emitted in special organs (Farina, 2018).

2.7

The Eco-Field Hypothesis

Farina (2000, 2001, 2004) and Farina and Belgrano (2004, 2006) have recently connected the ecological niche theory (Grinnell, 1917; Hutchinson, 1957) with the habitat paradigm and the spatial attributes of the perceived landscape they describe in a new cognitive model called the “eco-field.” The relationship between the ecological niche concept and the eco-field model is very distinct. In the ecological niche theory, every function is confined into a precise domain that does not overlap with the domain of other species to reduce competition. Ecological niche is a conceptualization utilized to explain the competition avoidance between species. The narrative on the eco-field model emphasizes the mechanisms adopted by living beings to intercept resources necessary to satisfy the internal needs to optimize their efforts to search for resources because they are heterogeneously distributed in space and time, and their availability is not always easy to be predicted. The term eco-field has been coined and inspired by the concept of “field” from Ervin Laszlo (1996: 167). According to Laszlo, “fields are curious entities: ordinarily only their effects are observable, the field themselves not [. . .] fields are medium that interconnect phenomena”. In the eco-field model, the field connects internal needs of organisms with the resources necessary to satisfy such needs using intermediate passages represented by functions, cognitive templates, spatial configurations of objects in the landscape, and finally resources. The vivoscape is the ecological place where perception, cognition, and culture meet within a geographical mosaic of soil, vegetation, human artefacts, organisms, information, and meaning. For a species survival, they follow autopoietic procedures to intercept the necessary material and immaterial resources to postpone mortality (Farina, 2012). Ecological, neurological, and cultural mechanisms are adopted to achieve this fundamental goal. For instance, Wiederman and O’Carroll (2013) have discovered in dragon ies a single neuron that reacts to a prey’s movements confirming a selective attention. Vogelstein et al. (2014) have demonstrated how a nervous system can generate motor outputs that depend on sensory input from the external world and on internal state. The eco-field model is based on semiotic mechanisms to intercept the resources finalized to accomplish specific physiological needs obtained by specific functions. The eco-field model is central and ancillary to developing vivoscape theory (Fig. 2.8). The eco-field model can be applied to visual configurations, to acoustic patterns, to mosaics created by olfactory gradients, to thermal constraints, and to cognitive representation like safety, aesthetic views, sense of place, symbolic landmarks, etc. The eco-field model generally is based on a hierarchy of semiotic choices. For instance, a grain of millet has the interior composed of proteins that are the resources requested for a granivorous species to survive. However, these proteins are masked

2.7 The Eco-Field Hypothesis

59

Gazelle flock pattern

Defence

Foraging

Migration

Lion predation eco-field

Fig. 2.8 Example of predation eco-field. A lion can recognize the most favorable spatial arrangement of gazelles to have a differentiated (length and dimension of arrows) level of successful predation

by an unpalatable shell. The grain in turn is protected by glumes organized in dense panicle. The millet plant is reared in millet fields that are well differentiated by the surroundings (forests or other crops). For a sparrow, access to the millet protein is like passing through an obstacle course. The highest level of the obstacle course is the millet field the sparrow must distinguish from other land covers (stage 1); once inside the millet field, the sparrow must distinguish the panicle from the leaves (stage 2); at the panicle level, the sparrow must distinguish seeds from glumes (stage 3); and finally, for each seed, the sparrow must separate the cover from the carbohydrate and protein core (stage 4). At stage 1, the sparrow must have generated a cognitive template of a millet seed that it is able to associate with a cognitive map of a landscape where it can land to find and eat a good millet seed. At stage 2, when at the plant, the bird must distinguish between leaves and panicle, subsequently leading it to stage 3 to distinguish the seed within the glume casing. At the fourth and final stage, the bird must trust that under the cover there are carbohydrate and proteins to satisfy its original need to acquire the seed. In this simplified example, at least three eco-fields are necessary to separate the carbohydrates that are the resources from the other semiotic envelops.

60

2 Ecoscape vs. Landscape: Riding a Transition

Adopting the vision of the “subjective surrounding” or Umwelt (von Uexküll, 1940, 1982), the eco-field is defined as the “space configuration meaning carrier.” According to this definition, every function that a species activates needs a specific spatial configuration recognized by innate or cultural neural mechanisms. A species settles where such configuration exists. The life cycle of every species may be considered as a collection of temporary activation of life functions like foraging, resting, mating, drinking, orienting, patrolling, etc. Every function requires a specific spatial configuration of objects that in some cases are recognized by a comparison with a cognitive map like described in animal navigation (Dyer, 1998; Gallistel & Cramer, 1996). Animals have cognitive capacities to assure a spatial memory (e.g., see Dyer (1996) for the honeybee and Tammero and Dickinson (2002) for Drosophyla) or an olfactory memory like in cricket (Matsumoto & Mizunami, 2002) or in honeybee (Thorn & Smith, 1997), in food-storing birds (Kamil & Cheng, 2001; Sherry, 1989; Sherry & Duff, 1996), in searching for food by ants (Graham et al., 2004), and in reorienting in fishes (Sovrano et al., 2002). Spatial learning has been found to be correlated with sexually dimorphic status in meadow voles (Microtus pennsylvanicus) and deer mice (Peromyscus maniculatus) (Galea et al., 1996), where gonadal hormones are responsible for differentiating the spatial performance. Changes in behavior and spacing occupation has been observed in males of robin (Erithacus rubecula) (Tobias & Seddon, 2000), supporting the hypothesis that changes in function (modulated by a change in physiological status) elicit a different surrounding appreciation. Age is an important variable in this case. For instance, Robichaud et al. (2002) have found a different perception between juvenile and adult birds dispersing along a riparian buffer strip surrounded by a managed forest. Displaced pigeons use extensively familiar landscape, and this improves homing capacity, as discussed by Wallraff et al. (1999) and Biro et al. (2002). Pigeons released after 5 min of preview of the surroundings have a better homing performance compared with individuals immediately released. For instance, a skylark (Alauda arvensis) searches for seeds to eat; the seeds can be found in several environmental contexts from woodland clearings to open prairies, but it is only in prairies that skylarks live confirming that this species can distinguish habitat suitability using visual cues. This means that every function requires a “search image” (Dawkins, 1971; Langley et al., 1996; Pietrewicz & Kamil, 1979; Tinbergen, 1960) inherited by adaptive mechanisms expressed by genes. We have no ideas as to how the specific functions switch in the neural system the searching image necessary to satisfy the uttered need. If for every function there exists a space configuration, meaning carrier (the eco-field), the habitat for a species is determined by the summation of all the eco-fields. Moreover, the eco-field is the combination of natural objects necessary to process a specific life function requested by a particular (physiological/psychological) need. To enhance survival, a species requires several eco-fields that, in general, exist in the same geographical space (habitat). Holistically, a species’ habitat is the summation of all the different eco-fields inherent to an individual. Using a book as a metaphor of the eco-field, the book is the habitat and the pages are the different eco-fields. The

2.7 The Eco-Field Hypothesis

61

subject of the book’s text is specific to a species or individual with the information of the text representing the coded information of the habitat. It is important to accept the eco-field model, to recognize that for every function individuals require a specific spatial configuration. For instance, indirect evidence of eco-field mechanisms in action has been observed in the daily regulation of body mass in European robin (Erithacus rubecula) (Thomas & Cuthill, 2002). When wild robins were supplied food ad libitum, birds foraged until they reach the same body mass at dusk, regardless of the body mass at dawn. This is a strong indication of a nonrandom and precise strategy of robins to handle a vital function like foraging in way to balance predation and other concurrent functions. The eco-field hypothesis expands and enhances the concept of habitat (Franklin et al., 2002). Every species perceives land mosaics differently (Etzenhouser et al., 1998) and should select habitats with the highest score in terms of each eco-field. Some habitats have a higher score for a specific eco-field and lower score for others. This fact creates a different selective pressure at the population level. Some geographic areas have high scores for a specific eco-field and lower for others; this allows a species to persist, but the result is affected by an area-specific environmental pressure. This fact is in line with the theory of evolution and with the struggle for survival. Recently, Pulliam (1988, 1996) has described some sources and sink populations with direct correlation with habitats, posing the question that habitat quality is based not on binary choice 0–1 but on a fuzzy mechanism where intermediate conditions are the rule and not the exception. The paradigm of the eco-field requires empirical verification and opens a new era of integrated investigation between behavioral, ecological, and evolutionary research (Mitchell & Powell, 2002). It emerges from experimental manipulation of animal movements in heterogeneous landscapes where behavior assumes importance on the structure of the habitat. It is well documented that the physiological state of individuals affects the movement and behavior (Bell, 2012). For instance, Morales and Ellner (2002), testing random models on confused our beetle (Tribolium confusum, Coleoptera, Tenebrionidae), argued that observed behavior heterogeneity reduces the efficiency of random models to describe the animal movements in an experimental arena. The paradigm of the eco-field can be utilized to investigate the relationships between plants and their environment. Plants, like animals, are organisms with an intense relationship with the surrounding but differ from animals for lack of an explicit intention and consciousness. Plants can be considered as second-order multicellular organisms with a perceptual capacity not different from the first-order cells that compose them. The eco-field in plants are detected by the changing rate of growing roots, branches, and leaves, increasing or depressing the chance of survivorship of seeds and young and adult plants. As pointed out by Callaway (2002), plants probably detect their neighbors, and, consequently, they do not respond only in terms of resource availability but also of surrounding characters. Most of the investigations have focused on avoidance mechanisms at root and shoot level, but others (Gersani et al., 2001) have found (in soybeans) an increased root growth when shared with conspecific competitors.

62

2 Ecoscape vs. Landscape: Riding a Transition

The eco-field approach can solve problems linked to difficulty in connecting the patterns of biodiversity with the landscape characters as cautioned by Jeanneret et al. (2003) on using a set of organisms. In the debate regarding the definition of habitat and the selection of environmental variables that describe the relationships with species and their aggregations (populations, guilds, communities), the eco-field hypothesis reduces the uncertainty intrinsically considered when environmental attributes are coupled to species distribution and abundance (Cushman & McGarigal, 2004). In fact, the eco-field hypothesis assumes that a species enters into a semiotic relationship with their surroundings after a living function is activated. The “traditional” approach to consider environmental variables according to the perception of the investigator does not provide an honest investigation of an organism’s life challenges or the truly important components of their surroundings. Often, several variables are selected and then condensed using a statistical procedure (e.g., Principal Component Analysis) to extract a minor number of latent variables (Hotelling, 1933; Pearson, 1901). There is no evidence at this time to say that a species can react to so many variables over the duration of a studies sample period. Often the gradient necessary to perceive a change in the subject’s behavior requires an unknown and often arbitrary time-space scale. We agree with the conclusion made by Cushman and McGarigal (2004) that a decision in terms of habitat preferences is achieved using hierarchical decision mechanisms, but the sensitivity to environmental variables should be restricted to only a few cues. Assuming that an individual is connected with its surroundings through interaction, mechanisms must exist for these interactions to occur. Such mechanisms must involve the use of organic codes. Organic codes represent the connection of an organism’s inner and outer world. It is likely that several codes are contemporarily active at a time, but understanding the mechanisms of why they occur and how they in uence behavior requires one to test the eco-field hypothesis with the functions that drive these processes during a specific time and space. The eco-field hypothesis converts the traditional landscape-species concept into a broader conceptualization of environmental-species relationships. The eco-field hypothesis can be useful for solving the dilemma of the movement rules of herbivorous in heterogeneous landscape. For instance, Gross et al. (1995), working on the movements of bighorn sheep (Ovis canadensis), assumed that the decision to move from one grazed plant to another follows a nearest-neighbor rule but does not explain their large-scale movements. The eco-field hypothesis can explain this complex behavior. The short distance movement between plants requires a foraging eco-field characterized by high density of palatable plants that is elicited by the function of foraging. When bighorn sheep move over longer distances, searching for a new foraging area, they use a different eco-field in which the spatial configuration must be based on obstacles and open paths. A landscape can be considered a container of eco-fields species specific, and its role to support life is so important that a multidimensional framework to consider micro-relationships at smaller scales and macro-relationships at larger scales is required for a broader understanding of species-environmental relationships across

2.8 The Sensoryscape

63

the landscape. One must also consider in this context the size of the organism and the realized scale of a species’ perception and level of interaction within the world. Shape, size, spatial arrangements of environmental factors, and patterns of energy are all objects that, when processed using appropriate codes, are associated with specific meaning a species utilizes to perform their living exigences. All these objects re ect an organismic vivoscape that is composed of a mosaic of cognitive patches and mental templates associated with an individual’s functional needs. In this way, the landscape represents a more holistic way of understanding the relationships between species and resources. The eco-field model represents a theory designed to understand ecological mechanisms that link species to their environment. The intentions are to link environmental relationships with the perception of the species of interest, not from an anthropocentric vision or human-focused interpretation. As a result, the eco-field theory requires other many perspectives across fields to fully develop its potential to understanding nature’s complexity.

2.8

The Sensoryscape

The vivoscape also includes a set of five scapes, each related to a specific sensorial capacity (visual, scent, acoustic, tactile, and thermal) that gives all organisms a perception of reality. In this case, we can use the term sensoryscape to mean every configuration of potential perception embodied by the environment that any given organism has the ability to sense directly with their unique sensory organs. We can then apply these senses into five composing scapes of the vivoscape: visionscape, scentscape, soundscape, tactilscape, and thermoscape. These different scapes are the subunits of the vivoscape and include every perceivable aspect of an organism’s environment. It may be important at this time to note that these sensoryscapes are present in both terrestrial and aquatic environments. Most organisms have uninhibited access to their sensoryscape. An organism’s sensory capacity is unique to the individual and across species. Yet, each organism utilizes their senses as a strategy to intercept and differentiate fundamental signals from the external world. The functionality of this behavior can be voluntary or incidental depending on the exchange of abiotic and biotic processes between organism and signal. The signals that a species can intercept from their surroundings have a common origin in the physical characters of matter and available energy. These signals may be activated by the will of organisms opening a communication channel. In other cases, signals may be received passively when organisms intercept the characters of matter, like tactile signals. Some signals are both willfully and passively activated by organisms such as visual, acoustic, and scent signals. Visual signals are the result of different re ection capacities of objects to light. For instance, the color black is typical of materials that absorb all the spectral frequency of light, while the color white is the result of the re ection of all light’s wavelengths. The varieties of colors that are displayed in the environment are the complementary results of substances that absorb a specific light wavelength. Since visual signals are

64

2 Ecoscape vs. Landscape: Riding a Transition

always present when there is light, the light-sensitive organs of animals allow them to differentiate visual information intentionally during food selection, for example, or passively as they move through their environment. Light-sensitive organs are active and absorbing visual information whenever they are active for any given purpose. Similarly, sounds and scents are omnipresent in most environments. Acoustic signals are the results of vibration of a medium while scent signals are results of chemical molecules released by a variety of substances. Much like vision, the ability of an organism to hear a sound, or smell a scent, is limited by the capacity of those individuals’ abilities to detect acoustic and scent signals. Cats and humans have a very different way of hearing the world. The human threshold of hearing is 22-22,000 Hz, while the threshold for cats is 48-85,000Hz. This means that humans have the capacity to hear much lower-frequency sounds than cats, while cats can hear much higher frequencies than humans. With regard to scent, dogs’ sense of smell is said to be 40 times greater than humans. As an additional note, sound and scent information cannot be turned off like that of sight or touch. Sound organs are developed in the womb so that the sounds of the mother’s heart are some of the first sounds to be heard by an animal. It has also been suggested that hearing is the last sense to be lost upon death. The manner in which sound and scent organs are designed makes sounds and scents a constant source of stimulation. To adjust for this, animals have evolved ways to “tune out” acoustic and scent information that does not provide an advantage to the organism’s purpose. This is why the rumbling sound of a truck outside does not bother the conversation you are having with your partner in a restaurant or why the variety of food smells that surround you does not confuse your smell of the garlic and scallop linguine you ordered to eat. Through the process of natural selection and genetic mutation, the diversity of sensory capabilities is matched by the diversity of extant species on Earth. Each sensoryscape unit allows each individual and every species to perceive their surroundings in different ways. The combination of these different perceptions provides a selective advantage that allows many species to engage and interact with the environment and with other species, all of which occur in the ecosphere.

2.9

Animal Movements in the Landscape

According to the eco-field hypothesis, it is possible to distinguish different typologies of animal movement and to associate every typology to a spatial requirement (Baker, 1996). However, the spatial configuration of the land mosaic that possesses all the cognitive maps required for a specific function can easily be disrupted and strongly affected by the presence and disturbances introduced by other organisms like humans. As such, the movements of animals across a landscape are in uenced by many natural and human-made factors that all vary according to each species’ life history.

2.10

Visionscape and the Aesthetic Dimension of the Vivoscape

65

Human infrastructure causes physical fragmentations to the landscape. Yet, additional factors that drive animal movements can be hidden beyond what is visible. Whittington et al. (2004), for instance, have studied the tortuosity and permeability of roads and trails in wolf movement. They observed that wolves seemed to have a major sensitivity to trails in which humans and dogs were common. This appeared to be caused by a source of hostile, unnatural, human, and/or dog scent rather than the physical presence of the trail. These findings give evidence that a scentscape should be considered within the landscape perspective. The landscape around us is largely manipulated by human intervention in agriculture, forestry, and development. As a result, the emerging mosaic has strong importance, not only for biodiversity and population abundance but also for many functions performed by species in relation to landscape characteristics. SteffanDewenter and Kuhn (2003) have demonstrated how landscape configuration can affect honeybee foraging. When observing bee dance activity used to communicate the location of hives, Steffan-Dewenter and Kuhn (2003) found a greater dance activity in landscapes with greater structural complexity versus simple, heterogenous landscapes. Foraging distance of pollen-collecting bees was also significantly greater in simple rather than in complex structured landscapes. Landscape characteristics are important elements for creating genetic heterogeneity in plant and animal populations. This was demonstrated by Merckx et al. (2003) on two captive populations of Pararge aegeria butter y originating in woody and open landscapes. Populations living in woodlands covered longer distances and were more active than populations living in open landscapes (farmland). Considering the controlled conditions of this experiment, their results show a weight of evidence that Pararge aegeria are dependent on heritable variation fixed into the genetic memory that are in uenced by the environmental constraint of landscape mosaics.

2.10

Visionscape and the Aesthetic Dimension of the Vivoscape

Dimension, shape, and spatial distribution of habitats are a central theme of landscape ecology. A landscape is considered to be the geographical character of an ecosystem that is directly related to the geometric attributes of living and not living objects. Unfortunately, the visual component of a landscape remains largely obscure and unattended in landscape ecology. Hence, there exists an important significance to deconstructing the numerous landscape components where visual variables are considered among others we address in this text. The importance of vision in human and most terrestrial animals makes the visionscape one of the most dominant representations of the environment. Signals, like the shape and color of objects, are intercepted by eyes and transformed by the

66

2 Ecoscape vs. Landscape: Riding a Transition

occipital lobe of the brain into signs through a variety of neurological decoding processes that assign meaning to the visual information based on mental formations from an animal’s instinctual and/or experiential memory. In most cases, the brain attempts to assign meaningful information to visual stimuli with what it is familiar with. Because visual information is so greatly important to most terrestrial animals and humans, there arise many consequences to how the landscape is inhabited by wildlife in relation to the manipulation and use by humans. Despite the in uence that the landscape mosaic has on human behavior, humans do not often consciously consider landscape configuration in their daily routines. Rather, humans focus intently on the aesthetic, criticism, and appreciation of landscape arrangements. This can be seen in the landscaping of suburban and urban areas or the cultural contributions of landscape paintings and photographs throughout the world. As a field of study, environmental aesthetics is a subfield of the philosophical aesthetics and is based on the appreciation of natural environment in comparison to human modified systems (Carlson, 2001a, 2007). Coincidently, the word aesthetic is usually associated with the human domain. However, several species are known to exhibit a sense of visual aesthetic. For instance, bowerbirds are a group of birds belonging to the Ptilonorhynchidae family with an Austro-Papuan distribution (Rowland, 2008). Bowerbirds have an extraordinarily complex courtship and mating behavior, where males build a bower to attract mates demonstrating an incredible landscape aesthetic appreciation (Endler, 2012). Yet, pragmatically as a science, the relationship between aesthetic and ecology is not found to be direct (Barrett et al., 2009; Toadvine, 2010). This is not to say we should dismiss aesthetic from the study of ecology. Rather, the presence of aesthetic in ecology is quite apparent. For instance, the plumage of male birds of paradise provides evidence that a female mate selection is visually based. The same is true for the vibrant feet of the blue-footed booby (Sula nebouxii). Chameleons and many other species rely on their ability to stay visually cryptic in their habitats to avoid predation or enhance their predation success. Some species also display morphological mimicry to visually appear like that of another venomous species. Although it is obvious, mimicry was not developed out of attery; it is evidence that visual cues are a very important part of animal behavior, habitat selection, and evolution. The human definition of aesthetic is notably different from that of animals. However, the visionscape provides a range of important utilities in ecosystems, from the visually pleasing to the visually functional. It is mostly true that humans cannot directly sense the ecological quality of an environment. Similarly, an ecologically healthy landscape is not always a beautiful place (Lee-Hsueh, 2018). From a human perspective, aesthetic perception and ecological quality seem to align where beauty is the “measure” by which quality is determined. However, there is more to life than humans’ subjective judgment of beauty. In this case, we can say that an ecological aesthetic, or the visually functioning component of a landscape, fills the gap between scenic and ecological experience. In fact, for the true ecological relationship between humans and nature, beauty and ugliness are very much in line with how landscapes are modified and how they are preserved. Yet, the

2.10

Visionscape and the Aesthetic Dimension of the Vivoscape

67

subjective nature of human-ecological aesthetic creates a stark contrast in the environment and human societies. The United States, for instance, has set aside millions of hectares of public land based almost entirely on visual aesthetic. This has allowed beautiful landscapes to remain largely intact and ecologically functional. Conversely, the highly biodiverse rainforests in Southeast Asia are being razed to be replanted with oil palms. In such cases, visual aesthetic is less important to monetary gain. Both examples emphasize two different outcomes with extreme impacts on ecological aesthetic and quality. There is a universal law in nature that states that regularities and complexity of forms, volumes, surface, and objects are the signals necessary to an observer to make decisions on resources. For this, nature has created unequivocal signals that mean high informative carriers of meaning, assuming that the more a subject is beautiful, the more it is attractive (Fig. 2.9). This universal principle that reduces the risk of confusion in the recognition of patterns guides the adaptive strategies of all the species and creates a “beautiful” world that is coincident with the ecological world. Beauty has some scalar properties, for instance, the beautiful color of a butter y become component of a butter y morphology. The same appears in many birds that have fantastic colors on the feathers, creating attractive bodies. When an organism, plant, or animal are stressed, ill, or aged experience a loss of beauty that can be easily appreciated by an external observer that can change its behavior. In conclusion, aesthetic is not simply a visual perception of the surrounding. The matter is more complex when beauty is attributed to an ensemble of objects and to the landscape. In

Fig. 2.9 This image (visualscape) has beautiful colors and composition and produces many other feelings in the observer. (Quintino painter, Signano, Italy, 2020, Farina courtesy)

68

2 Ecoscape vs. Landscape: Riding a Transition

Fig. 2.10 Mont Ventoux (Provence, France) and Francesco Petrarca (1304–1374) poet

this case, we have difficulties in comparing or separating the human perception from the perception of other organisms. Nevertheless, the concept of beauty/aesthetic always is coincident with ecological functionality (Gobster, 1999). For instance, a managed forest to assure scenery is not coincident with forest sustainability or forest complexity. In this case, at least for humans, an underground free from scars or dead trees assures a better visuality and improves the capacity of rapid movements that are required to assure a safe condition. In this case, beauty has a more specific requisite exclusive of a species. The entire ecosystem has not benefited for this, but benefits are for individual species. Beauty may be defined by coding mechanisms to select appropriate objects, habitats, or conditions. The landscape assumes a role in the process of ecological connection between beauty and ecology, actually largely mediated by cultural filters. In this way at the time of the famous ascent of the poet Francesco Petrarca to Mont Ventoux (Provence, France) on 26 April 1336, beauty from nature probably was not like in present time (Fig. 2.10). Mountains were not climbed, and for local people the top of mountains was simply a rocky area or an area to drive sheep or goats. Petrarca was the source of inspiration about scenery that probably was without a specific meaning for local people confirming that some aspects of natural beauty are a matter of culture. This fact after several centuries has been proved in a recent study on landscape preferences carried out in a Norvegian countryside (Dramstad et al., 2006) where the different groups of people (students versus local people) have had a divergent reaction to landscape interpretation. The former demonstrated a more complex interpretation of the landscape incorporating other values than visual cues alone, like biological diversity. The appreciation of visual cues and their effect on the human preferences have been object of psychological investigations that have demonstrated that not only aesthetic enters into play but also other emotional states. The vision of natural landscapes has been demonstrated to have beneficial effect of people subjected to physical and psychological stress (Ulrich, 1979). For instance, a preoperative

2.10

Visionscape and the Aesthetic Dimension of the Vivoscape

69

hospital patient has less anxiety if they have outside the window a vision of a park instead of a developed parking lot. People have preferences more for natural spaces than for urban areas (f.i., Skrivanova et al., 2014), and in a natural landscape, preferences change according to the addition of specific characters. Education, gender, and age have in uence on the preferences (Häfner et al., 2018). This seems a contradiction because we are living in urban areas and natural spaces are simply locations in which to spend a small part of our life.

2.10.1 Topographic Prominence We can define topographic prominence as the height of differential between an individual and his/her surroundings as apprehended from the individual’s point of view (Llobera, 2001). It is the perception of terrain that lies below the individual’s location. The topographic prominence must have been very important during the prehistoric times when human affordance was strategic for survivorship. Hunters or defenders probably used the topographic heterogeneity of the land to increase the individual and group performance. According the different uses, prominence is changed scalarly. If a small radius is used, prominence assumes high values, but such values decrease dramatically when the radius is enlarged. Often, we appreciate the shape and scenery from the visible landscape, but we do not pay attention to the visible and invisible parts of the territory when we move across and can or cannot observe details (Baldwin et al., 1996; Krause, 2001) or underestimate the importance of visual complexity of landscape and species diversity (Hehl-Lange, 2001). The position where a species is temporarily located can make the difference in terms of predation risks. During war time, soldiers were digging trenches to reduce the implicit prominence on the terrain escaping to the sight of enemies, but an opposite strategy is performed when the terrain scouting is requested. During the long evolutionary history of humanity, the suitability of the landscapes probably has been affected by land forms more or less adapted to offer topographic prominence (Fig. 2.11). The prominence effect is in practice a process felt by every climber and mountaineer when they climb a mountain. The possibility to have a widest possible view of the surroundings remains an innate attitude in every people.

2.10.2 Landscape Aesthetics as Ecological Indicators Although aesthetic is an expression solely attributed to humanistic research tradition, its implication in the ecological field seems extremely beneficial to better understand

70

2 Ecoscape vs. Landscape: Riding a Transition

Fig. 2.11 The San Leo Fort (Rimini, Emilia-Romagna Region, Italy) located on top of the mountain is an example of utilization of a relief to build a defensive system

a landscape functioning. In fact, many aspect of a landscape cannot be directly measured, and the use of adapt proxies can be very beneficial. Two theories can be applied to connect ecology and landscape aesthetics. These include an evolutionary theory (Appleton, 1975a, b; Kaplan & Kaplan, 1989) and cultural theory (Carlson, 2001b; Tuan, 1974). The evolutionary theory recognizes a relationship between survival and well-being and visual choices. The cultural theory considers cultural and personal experiences as the main drivers of preference. For instance, the aesthetics of a painting of male songbirds is the product of a subjectively, culturally driven mechanism, while the color phenotypes of the painting’s subject are ecological products derived for enhanced reproductive success as a result of evolutionary processes. An integration between these two divergent visions is recognized as appropriate (Bourassa, 1991; Norton et al., 1998; Bourassa et al., 2003) where genetic and cultural mechanisms coexist. At least nine key concepts have been utilized to characterize a visionscape: stewardship, coherence, disturbance, historicity, visual scale, imageability, complexity, naturalness, and ephemera. The following concepts of the visionscape are taken from Tveit et al. (2006): Stewardship: Concept: we define stewardship as the presence of a sense of order and care, contributing to a perceived accordance to an ‘ideal’ situation. Stewardship re ects human care for the landscape through active and careful management. Dimensions: sense of order; sense of care; upkeep. Landscape attributes: signs of use/non-use, vegetation succession, buildings, linear features (fences, paths etc.) management detail, drainage, waste.

2.10

Visionscape and the Aesthetic Dimension of the Vivoscape Potential indicators: percentage of abandoned land and stage of succession, status of maintenance of buildings, management type and frequency, length and condition of linear features (for example fences and walls), presence of waste, wet areas in crop fields, presence of weed. Coherence: Concept: we define coherence as a re ection of the unity of a scene, where coherence may be enhanced through repeating patterns of colour and texture. Coherence is also a re ection of the correspondence between land use and natural conditions in an area. Dimensions: harmony, unity/holistic, land-use suitability. Landscape attributes: land use water patterns. Potential indicators: percentage land use in correspondence with natural conditions, water presence and its spatial location, repeating colours and patterns. Disturbance: Concept: we define disturbance as lack of contextual fit and coherence, where elements deviate from the context. Disturbance is related to constructions and interventions occurring in the landscape, of both temporary and permanent character. Dimensions: lack of contextual fit, lack of coherence. Landscape attributes: extraction; natural disturbance (for example: fire and windfall), constructions (for example: motorway infrastructure urban elements temporary constructions). Potential indicators: number of disturbing elements, percentage area impacted by disturbance, visibility of disturbing elements Historicity: Concept: we define historicity as determined by two dimensions, historical continuity and historical richness. Historical continuity re ects the visual presence of different time layers, also in uenced by the age of the layers, while historical richness relates to the amount, condition and diversity of cultural elements. Dimensions: historical continuity, historical richness. Landscape attributes: visible time layers cultural elements (for example, historical agricultural buildings, grave mounds, ruins, cairns, signs of earlier cultivation, fences, stone walls, historical roads and paths) traditional agricultural structures. Potential indicators: presence of cultural elements, shape and type of linear historical elements, age of historical elements, number of time layers, percentage area of historic continuity, presence of traditional land use and pattern. Visual Scale: Concept: we define visual scale by the perceptual units that re ect the experience of landscape rooms, visibility and openness. Dimensions: visibility, openness, grain size. Landscape attributes: topography, vegetation, man made obstacle. Potential indicators: viewshed size, viewshed form, depth of view, degree of openness, grain size, number of obstructing objects.

Imageability: Concept: we define imageability as qualities of a landscape present in totality or through elements; landmarks and special features, both natural and cultural, making the

71

72

2 Ecoscape vs. Landscape: Riding a Transition landscape create a strong visual image in the observer, and making landscapes distinguishable and memorable. Dimensions: spirit of place, genius loci, uniqueness/distinctiveness, vividness. Landscape attributes: spectacular elements, panorama, landmarks, water, iconic elements. Potential indicators: viewpoints, presence of spectacular, unique or iconic elements and landmarks, presence of historic elements and patterns, presence of water bodies, percentage area of moving water. Complexity: Concept: we define complexity as the diversity and richness of landscape elements and features, their interspersion as well as the grain size of the landscape. Dimensions: diversity, variation, complexity of patterns and shapes. Landscape attributes: linear features, point features, land cover, land form. Potential indicators: number of objects and types, evenness index, dominance index, diversity indices, shape diversity, size variation indices, heterogeneity indices, edge density, aggregation indices. Naturalness: Concept: we define naturalness as closeness to a preconceived natural state. Dimensions: intactness, wilderness, natural, ecologically robust. Landscape attributes: natural feature, structural integrity of vegetation, vegetation/landcover type, water management, patch shape, edge shape. Potential indicators: fractal dimension, vegetation intactness, percentage area with permanent vegetation cover, presence of water, percentage area water, presence of natural feature, lack of management, management intensity (type and frequency), naturalism index, degree of wilderness. Ephemera: Concept: we define ephemera as elements and land-cover types changing with season and weather. Dimensions: seasonal change (human imposed and natural), weather related changes. Landscape attributes: land cover/vegetation, animals, land use (ploughing, etc.), water (color re ections and waves), weather. Potential indicators: percentage of land cover with seasonal change, presence of animals, presence of cyclical farming activities, percentage area water, projected and re ected images, presence of weather characteristics.

2.11

The Psychological Landscape

We have discussed, in some length, perception. Perception is a profound component interlaced with psychological processes that shape the internal and external worlds of every organism with a mind. The term psychological landscape is intended to describe how humans and many other animals react to visual cues submitted as caliber images. This is a new field of investigation, based on visual cues in which human decisions (behavior) are analyzed with a specific reference to psychological drivers (Kaplan & Kaplan, 1989). Environmental attitude and ecological behavior are two

2.13

The Thermalscape

73

components of the human decision (Kaiser et al., 1999) and ontogenesis (Kytta, 2002). The analysis of the psychological reactions can improve the knowledge of landscape evaluation and the successive actions (e.g., planning, conservation) (Bell, 2001).

2.12

Mystery in Landscape

Mystery is defined as the degree to which you can gain more information by proceeding further into the scene (Kaplan & Kaplan, 1982). Mystery is one of the perceived attributes from human-centered vision of landscape. The perception of mystery decreases with perceived distance. The perception of mystery declines correspondingly with perceived screening. The perception of mystery increases with the increase of spatial definition and the perceived physical access (Lynch & Gimblett, 1992). Mystery represents an important attribute to a landscape scene, and the more a landscape is valuable, the more mystery is inside the scene, although not all the landscape components like the visual effect of plant foliation may be a contributing factor (Kuper, 2013, 2015). The mystery in landscape is not a human-perceived process, but this paradigm can be extended to several nonhuman animals. It is not a coincidence if open parklands or savannah are the areas at higher concentration of large mammals that entrust their survival mainly to sight and that the highest diversity of frogs is in the interior of tropical forests where the light is strongly reduced and where their social contacts are entrusted to acoustic cues (Ghose et al., 2017).

2.13

The Thermalscape

The distribution of thermal gradients in time and space across a landscape contributes to the overall heterogeneity of the ecological systems. The term “thermalscape” has been recently introduced in the field of urban quality assessment and design (Hai & Feng, 2018; La Malva et al., 2015). Yet, the global warming caused by humaninduced climate change has put the thermal-scape at the center of scientific, cultural, social, economic, and political interests all over the world. This is especially concerning for its tremendous impact to many species whose life histories are directly and/or indirectly impacted by temperature (Sears et al., 2016). Temperature is a fundamental and innate ecological component for life on Earth. Many species can be classified according to their capacity to live in a broad or close range of thermal conditions (thermal niche) (Gvoždík, 2018). Species that are only capable of living in a narrow thermal range are called stenotherms. Stenotherms are considered climatically sensitive. Conversely, there are species that have the capacity to live across a broad range of thermal conditions. These species are called

74

2 Ecoscape vs. Landscape: Riding a Transition

eurytherms. Eurytherms are much more adaptable to a variety of climate conditions and can inhabit a broad range of habitats. Humans are perfect example of eurytherm species although humans adopt technological strategies to compensate extreme climate. Species that do not independently regulate their internal body temperature via metabolism, (e.g., ectotherms) must cope with thermal surfaces and thermal variations throughout the day and seasons to absorb the necessary energy for metabolic functioning. Butter ies, lizards, snakes, and turtles, for example, utilize the warm rays of sunlight in the morning on exposed surfaces to heat their bodies in order to engage in their daily movements. This behavior is called sunning or basking and is an effective behavior for thermoregulation. Because of this physiological constraint to thermal conditions, ectotherms are distributed to specific areas across the globe and, more specifically, within landscapes. Species that thermoregulate internally through metabolic processes (Endotherms, e.g., mammals and birds) are much more adaptable to a variety of thermal conditions and thus have a broader global distribution and larger range of habitats within landscapes. Comparably, ectotherms are constrained in their distribution by thermal conditions to a greater extent than endotherms. Heat is distributed in space and time according to the thermal conductivity of the substrate. For instance, black bodies absorb thermal radiations; white surfaces are heat “repellent.” Vegetation favors the dissipation of heat by evapotranspiration. If the heat across the land and inside the water bodies is not constant in time and space, we can imagine that organisms can cope the patchy distribution of heat waves, recognizing a mosaic of thermal patches. As consequence, we can admit, like for soundscape or lightscape, a mosaic of thermal patches can contribute to create habitat suitability for species. In the urban areas, the heat distribution re ects the architecture of buildings and infrastructure, the distribution of greeneries, the distribution of plants along road, the effects of air conditioners, and urban traffic. For instance, in desert areas of northwest United States, cities like Las Vegas or Phoenix have a microclimate milder than the surroundings due to the presence of a great amount of green spaces (parks, owerbed, golf course, etc.). Thermalscape can be modified directly by climate regime and indirectly by the effect of invasive plant species with deep effects on ectothermic species (see Garcia & Clusella-Trullas, 2019, for a review). The majority of tropical ectothermic species are living near their physiological limits, and climate changes with consequence increase of heat can produce serious effects on tropical ectotherms. A recent investigation conducted in the in French Guyana has demonstrated a great thermal heterogeneity from the scale of individual leaves until the scale of an entire landscape. This heterogeneity may be important for the majority of small ectotherms that have the capacity to make movements inside their habitats (Pincebourde & Suppo, 2016). The importance of thermal wind and food at wintertime has been recognized in Oriental Honey Buzzards in the Flores Island. This species spends wintertime mainly in core areas characterized by stronger thermal winds and higher food quality (Syartinilia et al., 2017). Thermal wind velocity is an important factor for the

2.13

The Thermalscape

75

movement of soaring species like the California condor (Gymnogyps californianus) (Rivers et al., 2014). The thermal niche in 33 amphibians and reptiles of southern Costa Rica has demonstrated importance in the survival of species from deforestation. The warm-adapted species have a significant advantage despite land-use conversion and climate change (Frishkoff et al., 2015). The concept of thermal refuge is central in conservation strategies. For instance, bunch grasses in semi-arid regions are fundamental to assure necessary condition for the survival to bobwhite (Colinus virginianus) reducing thermal stress (Tomecek et al., 2017). The fine-scale thermal regime in forest montane bird communities seems to play a relevant role in species distribution. Birds can vacate and settle sites with particular microclimatic conditions (Frey et al., 2016). Fine-scale temperature distribution is a strong predictor of bird distribution, and its effect is 1–1.7 times more than the vegetation effects. Frey et al. (2016) have found that at least 53.3% of species prefer warmer site, and 53.3% of species prefer cooler sites, demonstrating that microclimate is species specific (Fig. 2.12). The microclimatic heterogeneity that we expect to be maintained under a climatic change scenario could be the buffer in montane environment.

Fig. 2.12 Pacific wren (Troglodytes pacificus) is a species that prefers warm sites (a) with old growth vegetation (b) characterized by higher deciduous composition (c). This species avoids evenaged vegetation, such as plantations (d) and vacates cooler sites (e). (From Frey et al., 2016, with permission)

76

2 Ecoscape vs. Landscape: Riding a Transition

The thermal environment is heterogeneous, and this offers advantages to species like ground-dwelling birds like the northern bobwhite (Colinus virginianus) (Carroll et al., 2016a, b). Thermal quality is fundamental for ectothermic species. Thompson et al. (2018) have presented evidences that land cover change, deforestation, and secondary succession create new opportunities for two species of anole species (Norops humilis and N. limifrons) from Costa Rica, where pastures offer the lowest level of thermal quality (Fig. 2.13). Thermalscape can be altered by secondary succession but also by severe wildfire that can destroy refuges, resources, and microclimate. Wildfire, when severe, can create new conditions dominated for many years by a scorched landscape. Some mammals, like a small insectivorous marsupial, the yellow-footed antechinus (Antechinus flavipes), to survive use a combination of torpor and basking (sunning) to compensate food shortage and taking advantage of an increased solar radiation at ground level. Torpor is a physiological mechanism to reduce energy expenditure, and basking is a strategy, usually utilized by ectotherms, to store solar energy. Movement strategies are usually associated with antipredatory and foraging behavior. Thermal stress especially in large mammals like savanna elephants (Loxodonta africana) is an important factor that drives their behavior. In the tracking of 14 herds of elephants in Kruger National Park (KNP, South Africa) with a GPS collar with inbuilt temperature sensor, Thaker et al. (2019) have observed a complex

Fig. 2.13 Thermal preferences of two anolis (Norops humilis and N. limifrons) from Costa Rica, where de is the mean deviations between Te (operative temperature) and Tsel (selected temperature). These results show evidence of the low value of thermal quality of pasture land cover. (From Thompson et al., 2018, with permission)

2.14

The Odorscape

77

behavior of these animals according to the season and the climatic conditions. For instance, elephants moving in dense wooded areas moved slower, but they moved faster at higher temperature in wet season more than in dry season. The speed resulted higher moving close the water sources. In dry seasons were moving farther and changing water sources than in wet season. The typology of land cover has been found a key factor for buffering drought and high-heat events for ground-dwelling birds (Carroll et al., 2016a, b). The thermalscape plays a fundamental role to mitigate the effects of climatic change. Betts et al. (2017) have found that the proportion of old growth forest in the Pacific Northwestern United States contributes to buffering the climatic changes for bird populations sensitive to climatic warming.

2.14

The Odorscape

The odorscape is the result of spatial distribution of chemical molecules that are used by animals to navigate, to discover food resources, to intercept partners, to avoid predators, and to communicate. Odor consists of a specific mixture of green leaf volatiles (GLVs) and volatile organic compounds (VOCs) emitted by animals, rotting fruits, and excretion organs. The odorscape is a blend of many compounds associated to minor compounds and varies according to vegetation composition, animal communities, and daily and seasonal scales. Odorous traces may be persistent signals deployed by many terrestrial, aquatic, and aerial animals with a variety of goals. In addition, they include plant scents and other chemical processes like organic dead material decomposition and gas emissions from underground volcanic sources. Vegetation produces an amazing quantity of volatile compounds that have an impact on the sensorial capacities of animals, mainly insects. Trees, grasses, shrubs, and their parts (leaves, owers, fruits, roots, etc.) release a great variety of odors contributing to the prevalence of environment odorscape. VPCs have feedback effects on how plants grow and on the quality of air. Plants produce VPC even when stressed or when they are being predated. The odor-scape is of great importance for macrosmotic organisms like some ungulates, their predators, as well as many insects. The landscape is full of odors, but the majority of these are not perceived and processed by humans because they are a microsmatic primate. A complex structure of neuroreceptors is at the basis of the extraordinary odordetection capacity of several animals. Insects, for instance, track the odor molecules and decodify their meaning using specialized organs (Bohbot & Pitts, 2015). Their ability to efficiently use odors for their movements and life history stages make them candidates for exploring the VOC library various blends of scents derived from the landscape (Fig. 2.14). In Lepidoptera, the use of pheromones for attracting partners

78

2 Ecoscape vs. Landscape: Riding a Transition

Fig. 2.14 Mosquitos have odorant receptors (ORs) able to discriminate different sources of volatile organic compound (VOCs) in which to operate an olfactory discrimination. (From Bohbot & Pitts, 2015, with permission)

is very common. These insects emit pheromones in extremely small quantities and can easily elicit a behavior from another individual at relatively great distances. This mechanism is particularly efficient in species like the moth family, Saturniidae, and Bombycidae. Just a few molecules impinging on the antenna of a male are sufficient to modify their cardiac frequency (Celani et al., 2014). Insect olfactory capacity is very high, and this group uses the olfactory information of the environment to perform many functions. These animals have an olfactory apparatus, mainly on antenna and pulps, that is, olfactory receptor ORs and olfactory receptor neurons (ORNs). The richness of OR varies from 10 ORs in human body louse (Pediculus humanus) to 350 is some species of ants. The capacity of ORs is reduced by the dilution of olfactory plume. In insect the odor navigation is complicated by the complex physical behavior of plumes caused by air turbulence. Pheromones produced by insects have a plume shaped by wind direction to create patterns of scent concentration under the air movements of advection, convection, and turbulence. An odor plume although in its short life, before the complete dissolution, is structured in blank, clump, and whiffs. The odorscape is under continuous changes due the variety of changes in organic and nonorganic materials in uenced by water, light, weather, etc. This sensoryscape appears heterogeneous, highly dynamic, and extremely sensitive to every modification of land-cover, climatic, and human intrusion by chemical pollution. Since VOCs emitted by vegetation are extremely important for insects, the use of pesticide and airborne fertilizers has a great impact on the insects in agroecosystems that have altered and confounded their smelling maps.

2.14

The Odorscape

79

Human activity produces several types of anthropogenic volatile pollutants (AVPs) that create human-generated VOCs, as well as reactive oxygen substances (ROSs) that can interfere with plant-animal communication, disturbing or disrupting mutualistic interactions. The odor-scape is important because it is one of the more in uential semiotic variables that mediate the relationship between plants and animals (e.g., pollinators). This link has become substantial to assure the resilience of entire ecosystems over many parts of the Earth. For example, the plant-pollinator interactions, often in synergy with visual cues (visionscape), are strategic to assure the reproductive dynamics on both the sides that ultimately sustain a variety of life, including human agriculture. Unfortunately, the odorscape is not easy to be represented on maps, and its specificity according to species makes it impossible for visualization. The introduction of anthropogenic volatile compounds into the landscape from traffic, industrial waste, or non-natural biogenic compounds has had different ecological effects than the volatile compounds that have been introduced from agriculture (Fig. 2.15). For instance, anthropogenic volatile compounds can produce: 1. Changes in plant signaling in response to physiological stress. 2. Chemical interference that confounds the signaling processes. 3. Changes in the way pollinators perceive plant signals. The level of air pollution can reduce plant-pollinator communication signals. This can have cascading effects resulting in changes in plant community composition (Jürgens & Bischoff, 2017). Olfactory cues in the environment have been known to improve animals’ recognition and response to predation risk in the landscape, which has been observed in wood frog tadpoles (Lithobates sylvatica) (Mitchell et al., 2018). Changes in the odorscape can elicit specific behavioral responses of prey-species to predators. For instance, McCornick and Allan (2017) found that when three different species of coral reef fishes were experimentally immersed in water from degraded coral, all three species modified their fast-start escape behavior. What’s notable from this experiment is that each species has a different response to the chemistry of the degraded water. These findings can make a difference in a future scenario of coral reef degradation (McCornick & Allan, 2017). The odorscape is not composed by a single substance but by a mixture of many compounds that the species must discriminate to extract the relevant information from this chemical mosaic. Animals probably receive a great number of stimuli from which they must extract regularities. The saturation of chemoreceptors could be important in the discrimination process (Bos et al., 2013). Although the odorscape is patchily distributed and ephemeral, it is important in the navigation of many animals that use special organs (sensory neurons of the antennae of invertebrates and nasal cavity in vertebrates) to detect its chemical signals. Atlantic and Pacific Salmons are impressive examples on the use of olfactory cues for navigation. These fishes recognize their natal stream at distances up to 1000 km. Similarly, procellariiformes find their nesting hole in islands after

80

2 Ecoscape vs. Landscape: Riding a Transition

Fig. 2.15 Urban odorscape hierarchical categories. The top categories are in the inner ring, followed by a second-level categories, and finally in the third circle are examples of words. (From Quercia et al., 2015, with permission)

travelling thousands of kilometers of ocean. Because odorous traces can sometimes disperse long distances, with no certain directionality, animals have evolved efficient methods to intercept the ephemeral traces of scent. The odorscape is species specific. We can image hotspots of scent and paths of scents but also clouds of scents. All species has a specific scent that can be enhanced by the emission of pheromones. No vertebrate or invertebrate species escape from the odor mechanisms in intra- and interspecific communications. In reptiles, conspecific male-male agonistic interactions, sex recognition, and reptilian predator recognition are the main functions in which the role of odors has been documented (Mason & Parker, 2010). For instance, in wall lizards (Podarcis muralis), chemical and visual cues are combined to discover saurophagus and non-saurophagus snakes (Amo et al., 2005, 2006) inside the wall refuges, and this

2.14

The Odorscape

81

reduce the exposition of lizards to external predators as well. The refuge used to escape predators is more important than the thermalscape of the refuges for lizards (Amo et al., 2004). Predator odors are important cues eliciting fear behavior in prey. In birds, the capacity to avoid predator by using scent has been experimentally demonstrated by Amo et al. (2008) in breeding blue tits (Cyanistes caeruleus) (Fig. 2.16). When nest boxes were sprinkled with the scent of a mustelid, blue tits refused to enter in their nest box and reduced the time they spent feeding nestlings. A similar behavior has been observed also in great tits (Parus major) (Amo et al., 2011; Ekner & Tryjanowski, 2008). An interesting odorscape-related study on response of non-native European rabbits (Oryctolagus cuniculus) to native predators in Australia was conducted by Mella et al. (2016). Rabbits reacted to the scent of “historical predators” (cat and fox) but also to northern quoll (Dasyurus hallucatus) but not to black-head python (Aspidites melanocephalus). This species seems to have a genetic memory of larger groups of predators that has been fixed in the genes during the coevolution with predators (Mella et al., 2016). The odorscape can modulate complex behaviors (Bidlingmayer, 1994), like those of mosquitos (Aedes Aegypti) (van Breugel et al., 2015). In an experiment, mosquitos were placed in a special tunnel with black objects and CO2 sensors. Scientists were able to observe female mosquitos tracking plumes of CO2 and contemporarily searching from black silhouettes located in the oor of the chamber. However, mosquitoes did not elicit the same response when they were exposed to air without CO2. The scientists concluded that the visual inspection of black objects was elicited by the administration of CO2. This is an important example of integration between chemical and visual stimuli. According to the eco-field model, the location of the resource (human blood) is obtained either by a chemical template (CO2) or successively by a visual template (black features) and finally by a thermal template in the selection of the part of human body to be selected for blood sucking. The manipulation of the odorscape of herbivorous pest species may represent an efficient strategy to protect plants in substitution of pesticides. For instance, introducing plants with a different volatile plant compound could reduce the damage by pest insects. Odorscape is of primarily importance for the ecosystem functioning, and information on the impact of atmospheric pollution and change in climate is very important to preserve ecosystem functioning. In dead coral leaf, the change in odorscape obliges some species to extinction, but other can maintain their capacity to predate then to survive (Natt et al., 2017). Some seabirds like penguins seem to have a strong capacity to recognize sources of food (Cunningham & Bonadonna, 2015). It is well-known the capacity to use olfactory capacity by vulture, procellariiformes, and kiwi, but many other birds have olfactory capacities. In zebra finch (Taeniopygia guttata), edglings seem to return at the nest located in a dense concentration of other conspecific nests by using the natal nest odor (Caspers & Krause, 2001). Although actually under investigation,

82 Fig. 2.16 Modification of the behavior of blue tit (Cyanistes caeruleus) when the next box with chicken was sprinkled with the scent of a predator (mustelid), quail (non-predator), and water (neutral). It is evident the negative effect of the predator scent. (From Amo et al., 2008, with permission)

2 Ecoscape vs. Landscape: Riding a Transition

2.14

The Odorscape

83

pheromones in birds seem to exist although not too easy to be demonstrated (Caro & Balthazart, 2010). In Antarctic prions (Pachyptila desolata), it was experimentally demonstrated that these birds recognize individual odors (Bonadonna et al., 2009). Evidences of odorous capacity in discriminating their nest at night were found in petrels nesting in burrows but not in petrel that brood in open nests and that have diurnal activity (Bonadonna & Bretagnolle, 2002) (Fig. 2.17). In blue tits (Cyanistes caeruleus) was found the habit to adorn the nest with fragments of aromatic plants and that this habit is maintained during the breeding period with the evident goal of masking the brood scent against predators (Petit et al., 2002). The antipredator function of the odorscape has been proved in mammals like deer and wild boar at were been administrated wolf scant (Kuijper et al., 2014) (Fig. 2.18).

Fig. 2.17 Example of feeding root followed by a GPS tracked albatross. In circles feeding events. In A and B, the amount direct and zig-zag y. (From Nevitt et al., 2008, with permission)

84

2 Ecoscape vs. Landscape: Riding a Transition

Fig. 2.18 Wolf scat modifies the behavior of red deer and wild boar especially in vigilance and sniffing. (From Kuijper et al., 2014, with permission)

The odorscape has been proved as one of the mechanisms utilized by some birds like domestic pigeons in their homing performances although it is not clear what component of the scent blend is used (Papi et al., 1972). Olfactory navigation might be more important and diffuse in birds (Gagliardo, 2013; Wallraff, 2004) although other functions like breeding can utilize olfactory cues (Balthazart & Taziaux, 2009). Despite the popular belief that humans have a limited olfactory sense, experimental evidences have proved that humans can discriminate at least 1.72  1012 olfactory stimuli thanks to the presence of hundreds of olfactory receptors (Bushdid et al., 2014). The importance of olfactory perception has been experimentally tested on a sample of people at which were administrated images of forest combined with olfactory stimuli. The images of forest elicited a sense of relaxation revealed by the decrease of oxyhemoglobin concentration in the right prefrontal cortex especially when visual and olfactory information where combined (Song et al., 2019).

2.15

2.15

The Touchscape

85

The Touchscape

The sense of touch, although well developed and diffuse among animals, is not well studied and evaluated. The touchscape can be defined as the entire set of objects that, for their intrinsic qualities, return information to specific organs embedded into skin, fur, arthropod cuticle, or other specific sensorial organs sensitive to pressure. This scape is also species specific and furtherly distinct according to the size of organisms. The touchscape is the result of perturbation of the outer membrane in a ciliate (Paramecium) or of the de ection of nonmotile cilia in sponges. Cnidarians, such as jellyfish, have motor responses to tactile stimuli. The sense of touch may have an interpersonal behavioral role to consolidate the relationship between partners or members of a group. Tactile sensitivity is not restricted to animals but is extended also to plants that modify their shape or use special organs like tendrils to anchor to other plants or to physical supports. The sense of touch is well developed in snakes that use the entire body to move on the ground or on the vegetation. On the body surface, there are several tactile receptors that help snakes to move and to explore the environment. In lower invertebrate, the touch sense is well developed like in leech (Hirudo medicinalis) or in molluscs like in octopus. In this group of animals, the touch sensory is translated into a cognitive condition (Young, 1983). In arthropods, there are tactile hairs that are not like mammal hair. These hairs or setae provide the touch information by the presence of mechanoreceptors. Similar role is played by arthropod antenna that in insects is also an organ of olfaction. Fish and amphibians have the lateral line systems that is used as mechanoreceptors and known as neuromasts. These organs respond to the hydrodynamic stimuli of water ow and pressure. A remnant of this lateral line is present in mammals by the liquid-filled (semicircular) canals of the inner ear that represents an organ that informs organisms about the rotational movement of the head. In mammalians the hair represents an important touch sensory structure. Only successively hairs became structure to isolate the body and to maintain constant the interior temperature. In mammals vibrissae are specialized hair to tactile function. In naked mole rat (Heterocephalus), the body is without hairs, but the tactile proprioceptors have been maintained. In all the mammals, whiskers particularly developed on the head are specialized in tactile exploration. The sense of touch resulted particularly developed in some birds for prey detection. Species that search food into the soil or in turbid waters use a remote-touch probe. It is the case of kiwi, ibis, and Scolopacidae that use the neck as probe in foraging (Cunningham et al., 2007). Landscape modification often alters the touch-sensory system of animals, and often we have no idea on the effects of such alteration. For instance, in humans, a cornfield returns a sensoryscape that is expected to be different from natural

86

2 Ecoscape vs. Landscape: Riding a Transition

grassland. For this we have no idea about the level of habitat hostility perceived by different species when come in contact with new habitats. The most endangered touchscape remains the soil after the agriculture manipulation by deep and frequent plowings that especially in tropical and subtropical regions produce a dramatic reduction of soil fertility and extinction of soil fauna. The utilization of a conservative tillage practice seems a good solution to this dramatic impoverishment. However, if we consider the soil as the physical environment which soil organisms are literally immersed, the structure of soil is fundamental for the correct maintenance of their tactile sensorship. Due to the high specificity of the touch sensorship of animals when compared with people and considering the scarce capacity of people to utilize this sensorship, both conditions have maintained the touchscape at the margin of the scientific interest.

2.16

The Soundscape

2.16.1 Introduction Sound is defined as a mechanical vibration transmitted through an elastic medium. It travels long distances in the air, penetrating vegetation and water layers and maintaining some parts of the associated information. It propagates in the air at 331 m/s at 0
C and five times faster (1484 m/s) in water. Sound is modified by diffraction, reverberation, and absorbent effects, and its perceived quality depends on the position and distance a receiver is located compared with the position of sources. When a vibration is emitted through a medium (air or water), the vibration is transmitted where it can be received by specialized organs for hearing and thus producing the mental sensation we call sound. Sound is usually measured as a function of amplitude and frequency. Amplitude refers to how loud a sound is. Amplitude is often measured in logarithmic units, called decibels (dB), that indicate the physical quantity (usually power or intensity) relative to a pre-assigned reference level. Decibels are measured in reference to humans’ ability to detect sound. Therefore, the lowest detectible sound a human can hear is 0 dB, while the loudest sound that results in hearing loss is 120 dB when exposed for 1012 m2 covering the continents and interacting geological events, such as plate tectonics.

4.11

Examples of Scales in Landscape and in Ecologically Related Disciplines

169

4.11.2 Scaling Patterns: The Catchment Scale The catchment scale that pertains to terrestrial and aquatic ecosystems, recently reviewed by Hornung and Reynolds (1995), seems to be a very promising dimension to investigate the uctuations in water and elements that link the different components of terrestrial–aquatic systems (including riparian boundaries). Disturbance regimes such as agricultural intensification, afforestation, and fires can be monitored using the chemical composition of streams and underground and surficial waters. Small catchments can be appropriate components to study pollution, land management activity, and environmental changes. The possibility to increase spatial and temporal resolution is a further possibility to investigate in greater depth. However, McGlynn et al. (2003) argued that the landscape organization more than the total area affects catchment runoff characteristics.

4.11.3 Scaling Abiotic Processes: Hydrological Processes and Scales Hydrological processes are the principal phenomena of landscape mechanisms (van der Meij et al., 2018). After all, life on Earth would not exist without water and its dynamic in uences on living and non-living things. There are generally two levels of temporal scale used in the study of hydrological processes: 1. duration of a hydrological process (e.g., intermittent process, such as a ood); 2. period or cycle of a process (e.g., period where waterfalls as precipitation, turns to snow, and then melts). In hydrological studies, the spatial-scale hydrological subjects range from 1 m (local scale) to hill slope (reach) (100 m), to catchment scale (10 km), and to regional scale (1000 km). We have almost three temporal levels: the event scale (1 day), the seasonal scale (1 year), and long-term scale (100 years). They range in eight orders of magnitude in space and time occurring at a wide range of scales from unsaturated ow 1 m soil profile to oods in river systems of a million square kilometers, from ash oods lasting some minutes to ow in aquifers over hundreds of years (see Bloschl & Sivapalan, 1995 for a review) (Fig. 4.4).

170

4 Scaling Patterns and Processes Across Landscapes

Fig. 4.4 Component of catchment and hydrological processes across spatial and temporal scales. (From Bloschl & Sivapalan, 1995, with permission)

4.11.4 Scaling Evidences in Animals Every species perceives the surrounding environment (landscape) in a different way according to a so-called allometric correlation (Pritchard, 1993; Porter et al., 2002; Tamburello et al., 2015). The movement of a grass stem appears paroxysmal for aphides but is not perceived at all by a deer. What may appear to be a homogenous landscape by water pipit (Anthus spinoletta (Motacillidae, Aves)), like a mountain prairie, is perceived to be heterogeneous for Erebia (sp.) butter y (Satyridae, Lepidoptera) because the butter y distinguishes differences in food availability across prairie patches. Unfortunately, we often select scales more comfortable to our metrics as humans than to the perspective of a species (Dale et al., 1989; Wiens & Milne, 1989; Wiens, 1992). However, it is possible for humans, through our investigations, to judge the best range of scale at which an organism spends its life (territorial behavior, dispersal movements, food research) (O’Neill et al., 1988). This is the ultimate endeavor for all wildlife biologists. As such, an animal behavior is a response to their interaction with the patterns and processes of their environment that occurs across several spatiotemporal scales (Gardner et al., 1989).

4.11

Examples of Scales in Landscape and in Ecologically Related Disciplines

171

Dorcas gazelles (Gazella dorcas) foraging in the Negev desert (Ward & Saltz, 1994) where they select a plot with a high density of preferred forage, madonna lily (Pancratium sickenbergeri). In this circumstance, the plot presents a small-scale decision for occupancy. Gazelles generally stay longer in these plots compared to plots selected at random. But at broad scale, gazelles move directly from one Madonna lily plot to another, suggesting that this species samples the environment repeatedly during their movement patterns. Wild boar (Sus scrofa) creates rooted disturbed surfaces to search for food. This activity that creates so big problems to cultivated fields worldwide has been found by Welander (2000) scaled according to a temporal and spatial scale. The size of disturbed surfaces varied between 2.4 and 14.2 ha on 226 ha investigated. The largest patches of disturbed soil have been found in damp soils and smallest in dry soil. According to the habitat, the largest patches were found in deciduous forests, the medium size in coniferous forests, and the smallest in grasslands. Many organisms are selected as bioindicators. They must have the requisite to be independent to the chosen scale. To test this hypothesis, Weaver (1995) studied an arthropod community across a range of four hierarchical spatial scales, each scale nested in the others. He found that the proportion of species in a sample depended on the scale of observation. The abundance for two species showed declines when the scale of sampling increased suggesting scale-specific distribution at smaller scales. Conversely, the diversity of four other species increased with each new sample site, suggesting species diversity and abundance were more apparent at larger scales. These data are relevant in a perspective of monitoring biodiversity according to the scale of investigation. If species richness and abundance are a matter of spatial scale, then monitoring efficiency is improved when species-scale dependency is understood (Noon et al., 2012). For instance, Roshier et al. (2001) have found that the driest parts of Australia have the highest richness of water birds (Fig. 4.5). They argued that in respect to the human-scale perception of local water availability, birds that have a wider capacity to disperse perceive more connected and suitable habitats that are apparently isolated.

172

4 Scaling Patterns and Processes Across Landscapes

Fig. 4.5 Mean habitat availability (gray) for the period 1986–1997 at scales of (a) 100 km, (b) 200 km, and (c) 500 km of water resources across Australia. (From Roshier et al., 2001)

References Allen, T. F. H., & Starr, T. B. (1982). Hierarchy. In Perspectives for ecological complexity. University of Chicago Press. Angelstam, P., Hansson, L., & Pehrsson, S. (1987). Distribution borders of field mice Apodemus: The importance of seed abundance and landscape composition. Oikos, 50, 123–130. Austrheim, G., & Eriksson, O. (2001). Plant species diversity and grazing in the Scandinavian mountains-patterns and processes at different spatial scales. Ecography, 24(6), 683–695.

References

173

Benítez-López, A., Vinuela, J., Mougeot, F., & Garcia, J. T. (2017). A multi-scale approach for identifying conservation needs of two threatened sympatric steppe birds. Biodiversity and Conservation, 26(1), 63–83. Bloschl, G., & Sivapalan, M. (1995). Scale issues in hydrological modelling: A review. Hydrological Processes, 9, 251–290. Bock, C. E. (1987). Distribution-abundance relationship of some Arizona landbirds: A matter of scale? Ecology, 68, 124–129. Boyce, M. S., Mallory, C. D., Morehouse, A. T., Prokopenko, C. M., Scrafford, M. A., & Warbington, C. H. (2017). Defining landscapes and scales to model landscape–organism interactions. Current Landscape Ecology Reports, 2(4), 89–95. Cain, S. A. (1943). Sample plot technique applied to Alpine vegetation in Wyoming. American Journal of Botany, 30, 240–247. Cain, S. A., & Castro, G. M. (1959). Manual of vegetation analysis. Harper. Carlile, D. W., Skalki, J. R., Batker, J. E., Thomas, J. M., & Cullinan, V. I. (1989). Determination of ecological scale. Landscape Ecology, 2, 203–213. Chust, G., Pretus, J. L., Ducrot, D., & Ventura, D. (2004). Scale dependency of insect assemblages in response to landscape pattern. Landscape Ecology, 19, 41–57. Dale, V., Gardner, R. H., & Turner, M. G. (1989). Predicting across scales: Theory development and testing. Landscape Ecology, 3, 147–252. Dayton, P. K., & Tegner, M. J. (1984). The importance of scale in community ecology: A kelp forest example with terrestrial analogs. In P. W. Price, C. N. Slobodchinof, & W. S. Gaud (Eds.), A new ecology (pp. 457–481). Wiley. De Casas, R. R., Donohue, K., Venable, D. L., & Cheptou, P. O. (2015). Gene- ow through space and time: Dispersal, dormancy and adaptation to changing environments. Evolutionary Ecology, 29(6), 813–831. Debinski, D. M., Ray, C., & Saveraid, E. H. (2001). Species diversity and the scale of the landscape mosaic: Do scales of movement and patch size affect diversity? Biological Conservation, 98, 179–190. Delcourt, H. R., & Delcourt, P. A. (1988). Quaternary landscape ecology: Relevant scales in space and time. Landscape Ecology, 2, 23–44. Ducklow, H. W., Schofield, O., Vernet, M., Stammerjohn, S., & Erickson, M. (2012). Multiscale control of bacterial production by phytoplankton dynamics and sea ice along the western Antarctic Peninsula: A regional and decadal investigation. Journal of Marine Systems, 98, 26–39. Ernoult, A., Bureau, F., & Poudevigne, I. (2003). Patterns of organisation in changing landscape: Implications for the management of biodiversity. Landscape Ecology, 18, 239–251. Farina, A. (1996). Diversity and structure of bird assemblages in upland pastures facing land abandonment. In Proceedings of Fifth European Forum on Nature Conservation and Pastoralism, Cogne 17–21 September 1996. Gage, S. H., Isard, S. A., & Colunga, G. M. (1999). Ecological scaling of aerobiological dispersal processes. Agricultural and Forest Meteorology, 97, 249–261. Gardner, R. H., O’Neill, R. V., Turner, M. G., & Dale, V. H. (1989). Quantifying scale dependent effects of animal movement with simple percolation models. Landscape Ecology, 3, 217–227. Gehring, T. M., & Swihart, R. K. (2003). Body size, niche breadth, and ecologically scaled responses to habitat fragmentation: Mammalian predators in an agricultural landscape. Biological Conservation, 109, 283–295. Greig-Smith, P. (1964). Quantitative plant ecology (2nd ed.). Butterworths. Hall, O., Hay, G. J., Bouchard, A., & Marceau, D. J. (2004). Detecting dominant landscape objects through multiple scales: An integration of object-specific methods and watershed segmentation. Landscape Ecology, 19, 59–76. Horne, J. K., & Schneider, D. C. (1995). Spatial variance in ecology. Oikos, 74, 18–26. Hornung, M., & Reynolds, B. (1995). The effects of natural and anthropogenic environmental changes on ecosystem processes at the catchment scale. TREE, 10, 443–448.

174

4 Scaling Patterns and Processes Across Landscapes

Howell, C. A., Latta, S. C., Donovan, T. M., Porneluzi, P. A., Parks, G. R., & Faaborg, J. (2000). Landscape effects mediate breeding bird abundance in midwestern forests. Landscape Ecology, 15, 547–562. Knopf, F. L., & Samson, F. B. (1994). Scale perspective on avian diversity in Western riparian systems. Conservation Biology, 8, 669–676. Kolasa, J., & Rollo, C. D. (1991). Introduction: The heterogeneity of heterogeneity: A glossary. In Ecological heterogeneity (pp. 1–23). Springer. Krummel, J. R., Gardner, R. H., Sugihara, G., O’Neill, R. V., & Coleman, P. R. (1987). Landscape patterns in a disturbed environment. Oikos, 48, 321–324. Kunin, W. (1998). Extrapolating species abundance across spatial scales. Science, 281, 1513–1515. Lawrence, R. L., & Ripple, W. J. (2000). Fifteen years of revegetation of Mount St. Helens: A landscape-scale analysis. Ecology, 81, 2742–2752. Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology, 73, 1943–1967. Lubchenco, J., Olson, A. M., Brudbaker, L. B., Carpenter, S. R., Holland, M. M., Hubbell, S. P., Levin, S. A., MacMahon, J. A., Matson, P. A., Melillo, J. M., Mooney, H. A., Peterson, C. H., Pulliam, H. R., Real, L. A., Regal, P. J., & Risser, P. G. (1991). The sustainable biosphere initiative: An ecological research agenda. Ecology, 72, 371–412. MacArthur, R. H., & Wilson, E. O. (1967). The theory of island biogeography. Princeton University Press. Magnuson, J. J. (1990). Long-term ecological research and the invisible present. Bioscience, 40, 495–501. Maguire, D. A. (1985). The effect of sampling scale on the detection of interspecific patterns in a hemlock-hardwood forest herb stratum. American Midland Naturalist, 113, 138–145. Marceau, D. J., & Hay, G. J. (1999). Remote sensing contributions to the scale issue. Canadian Journal of Remote Sensing, 25(4), 357–366. Maurer, B. A. (1985). Avian community dynamics in desert grassland: Observational scale and hierarchical structure. Ecological Monographs, 55, 295–312. McGarigal, K., Wan, H. Y., Zeller, K. A., Timm, B. C., & Cushman, S. A. (2016). Multi-scale habitat selection modeling: A review and outlook. Landscape Ecology, 31(6), 1161–1175. McGlinn, D. J., Xiao, X., May, F., Gotelli, N. J., Engel, T., Blowes, S. A., . . . McGill, B. J. (2019). Measurement of Biodiversity (MoB): A method to separate the scale-dependent effects of species abundance distribution, density, and aggregation on diversity change. Methods in Ecology and Evolution, 10(2), 258–269. McGlynn, B., McDonnell, J., Stewart, M., & Seibert, J. (2003). On the relationships between catchment scale and streamwater mean residence time. Hydrological Processes, 17(1), 175–181. McManus, M. A., & Woodson, C. B. (2012). Plankton distribution and ocean dispersal. Journal of Experimental Biology, 215(6), 1008–1016. Meentemeyer, V., & Box, E. O. (1987). Scale effects in landscape studies. In M. G. Turner (Ed.), Landscape heterogeneity and disturbance (pp. 15–36). Springer. Meyer, C. B., Miller, S. L., & Ralph, C. J. (2002). Multi-scale landscape and seascape patterns associated with marbled murrelet nesting areas on the U.S. west coast. Landscape Ecology, 17, 95–115. Morris, D. W. (1987). Ecological scale and habitat use. Ecology, 68, 362–369. Mueller-Dumbois, D., & Ellemberg, H. (1974). Aims and methods of vegetation ecology. Wiley. Nieto-Lugilde, D., Lenoir, J., Abdulhak, S., Aeschimann, D., Dullinger, S., Gégout, J. C., . . . Svenning, J. C. (2015). Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the Alps. Ecography, 38(6), 578–589. Noon, B. R., Bailey, L. L., Sisk, T. D., & McKelvey, K. S. (2012). Efficient species-level monitoring at the landscape scale. Conservation Biology, 26(3), 432–441. O’Neill, R. V., Milne, B. T., Turner, M. G., & Gardner, R. H. (1988). Resource utilization scales and landscape pattern. Landscape Ecology, 2, 63–69.

References

175

O’Neill, R. V., Johnson, A. R., & King, A. W. (1989). A hierarchical framework for the analysis of scale. Landscape Ecology, 3, 193–205. O’Neill, R. V., Turner, S. J., Cullinan, V. I., Coffin, D. P., Cook, T., Conley, W., Brunt, J., Thomas, J. M., Conley, M. R., & Gosz, J. (1991). Multiple landscape scales: An intersite comparison. Landscape Ecology, 5, 137–144. O’Neill, R. V., Hunsaker, C. T., Timmins, S. P., Jackson, B. L., Jones, K. B., Riitters, K. H., & Wickham, J. D. (1996). Scale problems in reporting landscape pattern at the regional scale. Landscape Ecology, 11, 169–180. Oline, D. K., & Grant, M. C. (2002). Scaling patterns of biomass and soil properties: An empirical analysis. Landscape Ecology, 17, 1–14. Porter, W. P., Sabo, J. L., Tracy, C. R., Reichman, O. J., & Ramankutty, N. (2002). Physiology on a landscape scale: Plant-animal interactions. Integrative and Comparative Biology, 42(3), 431–453. Powell, T. M. (1989). Physical and biological scales of variability in lakes, estuaries, and the costal ocean. In J. Roughgarden, R. M. May, & S. A. Levin (Eds.), Perspectives in ecological theory (pp. 157–176). Princeton University Press. Pritchard, W. G. (1993). Scaling in the animal kingdom. Bulletin of Mathematical Biology, 55(1), 111–129. Qi, Y., & Wu, J. (1996). Effects of changing spatial resolution on the results of landscape pattern analysis using spatial autocorrelation indices. Landscape Ecology, 11, 39–49. Razgour, O., Hanmer, J., & Jones, G. (2011). Using multi-scale modelling to predict habitat suitability for species of conservation concern: The grey long-eared bat as a case study. Biological Conservation, 144(12), 2922–2930. Roshier, D. A., Robertson, A. I., Kingsford, R. T., & Green, D. G. (2001). Continental-scale interactions with temporary resources may explain the paradox of large populations of desert waterbirds in Australia. Landscape Ecology, 16, 547–556. Rouget, M. (2003). Measuring conservation value at fine and broad scales: Implications for a diverse and fragmented region, the Agulhas Plain. Biological Conservation, 112, 217–232. Sandin, L., & Johnson, R. K. (2004). Local, landscape and regional factors structuring benthic macroinvertebrate assemblages in Swedish streams. Landscape Ecology, 19, 501–514. Steele, J. H. (1989). Discussion: Scale and coupling in ecological systems. In J. Roughgarden, R. M. May, & S. A. Levin (Eds.), Perspectives in ecological theory (pp. 177–180). Princeton University Press. Steinhardt, U., & Volk, M. (2002). An investigation of water and matter balance on the mesolandscape scale: A hierarchical approach for landscape research. Landscape Ecology, 17, 1–12. Stratton, D. A. (1994). Genotype-environment interactions in Erigeron annuus show fine-scale selective heterogeneity. Evolution, 48, 1607–1618. Stratton, D. A. (1995). Spatial scale of variation in fitness of Erigeron annuus. American Naturalist, 146, 608–624. Swanson, F. J., & Sparks, R. E. (1990). Long-term ecological research and the invisible place. Bioscience, 40, 502–508. Tamburello, N., Côté, I. M., & Dulvy, N. K. (2015). Energy and the scaling of animal space use. The American Naturalist, 186(2), 196–211. Thompson, C. M., & McGarigal, K. (2002). The in uence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA). Landscape Ecology, 17, 569–586. Turner, M. G., Dale, V. H., & Gardner, R. H. (1989). Predicting across scales: Theory development and testing. Landscape Ecology, 3, 245–252. Turner, D. P., Cohen, W. B., & Kennedy, R. E. (2000). Alternative spatial resolution and estimation of carbon ux over a managed forest landscape in Western Oregon. Landscape Ecology, 15, 441–452.

176

4 Scaling Patterns and Processes Across Landscapes

Van der Meij, W. M., Temme, A. J., Lin, H. S., Gerke, H. H., & Sommer, M. (2018). On the role of hydrologic processes in soil and landscape evolution modeling: Concepts, complications and partial solutions. Earth-Science Reviews, 185, 1088–1106. Verburg, P. H., & Veldkamp, A. (2004). Projecting land use transitions at forest fringes in the Philippines at two spatial scales. Landscape Ecology, 19, 77–98. Walker, D. A., Halfpenny, J. C., Walker, M. D., & Wessman, C. A. (1993). Long-term studies of snow vegetation interactions. Bioscience, 43, 287–301. Ward, D., & Saltz, D. (1994). Foraging at different spatial scales: Dorcas gazelles foraging lilies in the Negev desert. Ecology, 75, 48–58. Weaver, J. C. (1995). Indicator species and scale of observation. Conservation Biology, 9, 939–942. Welander, J. (2000). Spatial and temporal dynamics of wild boar (Sus scrofa) rooting in a mosaic landscape. Journal of Zoology, 252(2), 263–271. Wiens, J. A. (1986). Spatial scale and temporal variation in studies of shrubsteppe birds. In J. Diamond & T. J. Case (Eds.), Community ecology (pp. 154–172). Harper & Row Publishers. Wiens, J. A. (1992). Ecology 2000: An essay on future directions in ecology. Ecological Society of America Bulletin, 73(3), 165–170. Wiens, J. A., & Milne, B. T. (1989). Scaling of “landscape” in landscape ecology, or, landscape ecology from a beetle’s perspective. Landscape Ecology, 3, 87–96. Wiens, J. A., Addicot, J. F., Case, T. J., & Diamond, J. (1986). Overview: The importance of spatial and temporal scale in ecological investigations. In Community ecology (pp. 145–153). Harper & Row Publishers. Wilbanks, T. J. (2006). How scale matters: Some concepts and findings. In Bridging scales and knowledge systems: Concepts and applications in ecosystem assessment (pp. 21–35). Woodcock, C. E., & Strahler, A. H. (1987). The factor of scale in remote sensing. Remote Sensing of Environment, 21(3), 311–332. Wu, J. (2004). Effects of changing scale on landscape pattern analysis: Scaling relations. Landscape Ecology, 19, 125–138. Wu, H., & Li, Z. L. (2009). Scale issues in remote sensing: A review on analysis, processing and modeling. Sensors, 9(3), 1768–1793. Zuercher, R., & Galloway, A. W. (2019). Coastal marine ecosystem connectivity: Pelagic ocean to kelp forest subsidies. Ecosphere, 10(2), e02602.

Chapter 5

Emerging Processes in the Landscape

Synthesis The disturbance is one of the most common and widespread phenomena shaping natural and man-made environments. Disturbance modifies land mosaic, shapes individual patches, and spreads across a broad range of temporal and spatial scales. Snow cover, flooding, gap disturbance in forest, fires, pathogens, animals, and human activity concur with create and maintain landscape patchiness. Fragmentation is one of the most evident processes shaping land mosaic according to the severity of disturbance and can occur in forest and in open lands as well. Fragmentation reduces the size of patches with effects on movements and dispersion of organisms, affecting finally the composition of communities. The reduction in the size of habitat patches may contribute to local extinction. The modified ratio area/perimeter if on one side favors edge species on the other increases the risk of predation. Connectivity, connectedness, and corridors are the responses to a fragmented, disturbed landscape. Corridors concur with the maintenance of a flux of organisms between different patches of the landscape. The dynamism of a landscape is extended also to the soil assuring movement of water and nutrients across different geological layers and land uses contributing to the maintenance of biodiversity.

5.1

Introduction

This chapter focuses mostly on the processes of disturbance, fragmentation, and connectivity, operating in a landscape across a broad range of spatiotemporal scales that, in turn, influence many landscape patterns. Landscape is often investigated from the perspective of patterns, but it is also a dynamic system, where steady state is time-dependent (Bingham et al., 2016), in which instability creates a sensitivity mosaics with the possibility of rapid irreversible changes of the entire system due to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Farina, Principles and Methods in Landscape Ecology, Landscape Series 31, https://doi.org/10.1007/978-3-030-96611-9_5

177

178

5 Emerging Processes in the Landscape

Fig. 5.1 Landform elements along a sensitivity land mosaic cycle. After a landslide, a colluvial sediments are eroded along a riverbank. (From Thomas, 2001, with permission)

perturbations (Thomas, 2001). Perturbation that occurs at small scale cannot change the stability of the system, but if this perturbation occurs at a larger scale, the system can abruptly react by changing. For instance, in the cycle of erosion/sedimentation, many landslides are the result of the exceeding an internal threshold of soil stability, after a long-term accumulation of small perturbations. New organization after a perturbation event can be propagated in the space like the gully erosion of sediments (Fig. 5.1). The behavior of the landscape in this case appears to be nonlinear and selforganized (Phillips, 1999). Among the huge number of processes acting in a landscape, we have selected the more pre-eminent and widespread, on which we have enough information like disturbance, fragmentation, connectivity, connectedness, and fluctuations of water and nutrients in the soil. The framework produced by this analysis is not uniform or homogeneous, reflecting the abundant gaps in research and diversity in conceptual and operative. The approach that we have utilized in distinguishing these processes is the result of a strategy to describe in a simplified but a more understandable way the emerging components of the landscape complexity and dynamicity. Most of the processes described in this chapter are related to each other. In particular, disturbance and fragmentation are two processes with strong relationships and it appears hard to distinguish the role and the rate of the interactions. These two processes are mainly responsible for the emergence of heterogeneity in the (landscape) mosaic. These processes have effects and relationships with many other abiotic and biotic processes according to different hierarchical levels of functions and patterns. Disturbance occupies a pre-eminent position in the landscape

5.2 Disturbance

179

functioning and is the main responsible action in shaping landscapes and their components. This process can be driven by many factors and interacts with other processes acting in a more restricted context like fragmentation or land abandonment.

5.2 5.2.1

Disturbance Introduction

Disturbance is a very common and widespread phenomenon in nature and may be defined as a discrete event along a temporal window that modifies population structure, community, ecosystems, and landscapes changing the substrate, the physical environment, and the availability of resources (White & Pickett, 1985; Newman, 2019). Disturbance is very common in any system, operating at all spatiotemporal scales and producing an alteration of the structures of a system and impacting resource availability. Disturbance occurs in many biotic assemblages, at all levels of organization. Disturbance combines long time scale changes with “actuality.” The basic variable of disturbance is magnitude, frequency, size, and dispersion (Fig. 5.2).

Fig. 5.2 Example of fire disturbance regime along a recent period. Every disturbance event, in this case fire, has a disturbance patch with different shapes and attributes (size, interval, and intensity). (From Baker, 1992, with permission)

180

5 Emerging Processes in the Landscape

It can be considered as a basic process, responsible for many other processes like fragmentation, animal movements, and local and regional extinction. Disturbance is a source of spatial and temporal heterogeneity. In addition, we must also consider the self-disturbance by litter production of plants as source of spatiotemporal heterogeneity (Bascompte & Rodriguez, 2000). Every landscape is shaped, maintained, and/or changed by disturbance. Disturbances like clear-cutting and wildfire have a strong influence on the structure and functioning of many landscapes. To predict the impact of a disturbance regime on communities and landscape, it is necessary to understand at least the spatial and temporal “architecture” of the disturbance, as questioned by Moloney and Levin (1996). At the landscape level, disturbance is related to patch structure and spatial arrangement, and it determines the fate of patches, their size, and duration. Severe disturbances or the lack of disturbance generally have depressing effects on diversity although evidences of an increase in genetic diversity have been observed in some groups of animals (Pagan et al., 2020). An intermediate disturbance seems to enhance diversity in the systems (Connell, 1978; Townsend et al., 1997). Where disturbance recurs more frequently than the time required for competitive exclusion, the diversity of the biological assemblage is maintained. It is the case of plant diversity of montane meadows in which human disturbance regime by summer hay harvesting and late summer and early autumn grazing prevents the development of dominant species, and as a consequence, the floristic diversity is maintained at the highest levels compared with less disturbed prairies (Dolezal et al., 2011). However, this system appears to be extremely fragile and needs continuous external input to be maintained. We will discuss in greater detail the fate of such landscapes in the chapter devoted to land abandonment. Disturbance may be produced by abiotic factors such as solar rays, water and wind erosion, floods, wind storms, landslides, and avalanches or by biotic elements like bacteria, virus, plant, and animal competition. Landscape is deeply influenced by the disturbance regime that results one of the most efficient agents of landscape engineering. Gluck and Rempel (1996) have compared the landscape pattern of two forested areas of Northwestern Ontario, respectively, subjected to clear-cutting and wildfire. Patches in clear-cutting were found to be larger in size and with more irregular edges and with more core areas than those in the wildfire regime landscape. When an infrequent disturbance occurs, deep changes in floristic and vegetational structure are expected (Schoennagel et al., 2008). This is the case of the aspen seedling recruitment in the Yellowstone Park after the 1988 large fire, which represents a substantial change in the forest landscape, although long-term fate of these postfire recruitment cannot be presently predicted (Turner et al., 2003). Before the European settlement in North America, fires produced by indigenous dwellers or by spontaneous causes occurred more frequently than suspected, shaping landscape mosaic and driving soil nutrients (Vale, 2002). For instance, in Eastern deciduous forests, after the intensive logging for charcoal production during midand late 1800 for iron industry, most of the forests experienced a secondary succession for at least a century, fire suppression for half a century, and chronic atmospheric deposition. The combinations of these factors have varied structure and composition of such forests. Recently, in order to restore a forest dynamics and to

5.2 Disturbance

181

reduce the effect of atmospheric deposition, a program of prescribed fires long-term program has been launched with controversial results as argued by Boerner et al. (2000). The use of prescribed fires as a regular practice to restore ecosystem functionality is more than a promise, required by a lot of local information about natural history of the burned site and a precise long-term protocol of interventions.

5.2.2

Snow Cover, an Example of Abiotic Disturbance

Due to the high climatic constraint, the vegetation cover in mountain landscapes is extremely patchy. Snow cover controls the distribution of many species of plants, reducing the length of growing season. The distribution of snow is strongly conditioned by topography, wind patterns, and extreme winter warming (Semenchuk et al., 2013) (Fig. 5.3), but exposed ridges experience low temperature in winter and animals like moles, gophers, and voles, responsible for the fine-scale disturbed mosaic, and are also conditioned by snow accumulation. These species find refuges in snow accumulation and protected trails for soil exploration. For instance, in winter, when the soil is covered by snow, European snow voles (Chionomys nivalis) often build the nest with dry grasses and moss on the surface of soil and dig tunnels in the compact snow searching for food.

Fig. 5.3 Patchy distribution of snow accumulations influences vegetation, especially in springtime, due to the different soil temperatures and water contents. Mount Casarola, Northern Apennines, Italy

182

5 Emerging Processes in the Landscape

6

Event frequency (log of years)

Glaciations 4 Climate change

2 Spring flood Animal disturbances

0

Ground water discharge Snowbank formation and melting Daily freeze-thaw cycle -2 -4

-2

0

2

4

6

8

10

Point sampling 1:500-scale maps 1:10,000-scale maps SPOT panchromatic SPOT multispectral Thematic Mapper (TM) Multispectral Scanner (MSS) Advanced Very High Resolution Radiomater (VHRR)

Fig. 5.4 Frequency of the disturbance and the spatial scale of resolution in cold climate. The available data types are indicated at the bottom of the figure as example of application of a multiscale approach ranging from data input by field survey (quadrat plots) to remote sensing technique (advances very high-resolution radiometer, AVHRR). (From Walker et al., 1993, with permission)

Plants react to snow cover in different ways (see Walker et al., 1993 for more details on plants of Rocky mountains) (Fig. 5.4). Some species escape deep snow cover (e.g., Paronychia pulvinata), others are mainly localized in deep snow cover (e.g., Sibbaldia procumbens) and others show no precise snow interaction spanning a broad range of snow depth. Snow accumulation has indirect effect on vegetation and the circulation of nutrients in the soil during the spring snow melting. This process is extremely important in alpine regions where plants suffer from nitrogen and phosphorus deficit (Clement et al., 2012).

5.2 Disturbance

183

Artificial snow production frequent in these last times of warm winters in the majority of ski resort contributes to mitigate the effects of climate change on vegetation but contemporarily is source of uncertainty on the effect of nitrate salts applied to improve the snow quality for ski race (Rixen et al., 2003).

5.2.3

Gap Disturbance in Forest

Gaps are small openings in forest cover due to local events as trees fall and in forests where large disturbances are rare, the gaps created by the killing of one or a few canopy trees play a fundamental role in structuring the entire forest (Muscolo et al., 2014). An edaphic gap has to be distinguished from tree gap because it is produced by an edaphic condition such as stream erosion or thin soil. The interest for the gap dynamics in forest ecology is confirmed by a rich literature (see a recent review by McCarthy, 2001; Yamamoto, 2000). Most of the trees that live in old-growth forests require gaps to reach maturity. Generally, forest gaps are not a random event, but some sites are more likely to have gaps than others. Across the forest, the gap density is constant, but there are evidences that along a catena, gaps are more abundant in the middle part, while in the upper and lower slope, gaps are less frequent (Poorter et al., 1994). The regeneration occurring after the formation of a new gap plays a fundamental role in structuring forests and to maintain species diversity. Gaps are occupied by different species of organisms in comparison with the forest understory. Large, infrequent blowdown can modify the structure of forests favoring insect pests, secondary succession, and fire propensity. Such disturbances produce only a partial loss of overstory trees, and most of the dead snags and down woody material are retained. Edges between blowdown and intact forests are typically lower-contrast edges. A study conducted by Lindemann and Baker (2001) on the effect of a RouttDivide Blowdown in 1997 in Northern Colorado has demonstrated a wide array of patches produced by wind storm with an unusual variability in patch attributes (size, perimeter length, distance to the nearest patch). Large forest disturbance, like the hurricane Hugo, can change the structure of bird assemblage in old gaps and new forest disturbance (Foster et al., 1998). Wunderle (1995) has found that the major effect of the passage of Hurricane Hugo on bird assemblage in a Puerto Rican forest has been the loss of distinctiveness between the bird assemblage living in the gaps and birds living in the disturbed understory. According to this author, it will probably take many years before the gap and understory become distinct in structure and resources. Gaps are particularly evident in the changing phase from mature to old-growth forests. A tree that dies is considered a “gapmaker” because it creates the gap. In the forests of British Columbia, Lertzman et al. (1996) have estimated that in the absence of a large disturbance such as fire, wind storm, or insect diseases, gaps created by a regime of small-scale, low-intensity disturbance are responsible for the turnover of these forests over 350–950 years. Most gaps are produced by more than

184

5 Emerging Processes in the Landscape

Soil nutrientenrichment Unique species composition Enhanced understory productivity and tissue nutrient concentrations Reduced soil and plant temperatures Reduced solar radiation

Fig. 5.5 Effects of isolated trees in tropical savanna (Acacia tortilis) in Tsavo National Park, Kenya. (From Belsky & Canham, 1994, with permission)

one dead tree. Some gaps (a third) were found produced by edaphic factor as stream erosion. In savanna landscape, dynamics (Baudena et al., 2015) may also be explained in terms of gap dynamics where gaps are not created by dead trees but by living trees that affect grass and shrub cover. Trees and shrubs are the “gap” in the grassland matrix. Belsky and Canham (1994) have discussed the structure and function of savanna trees in a matrix of grasslands comparing forest gaps with savanna trees. Figure 5.5 shows an example of forest and savanna gaps (Belsky & Canham, 1994). In savanna “gap,” a gap is initiated by tree seedling establishment and growth. The physical conditions under savanna trees are different compared with the surrounding open savanna-like gaps in forest environment. During wet season, the soil under the trees is dryer, but later in the season, the soil is wetter under trees due to a reduced evapotranspiration in the shade and a cooler temperature. It is well known that under savanna trees, the soil is richer of nutrients due to root transportation, manure deposition by wild and domestic animals, and by a less stressful bacteria cycle.

5.2.4

Fire Disturbance in Landscapes

Fire is one of the most important shaping agents in landscapes. It removes the undecomposed biomass and creates nutrient fluxes by ash deposit watering, contributing to the ecologically rejuvenating qualities in the grassland and forest ecosystems (Moore, 1996). In particular, fires contribute to the resilience of old-growth landscapes where a symbiotic relationship occurs between trees, understory graminoids, and fires (Binkley et al., 2007).

5.2 Disturbance

185

Fig. 5.6 Relationship between area of fires and their shape (perimeter, number of tongues, edge simplicity, and ratio of major to minor axis) of 817 fires (in B) only 341 fires were used in Great Victoria Desert, Australia, from 1972 to 1991. (From Haydon et al., 2000a, with permission)

Fire has been utilized as a management tool to manipulate the ecosystem since Mesolithic times (Naveh, 1990, 1991; Grove & Rackham, 2001; Blondel & Aronson, 2001; Coughlan, 2015). In a dry continent like Australia, fires have played an important role in shaping vegetation mosaic and fauna distribution. In desert areas, fires produced by lightings create a complex mosaic of burned-unburned areas that favor several species of animals like lizards (Pianka, 1986). Recent studies and simulations conducted by Haydon et al. (2000a, b) in the Great Victoria Desert (Australia) have emphasized the role of wildfires as main perturbation agents. Fire size is influenced by wind direction, shaping scaring areas that assumes an oblongata form. Perimeters of larger fires are more complex than smaller fires. Large fires have more tongues than smaller fires according to the wind direction (Fig. 5.6). Haydon et al. (2000a, b) have calculated that the return time is

186

5 Emerging Processes in the Landscape

not less than 20 years and approximately 2–5% of the area is burned each year. Older patches are more prone to burn due to a major accumulation of biomass. The release of nutrients in the soil is well documented in postfire dynamics; however, the role of pyrogenic carbon appears neglected. Pyrogenic carbon has the capacity to retain water, and a sand soil can behave as clay if added with charcoal (Pingree & DeLuca, 2017). The effect of fires on vegetation cover depends on a great amount of concurrent factors like fire history (severity, recurrence), climate, topography, and dominant type of vegetation. In Catalonia (northeastern Spain), Diaz-Delgado et al. (2002), using the Normalized Difference Vegetation Index from Landsat imagery, have observed that vegetation resilience calculated on the green biomass 38 months after the second fire increases with the time between consecutive fires. In the Mediterranean region, fire regimes actually have no possibilities to counterbalance the general trend of landscape coalescence, principally due to agriculture abandonment and shrubland increase. In Tivissa municipality (Catalonia, Spain), Lloret et al. (2002) have provided evidences, investigating the land cover changes in the period 1956–1973, that landscape heterogeneity decreases disturbance spread and that fires cannot reduce the actual trend of transformation of woody areas in shrublands. It seems that other drivers have to be searched in the economic domain to explain the actual trend common to all southern Europe. Fire, when associated with wild grazing, represents a dramatic disturbance in ecosystems. As reported by Bailey and Whitham (2002), the effects of large crown fires in Arizona on aspen (Populus tremuloides) forest and on arthropod community largely depend on fire severity and on elk grazing pressure. Moderate severity and moderate levels of elk browsing assure 30% of greater richness and 40% greater abundance in arthropod communities. On the contrary, high severity fire and high level of elk browsing reduce diversity and abundance of 69% and 72%, respectively. In Brittany, Morvan et al. (1995) have observed that fire speed in heathland landscape is strongly linked to landscape heterogeneity when a scale of resolution of 25
25 m is considered. The landscape heterogeneity increases with fire frequency, but the diversity that grows after a burning event decreases when fire disturbances occur frequently. The study of fire occurrences during a large period of time is recommended by Rollins et al. (2002) as a historical baseline for fire management in wilderness mountain complexes. These authors have reconstructed the fire history during the twentieth century in two wilderness areas of Rocky Mountains in New Mexico, Montana, and Idaho. Results indicate that the amount and horizontal continuity of herbaceous fuels are the limiting factor of frequency and spread of surface fires in Southern Rockies, while moisture status of large fuels and crown fuels are the limiting factors of the frequency of moderate-to-high severe fires in Northern Rockies. Fire suppression has been a common practice during the second half of the twentieth century. Such suppression has increased C storage in soil, and according to empirical and predicted data, fire suppression in the USA might represent 8–20% of missing global carbon, as reported by Tilman et al. (2000).

5.2 Disturbance

5.2.5

187

Pathogen Disturbance in Forest Landscape

Less attention has been reserved to the role of pathogens in shaping and structuring forests (Burdon et al., 2006, Winder & Shamoun, 2006). Pathogens influence forests at different ranges of spatial and temporal scale. Forest diseases occur in patchy distribution across the landscape (Lundquist & Klopfenstein, 2001), and pathogens reflect into their genetic variability the landscape heterogeneity. This creates a disease-prone land mosaic across the entire forest landscape. Some pathogens like Brunchorstia pinea, responsible for the Scleroderris cancer in pines, are more active in cooler climates with more impact on stands occurring in topographic depressions and forest opening where cold air accumulates. The cancer of sweet chestnut has accelerated the change in landscape in south Europe in combination with land abandonment. A great area occupied by this type of orchards has been in a short time modified by cutting off of diseased plants returning an uncertain future-proof tree species at least for Central Europe. Pathogens play a fundamental role in the formation of gaps in mature and wealthy old-growth forests. Patch-phase processes of disturbance create the condition for a landscape heterogeneity, enhancing the plant diversity, and resource availability for plants and animals. Pathogens change also the composition of forests, increasing unevenness of stands. The knowledge of pathogen cycles is essential to an efficient and accurate management of forest (Castello et al., 1995).

5.2.6

Animal Disturbance

Digging and grazing are the most common disturbances produced by animals (mainly herbivores) (Figs. 5.7 and 5.8). Well known is the effect of keystone species

Fig. 5.7 Spatial distribution of gopher disturbance by digging in a plot of 3
1 m from 1983 to 1988 in a serpentine grassland. (From Hobbs & Mooney, 1991, with permission)

188

5 Emerging Processes in the Landscape

Fig. 5.8 Moles (Talpa sp.), wild boars (Sus scrofa), and voles are disturbance agents in open and forested landscape

like bisons in North American tall grass prairie (Knapp et al., 1999) that severely affect distribution and structure of vegetation (grass, forb, and shrubs). In forested area, grazing prevents the growth of seeds. Trampling associated with grazing modifies the composition of natural vegetation and reduces the interspecific competition creating patches of high diversity, but this disturbance is often quite complex (Hobbs & Mooney, 1991). In natural and livestock-grazed prairies, the deposition of urine is a cause of local disturbance that produces a complicated mosaic at a larger scale (Morris & Reich, 2013). This mosaic depends mainly on the density of grazing wild or domestic animals. Steinauer and Collins (1995) tested the effect of urine deposition in differently disturbed grasslands. Plant abundance increases after urine deposition, but alfa and beta diversity displayed local behavior mainly due to litter depth. This biomass accumulation attracts more herbivores, and the effect of urine is expanded in the neighboring environment. Finally, the grazing intensity in such patches has more deep effect than urine deposition alone. This seems a good example of co-occurring disturbance that can reinforce the reaction of the environment.

5.2.7

Human Disturbances

Human disturbance is not really different than the natural disturbance but with some significant peculiarities, especially in extension, severity, and frequency (Fig. 5.9). These last factors make the difference. Forestry, agriculture, development, and infrastructures are some of the disturbances that human activity can produce on the landscape and at larger scale on regions. Human–environment interactions are distributed worldwide, and we can emphasize that all parts of the planet are affected by such dominant presence. Zaveh (1992)

5.2 Disturbance

189

Fig. 5.9 Examples of different disturbance regimes produced by human activity: (a) mining; (b) river banking; (c) woodland selective logging; (d) accidental fires

utilized the term “total human ecosystem” referring to the earth. More recently, Crutzen and Stoermer (2000) have called the present epoch “Anthropocene,” highlighting the global and detrimental significance human activity now has on the earth, surpassing the significance of natural processes underlining the previous Holocene epoch. The disturbance regime due to human activity is expanded by the technology to a broad range of spatiotemporal scales with effects ranging from the deepest oceans to the highest mountain ridges. The capacity of the landscape to incorporate human disturbance in many cases is outside the limit, and disturbance processes are transformed into stressful processes that reduce the diversity of the land mosaic and composition and dynamics of the ecological communities. In some cases, human disturbance has multiplicative effects on both landscape patterns and population dynamics. An example of this can be observed in stream ecology. Streams have longitudinal and lateral dynamics influenced by the watershed quality and in-stream modifications. Fishes living in such habitat are sizestructured vertebrates. This means that a population is composed of different age individuals with a broad range of sizes. For every size class, the provided habitat is

190

5 Emerging Processes in the Landscape

specific. Any alteration of stream structure has a specific effect on the different age classes of fishes. The alteration of stream dynamics and structures by human use of the land produces consistent effects of the composition of fish populations. For instance, a disturbance of the mainstream for gravel mining can reduce the depth of water-removing habitat for large-sized fishes and, conversely, can increase the abundance of small-scale individuals offering shallow waters. As pointed out by Schlosser (1991), alteration in structural and functional relationships inside the landscape can reduce diversity of adult and juvenile fish, decrease complexity in the size structure, increase the abundance of juveniles because of increased area of shallow refuges, and also increase the variability in fish abundance. Deforestation for logging represents a major source of landscape disturbance, especially in relatively pristine areas in the Anthropocene (Malhi et al., 2014). McGarigal et al. (2001) have described the cumulative effect of roads and logging in a wilderness area in the San Juan Mountains, Colorado. These effects evaluated in the interval 1950–1993 seem quite trivial at the scale of 228 000 ha landscape. At this scale, the landscape seems capable to incorporate disturbances, but at an intermediate scale of 1000–10000 ha, changes appear evident. This study demonstrates that environmental evaluation is scale-dependent and that a multiscale approach can overpass the difficulties to environmental assessment. Often, disturbance regime is the result of cumulative effects. For instance, in Great Plain grassland, largely converted into cropland, the remnant native grasslands have experienced from 1965 to 1995 a dramatic increase in eastern juniper (Juniperus virginiana L.) woodlands and of deciduous woodlands (Coppedge et al., 2001). This last cover largely depends by the juniper encroachment. This fact has increased the fragmentation of vegetation cover that appears more patchily distributed and is also favored by the lack of recurrent wildfires. Often, the characters of the human disturbance differ from the characters of a natural disturbance. For instance, a fire along a Mediterranean coast produced by human “lack of attention” or fraudulently is not different as process from a wildfire, but this fire, when repeated at every season (improbable that should be caused by spontaneous causes), can produce a stress on vegetation, reducing vegetation cover, increasing soil erosion, reducing animal diversity, etc. Human activity is modifying the face of the earth, reducing natural vegetation, animal communities, and exemplifying landscape mosaic. The rearrangement of natural systems according to human needs, especially if carried out by burning fossil oil, dramatically interferes with the ecosystem dynamics and landscape mosaic structure. For instance, the increase in urban sprawl at the planetary scale affects the environment in several ways and at multiple levels of biological organization. Recently, Blair (2004) has discussed the effects of urban development on birds in northern California and Ohio at individual level in terms of predation that seems lower in urban areas. At the species level, invasion and extinction are strongly affected by different levels of local urbanization. At the community level, urban area can have a diversity peak where urbanization is moderate.

5.3 Fragmentation

191

Human-caused disturbance has been recently considered by Frid and Dill (2002) as a form of predatory risk. In fact, there are several evidences that human activity produces a non-lethal disturbance on the behavior and the reproductive success of animals. Predatory pressure and disturbance produce similar trade-offs to avoid risks and perform other functions (activities) like feeding, mating, or parental caring. The predatory avoidance is a function that requires energy, and this activity reduces the energy available for other functions. Human activity disturbs wildlife in different ways, by producing loud noises like the shot of a rifle or the sound of a horn or the intrusion of visitors in animal sanctuaries. Landscape features can mitigate the cost of this disturbance reducing the distance at which animals react, moving from a site and interrupting a function. For instance, animals like fallow deer (Capreolus capreolus), feeding at forest ecotones, can feel safer if the ecotones have a convolute shape or if there are fragments of woodlots between forest and open grazing fields functioning like stepping stones. The reaction to human disturbance largely depends on the physiological status of animals. For instance, in some amphibians, safety is postponed to mating access and frogs can cross busy roads exposing to the risk to be killed by motor vehicles. During a territorial display, European robin (Erithacus rubecula) reduces the distance from feral cats, dogs, and humans, but organisms forced by the disturbance to select less favorable areas can experience a rapid decline in intraspecific competition or an increased predatory pressure. Human disturbance can produce unexpected effects; for instance, it can facilitate the entrance of invaders into a community. This is the case of Chaerophyllum aureum, a plant that is common in meadows in Pyrenean valleys. The genetic variability that is the main cause of diffusion of this species is increased by human practices of hay production, as reported by Magda and Gonnet (2001). These authors, using polyphenol compounds as individual markers, have found that in the studied area at least one dominant genotype and five different populations exist. The spatial arrangement of “genetic” populations was found independent by environmental factors but mainly due to human practices that mix seeds of this plant, collecting hay and “amplify the colonization process of adapted genotypes.” Also, recreation is not a secondary disturbance on organisms like birds (see Bennet & Zuelke, 1999). Last but not least, domestic animals like cats and dogs contribute, especially in urban areas to the decline of many species of birds and small mammals (Baker et al., 2005).

5.3 5.3.1

Fragmentation Introduction

Fragmentation of natural environments is one of the most severe worldwide processes depressing biodiversity (Fischer & Lindenmayer, 2007). For instance, 70% of the remaining global forest is within 1 km of the forest’s edge (Haddad et al., 2015).

192

5 Emerging Processes in the Landscape

These processes have a strong influence on the dynamics and fate of material and energy moving across a landscape. Several papers have focused on the fragmentation processes as central issue in landscape ecology and conservation planning (Saunders et al., 1991; Wilcove et al., 1986; Wiens, 1994; Collinge, 1996). Loss of native plant and animal species, invasion of exotic species, increase in soil erosion, and decrease in water quality are some of the consequence of habitat fragmentation. Fragmentation moves at an alarming rate around the world reducing large forest cover and natural prairies (Wade et al., 2003). In some parts of the earth, fragmentation occurred mainly in the previous centuries as in Australia and in Brazil (Hobbs & Hopkins, 1990) with devastating consequences on the environment. A recent investigation (Wade et al., 2003) has estimated that over half of temperate broadleaf and mixed forest biome and about one quarter of the tropical rain forest biome have been fragmented or removed by human use. Conversely, in boreal biomes, only 4% of the forest have been fragmented or removed. Clear-cuts and roads dramatically increase the fragmentation effects on forest cover, and roads are especially important agents of forest cover changes as argued by Tinker et al. (1998). Fragmentation is a process that presents negative influence on many species of plants, animals, and ecological processes in landscape (Hovel, 2003). Reducing size of fragmented blocks decreases the density of populations and metapopulations with the growth of extinction risk, as is documented by Conner and Rudolph (1991) for red-cockaded woodpecker (Picoides borealis) populations in East Texas. According to different perspectives, fragmentation can be considered as the “negative image” of connectivity. In fact, fragmentation means geographic isolation, and after extinction, the probability of recolonization strongly depends on the distance of fragments from the main core and on the quality of the surrounding habitat. Doherty and Grubb (2002), investigating residential birds living in forest fragments of different sizes in an Ohio agricultural landscape, have found that the survival rate was negatively affected by fragment size. Survival was higher in larger remnants. Zanette et al. (2000) have investigated the effects of fragmentation on forest-interior songbirds that are considered “area-sensitive” species because they are not found in small forest fragments. These authors have investigated the effects of forest fragmentation on the eastern yellow robin (Eopsaltria australis) living in two small (~55 ha) and in two large forest fragments (>400 ha) in a farmland mosaic in New South Wales (Australia) by counting the insect abundance and the reproductive success. Incubating females living in small fragments received 40% less food from males. The breeding season in small fragments was shorter; females laid eggs 7% lighter, and nestlings were smaller than the ones living in larger plots. This study confirms food shortage as the small fragment condition that affects the reproductive performance in this species. In tropical areas, forest fragmentation affects pollen and seed dispersal, with modification in gene flow (Hamilton, 1999). Species sensible to the edge (interior species) can reduce in abundance or in pairing success (Villard et al., 1993). Fragmentation is often interpreted by adopting the general framework of the island biogeography theory (MacArthur & Wilson, 1967), but area size and isolation factors taken into account by this theory are not enough to explain the effect of

5.3 Fragmentation

193

fragmentation in habitat islands. If the fragmentation is simply considered as the size of isolated patches, this approach appears uninformative. Fragments cannot be considered true islands; in fact, the surrounding habitats are often not completely hostile for the species. Other factors such as connectivity, the presence of ecotones and corridors, and the metapopulation structure have to be taken into account, especially when fragmentation is studied at a landscape scale (Gu et al., 2002). For instance, Borowski et al. (2021) have found that a forest network of unpaved roads discourages red and roe deer reducing their impact on regenerating forests. Fragmentation can be considered a continuum process, and according to a landscape perspective, matrix and patches are the elements that have to be used for classifying a landscape fragmented or not (Wiens, 1994). Fragmentation is a process perceived in a different way by different species. For instance, Thomas (2000) found that butterfly species with intermediate dispersing capacities are more affected by fragmentation in comparison with sedentary or highvagile species. At the edge, the behavior of species is different. For some species, edges are highly suitable habitats, but others avoid edges. Nest predation can be higher at the edges, and this has a big influence on the suitability of the patches in a fragmented landscape (Pasitschniak-Arts et al., 1998; Flaspohler et al., 2001; Sosa & de Casenave, 2017; Sedláček et al., 2014). However, inverse edge effect has been observed on nest predation in a Kenyan forest fragment where ground-nesting species seem less affected by small predatory mammals and snakes (Spanhove et al., 2009). Fragmentation is really a dynamic process. The human disturbance regime and natural disturbances produce fragmentation but often recovers vegetation cover masks or mitigates this process. In other cases, the fidelity of some species to a site reduces the effect of fragmentation. Fragmentation dynamics is strongly influenced by human decisions and by the type of land policy. Staus et al. (2002) have found a higher fragmentation rate in private than in public forested land in the Klamath–Siskiyou ecoregion (Pacific United States coast) during the period 1972–1992 (Fig. 5.10). Fragmentation can be increased not simply by reduction in patch size but also by insulation of patches produced by large roads. Brotons and Herrando (2001) have observed the severe effect of highways on the distribution of birds in an agricultural matrix. In particular, forest birds seem more sensible to the presence of such barriers. Noise produced by traffic does not seem the only factor impacting bird presence. Other factors, like the decrease in connectivity between the forest fragment, are also important. Fragmentation in tropical areas strongly affects the survivorship of large trees that are unusually vulnerable. Large trees, as argued by Laurance et al. (2000), are more prone to uprooting and breakage near forest edges where wind turbulence is frequent and higher. Large trees are frequently invaded by lianas (woody wines) that benefit from light and nutrients but that reduce the survivorship of trees. The higher exposition to sunlight and evaporation also contributes to tree desiccation. The

194

5 Emerging Processes in the Landscape

Fig. 5.10 Different patterns of changes in forest and nonforest land on public and private land in Klamath–Siskiyou ecoregion, located at the border between Oregon and California. (From Staus et al., 2002, with permission)

reduction in large trees affects fruit production, flowers, and shelter for animal populations.

5.3.2

Scale and Patterns of Fragmentation

Fragmentation is a scale-dependent process producing different effects (Garcia & Chacoff, 2007). Fragmented vegetation can have a different spatial arrangement and produce different effects on other ecological processes. To describe the dispersion of fragments in an area, it is necessary to consider different attributes of the fragments such as density, isolation, size, shape, aggregation, and boundary characteristics. The isolation of patches increases geometrically as the density of fragments decreases. The fragments become smaller and receive more influence from the

5.3 Fragmentation

195

surrounding matrix. If fragments are aggregated, their isolation is mitigated and smaller than in conditions of equi-dispersion. We have different types of fragmentation. According to Lord and Norton (1990) when an intact area is divided into smaller intact fragments, we have a “geographical fragmentation.” This process has received a lot of attention from conservation ecologists for its implication to nature conservation. On the other extreme, we can have fragments at the scale of individuals or small plots. It is the case of small remnants of native vegetation embedded in an alien matrix, and the fragmentation is considered as fine-grained “structured fragmentation.” Fine-grained fragmentation generally presents patches close to each other, and the structural contrast between patches and matrix is shallow, creating a pseudocontinuum. While geographical fragmentation is associated with forest ecosystems, the structural fragmentation may be the result of a broad range of conditions. Fragmentation increases the vulnerability of patches to external disturbances. For instance, windstorm or drought has consequences on the survival of these patches and of the supporting biodiversity (Nilsson & Grelsson, 1995). Fragmentation effects on organisms largely depend on the scale of perception of focal species. Habitat generalists are less affected by fine-grained fragmentation than specialists. The scale of fragmentation has a direct impact on the dynamics of individuals, populations, and communities. In particular, large fragments maintain a good subset of species, but small fragments preserve only few species, typically generalists. So, specialists disappear from smaller patches when the fragmentation is at a fine scale. This could be the reason of a high diversity composed of generalist species in temperate region persisting in a fine-grained fragmented mosaic. On the contrary, in tropical regions the majority of species are specialists and require a more coarsegrained mosaic to survive. Fragmentation has been proved to affect the morphological and genetic structure of Ecuadorian Tapaculo (Scallops robbins) a bird at risk of extinction (a population of only 3000 mature individuals survives). In this species, individual living in small fragments shows adaptation of wing morphology to increase mobility. This could be an important signal of adaptation with possible reduction in local risk of extinction (Hermes et al., 2016). The patterns of fragmentation are under the effect of many natural and humanmade variables. For instance, the presence of agricultural proximity is a good estimator of fragment probability in the bottomland hardwood forest, but access, urban development, ownership, fencing, and regional differences are other secondary parameters useful to predict type and modality of fragmentation (Rudis, 1995). For instance, Fuller (2001) has utilized band 6 of LANDSAT Enhanced Thematic Mapper Plus to evaluate the forest fragmentation in Loudoun County, Virginia, based on thermal response of developed areas versus intact woodland. Fragmentation reduces the size of woodlot but also the habitat quality. Belanger and Grenier (2002) have noted an increase in woodlot density in St. Lawrence Valley (Quebec, Canada) with the increase in agriculture, but the size of woodlots was observed to be decreasing. Fragmentation was found to increase moving from traditional dairy agriculture to more intense cash crop agriculture (Fig. 5.11).

196

5 Emerging Processes in the Landscape

Fig. 5.11 Relationship observed on the first axis of a canonical correlation between the scores of the first PCA axis that characterizes the agricultural landscape in 59 county municipalities in southern Quebec (Canada) and human population densities, with different forest fragmentation indexes. Forest fragmentation increased from dairy farming toward a cash crops. (From Belanger & Grenier, 2002, with permission)

5.3.3

Community Composition and Diversity in Fragments

Fragmentation depends on human use, but the human use is also affected by the fragmentation rate. This is relevant in regions such as the Mediterranean where several changes in land uses occurred across centuries, modifying the behavior of people according to the new characters of fragments. Large fragments have more species, are less disturbed, and have low road access than smaller fragments. Unfortunately, large fragments are uncommon or rare and their importance is high for nature conservation issues. Small woodlots have less species than the biggest ones, and there are more generalists in small rather than in larger woodlot. More specialized species increase with the increase in the woodlot area. Blake and Karr (1987) found more than 66–72% of species are more strongly influenced by habitat variables. Birds breeding in the interior forest and wintering in the tropics are more affected by reduction (fragmentation) in forest habitat (Fig. 5.12). The area effect is disputable according to neighboring habitats. In fact, if there are suitable habitats around a woodlot, these habitats could be incorporated by some species, but if the woodlot is separated by agricultural fields, in this case, the habitat constraint is stronger and the isolation is higher, negatively affecting the presence of species. The continuous loss of forests across the USA will probably have negative effects on several species of birds, although 3-year studies have demonstrated a good

5.3 Fragmentation

197

Fig. 5.12 Number of breeding species plotted as function of natural logarithm of an area of woodlot in east-central Illinois. (From Blake & Karr, 1987, with permission)

stability in populations in woodlot (Blake & Karr, 1987). However, this does not exclude that in a longer time lag, negative effects could appear. Large patches of Nothofagus forest in south-temperate rainforest result in more heterogeneous than smaller patches. Bird diversity decreases according to the patch size. This effect is also evident in the cases in which patches are not far from main forest or from other patches and apparently shrubby corridors occur. For instance, the main causes of the decrease in abundance and diversity of Chilean avifauna depend on habitat destruction, but also the clearing of understory may be a negative factor because many species are breeding in the shrub layers or find resources at this height (Willson et al., 1994). The landscape composition has been found by Rodewald and Yahner (2001) to be an important factor influencing avian community in central Pennsylvania. In forests disturbed by silviculture practices, forest-associated, long-distance migrants, forest canopy, and forest understory nesting species were found to be more abundant than in forests disturbed by agricultural practices. These authors argue that the type of disturbance is more important than its extension. Ground beetle (Coleoptera:Carabidae) diversity has been found to be lower than expected in forest remnants in French agricultural landscape and quite similar to crop and land edges. These data have been discussed by Fournier and Loreau (2001) in terms of marginality of the role of small forests to preserve beetle biodiversity. Laurance et al. (1997) have found in forest fragments in central Amazon a dramatic loss of aboveground tree biomass (36% in the first 10–17 years after fragmentation) inside 100 m of fragment edges (Fig. 5.13), but other results have been found by Davies et al. (2001) working on the effect of experimental fragmentation of native

198

5 Emerging Processes in the Landscape

Fig. 5.13 After tree removal in tropical landscape, the atmosphere circulation may influence the surrounding forest humidity. (From Laurance et al., 2011, with permission)

eucalyptus of Australia on 325 species of beetles. Fragments do not seem to affect their richness or colonization, and extinction rates. At the edge of fragments in tropical regions, trees experience mortality due to microclimatic changes and elevated wind turbulence (Laurance et al., 1997). The major decline in biomass occurs from 0 to 4 years after fragmentation. When tropical forests are fragmented, an immediate loss of biodiversity is experienced by the stands and a consistent subtraction of humidity from the neighboring forest is observed (Laurance et al., 2011). Despite the temperate fragmented forests in which the diversity, especially birds, is maintained relatively high and species also move a long distance to colonize a new site, in the tropical forests, also short distances between fragments may represent true barriers for movements of animals. Obligate army ant followers disappear within 2–3 years from isolation. Insectivorous birds are heavily affected by isolation. On the isolation, a fundamental role is played by the surrounding vegetation after logging or agricultural use. In Amazonia, woodlot surrounded by Vismia (Hypericaceae), a dominant vegetation after forest removal by burning and cattle pasture, are considered more isolated by birds than plots surrounded by Cecropia (Cecropiaceae), a vegetation that occurs where forest is removed by logging but not by burns (Stouffer & Bierregaard, 1995). Tropical rainforests cover less than 7% of the planet landmass but support half to two thirds of plants and animals species of the earth. The sensitivity of tropical forests to fragmentation has been investigated by Bierregaard et al. (1992) during the Biological Dynamics of Forest Fragments Project. Distance effects, fragment size, edge effects, and biotic changes were some of the more important issues. 80 m of

5.3 Fragmentation

199

nonforest is enough to be a barrier for mammals, insects, and understory birds living in a fragment. 1, 10, and 100 hectares are the size of fragments at which most of the species of insects, mammals, and birds are sensitive. After fragmentation, birds move more across fragments and also the density increases at least after 200 days after logging. This clearly shows how birds have the capacity to move away from fragments. The hypothesis that small patches contain subsets of organisms of larger ones was discussed by Cutler (1991) studying mammals in Great Basin of western North America. Actual mammal composition is the result of selective, deterministic extinction of species of originally richer fauna. In Florida keys, the deforestation is producing a decrease in birds. For some sensitive species, the loss of habitat exceeds the actual loss of deciduous forest. This means that habitat requirement for sensitive species is not limited to area size but also to surrounding characters (Bancroft et al., 1995). Fragmentation in shrub-steppe habitats negatively affects the breeding distribution of shrub-obligate species (Knick & Rotenberry, 1995). The difficulties in shrubsteppe restoration may cause irreversible loss of habitat and negative consequences at long-term scale on shrub-obligate species. In old-growth montane forests on Vancouver Island (Pacific Canada), the effect of fragmentation of bird assemblages was less dramatic than in other areas (Schieck et al., 1994). This probably depends on the fact that old-growth forest evolved within heterogeneous montane forests. It could also depend on the lower contrast between old-growth and logged areas compared with forests and agricultural/urban areas from which most of fragmentation studied were carried out. Tscharntke (1992) has found that fragmentation of Phragmites habitats is producing severe effect on insects and birds. An important point stressed by this author regards the significance of habitat fragmentation that is not limited only to the size of patches but also to the mean shoot diameter. Further, small patches of Phragmites receiving more light have greener leaves than individuals in large-dense patches and some species of aphids are positively correlated with fragmentation. The increase in farmlands has dramatically reduced the size and distribution of native grasslands. This fact has determined the decline of many birds (Fig. 5.14). The fragmentation of midwestern grasslands has impoverished the breeding bird communities (Herkert, 1994). Five species (Savannah sparrow, Henslow’s sparrow, bobolink, and eastern Meadowlark) have been found to be sensitive to patch size. Fragmentation of suitable habitat, in this case traditional agricultural land, increases the risk of extinction in Belgian primrose (Primula vulgaris) populations as documented by Endels et al. (2002). They argued that long-term survival of primrose is at risk if land use practices change drastically in the near future. In boreal European forests, landscape fragmentation has been observed to negatively affect the breeding success of black grouse (Tetrao tetrix) and capercaillie (Tetrao urogallus) either in terms of forest rarefaction as in terms of destruction of older forest (Kurki et al., 2000) although are factors like a decrease in reproductive success to be carefully considered (Jahren et al., 2016).

200

5 Emerging Processes in the Landscape

Fig. 5.14 Bird species richness of grasslands plotted against the area of fragments. Symbols represent number of species found per site in 1987–1989 censuses. (From Herkert, 1994, with permission)

Mosaic context is a further element that can reduce or mitigate the effects of fragmentation. Bayne and Hobson (2002) have observed that the apparent annual survival of male ovenbirds (Seiurus aurocapillus) is lower in small forest fragments (90% over the past 100 years as reported by Fuhlendorf et al. (2002). Although many aquatic birds such as waterfowl have resilient capacities to buffer the habitat losses, the vanishing of large natural breeding habitats tremendously increases the risk of predation. Pasitschniak and Messier (1995), using artificial nests placed at different distances, simulated predation risks at the edges. The predation Fig. 5.18 Forest size and predation rate in (back square) ¼ large forest tracts, ○ rural fragments, • suburban fragments. (From Wilcove, 1985, with permission)

206

5 Emerging Processes in the Landscape

risk of a nest of a waterfowl is related to distance from edge in dense nesting cover, but no edge effect has been observed in idle pastures or delayed hay fields. This could depend on the abrupt edge between these cultivations and the more accessibility by predators. So, the edges in a man-made landscape could be less important than vegetation structure. The argument has been differently questioned by other authors. Predation of ground nests in prairies fragments in Missouri was studied by Burger et al. (1994). The artificial nests in prairies 1 million (United Nations, 2019). The urban landscape, in the majority of developed countries, is represented by urban and peri-urban areas. The two differ in terms of structure and functions. An urban area is a built-up urban area or urban agglomeration (cities), and a peri-urban area is a labor market (and a housing market) connected to rural areas (and smaller urban areas) to the outside. Urban landscape, according to an ecological perspective, could be perceived as an alien environment full of canyons (house walls) and soils compacted and sealed with asphalt where life cycles are really reduced supporting few ecosystemic processes (Brown et al., 2017). Water drains quickly to disappear from the impervious surface into the underground sewerage networks (McGrane, 2016). Many studies have compared urban environment to an ecosystem. This image is very attractive but often is far from the reality. Urban system could be compared to the gears of a watch. The single gear, for instance a owerbed or a city park, to be in function requires a lot of human stewardship (energy, working time, material, nutrients). The vegetation of a owerbed without providing irrigation can dry in short time. Trees along an avenue have the root system drowned into cement and have several wounds on branches periodically pruned to assure a functional silhouette to traffic and settlements. The leaves fall in autumn but without benefit for soil dwellers because removed and taken to landfill. Distribution of green space is planned according to aesthetic priorities and the linear structure, apparently green corridors that border the streets, cross the cities without significant contribute to biodiversity maintenance.

9.2 Urban Landscape

341

Climatically, urban areas are heat islands where the re ection of cement and the absorption of sun rays by road asphalt and roofs create strong daily and seasonal oscillations of temperature and humidity level with thermal extremes. Ultimately, urban landscapes despite the efforts to be described under an ecological perspective like an ecosystem, have several limitations in terms of autopoietic functionality requiring a continuous human stewardship and input of nonsolar energy.

9.2.1

Main Characters

The urban landscape is characterized by impervious surfaces, buildings, roads, energy facilities, and sewer networks that create a highly interconnected technological system. Urban areas can be considered according to a hierarchy of spatial scale a complex mosaic of island at different content of naturalness. At larger scale, urban landscape is a prevailing matrix of cement in which are interspersed small green spaces whose connectivity is low, creating true insular nature elements in a matrix of buildings and roads. At smaller scale, parks, woodlots, gardens are patches whose functionality is largely deteriorated but in which some level of biodiversity is preserved, although often a sink dynamic (sensu Pulliam, 1988) is prevailing. In some regions, well-developed green spaces created inside the cities, returns a patchy multifunctional mosaic of small natural systems with low ecological functionality.

9.2.2

Extension and Human Population

The spatial extent of urban landscapes are increasing around the world (http:// atlasofurbanexpansion.org/). In 2010, 4231 cities in the world had 100,000 or more inhabitants. The spread of urban development is generally made at the expense of surrounding farmlands and more rarely to forests or wild areas. In 2015, 744,000 km2 of our planet were occupied by urban landscapes compared with 349,000 km2 just in 1992. In 1950 just 30% of global population was living in urban areas (World Urbanization Prospects 2018), but by 2019, 55% of the human population (4 billion people) (https://ourworldindata.org/urbanization) lives in urban areas (http://www. demographia.com/db-worldua.pdf). The United Nations expect that 68% of the world population (7 billion people) will live in urban areas by 2050 (https://www. un.org/development/desa/en/news/population/2018-revision-of-world-urbanizationprospects.html).

342

9.2.3

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Energy

A urban landscape is the most energy demanding system. It is the only landscape that for its existence requires a continuous transfer of energy and matter from natural ecosystems. This represents a formidable sink that requires a permanent supply of natural resources from external systems (Papa et al., 2014), and due to this unsustainability it urgently requires new energetic strategies (Asarpota & Nadin, 2020). Compared to natural ecosystems, the urban landscape has only a modest possibility to reuse wastes, at best, where the distribution of energy (electricity) largely depends on the overall quality of the urban landscape creating a mosaic of energyusable patches (Broto, 2017, 2019). Otherwise, all wastes must be removed from roads and buildings to be placed into landfills far from city centers or burned to produce energy.

9.2.4

Threats

Several threats menace urban landscapes, such as follows: Intensive urban growth can lead the greater poverty of people. High pollution as consequence of intense use of fossil energy. Elevated lead level in urban air from automobile exhaust. Health hazard because of the large volumes of uncollected wastes. Flash ooding risk as consequence of urban development. Reduction of urban tree cover affected by pollution and physical barriers to root. Loss of habitat and food sources associated with toxic substances that reduce the success of animal populations. Air, soil, water, light, and noise pollution are some of the most important causes of concern for urban inhabitants. In particular, air pollution is one of the major causes of health problems to urban inhabitants, especially for low-income people. Air pollution kills 7 million people worldwide every year (VHO.int). The presence of artificial lighting in urban landscapes is problematic for the night sky brightness, posing difficulties for human health and animal misfunctioning (Russart & Nelson, 2018) with consequences extended also to neighboring areas. It is known that 80% of the world and more than 99% of the US and European populations live under light-polluted skies (Cinzano et al., 2001; Falchi et al., 2016). Constant exposure to artificial light causes significant disruptions to the biological clocks and to behaviors of humans and animals alike. There are urgent appeals to develop lighting strategies that reduce ecological impact and maintain human utility, comfort, and safety (Gaston et al., 2012).

9.2 Urban Landscape

343

Noise exposure is one of the most pervasive of all urban-centric pollutants. Noise in urban landscapes causes severe health issues to billions of people, ranging from sleep disfunction to heart disease. Noise assessment is a necessary action before every planning scheme (Morillas et al., 2018) regarding sound propagation and amplification mechanisms linked to shape of building, street orientations, positioning new parks acting as noise buffer, etc. (Vladimir & Madalina, 2019).

9.2.5

Trends

Due to the high economic value of cities that produce >80% of the gross domestic product, we expect a continuous immigration of people from rural area to urban cities in the next several decades. For instance, it is expected that more than 1.1 billion people will move to Asian cities to find better life opportunities (UN, 2019). However age seems to be an important variable in population movements. For instance, in Spain, Montalvo et al. (2019) have found that aged population increases with spatial accessibility (roads and services) and decreased with rurality and a depopulation of a territory. The rapid growth of urban landscapes produces fragmentation of natural habitats that favor the invasion of exotic species that in turn affect biodiversity (Zambrano et al., 2019).

9.2.6

Level of Sustainability

Urban landscape ecology goal is to use scientific information to preserve and to restore biodiversity in urban ecosystems (Dearborn & Kark, 2010; Faeth et al., 2011; La Sorte et al., 2014; Norton et al., 2016). Urban nature is increasing in importance and concern and more demand is issued for the ecosystem services provided just to small fragment of nature (Chiesura, 2004). It is a moral and ethical duty to transform cities into more sustainable areas for the future. Sustainability in urban landscape is today an urgent issue to face because the problem is exacerbated by the combination of climate change that represents the greatest challenges to society of the present time (Wu, 2014; Kabisch et al., 2017). This can be achieved by acting on two different strategies. The first is based on improving the living conditions of people: better salaries; better health care; reduce air, soil, noise, and light pollution; and imposing new rules of social partnership. The second strategy is based on the improvement of urban greenery. Depending on the climatic zone in which cities are located, artificial warming in cold regions and artificial cooling in warm regions are the two faces of the same coin, for both is requested a tremendous amount of energy. The electricity required for cooling and heating is in dramatic increase especially in tropics and subtropics areas (Waite et al., 2017).

344

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Fig. 9.1 An example of urban park where tress are planted at a regular distance each other imitating an open savanna

Urban parks have an important role to play in finding local solutions for some environmental problems. Their functioning is central to the life of modern cities; parks are seen as valuable assets. Although not directly profitable business, the presence of parks in a city context offers space for cultural activities (art, music, performances, public debates), provides recreation, increases community development, maintains community’s history (heritage), and so on. Parks encourage individual and community health and wellness and are vectors of economic development. In particular the presence of parks in some sectors of large cities encourages to invest for new commercial and residential activities coming from the benefit to have parks in a core area. Urban parks are an extraordinary tool to connect people with nature, offering eventually protection to rare or endangered species. Urban parks are also important source of wellness and spaces for walking, cycling, using wheelchair, in-line skating, etc. (Ellis & Schwartz, 2016) (Fig. 9.1). Urban parks attract plants and animals; mitigate the effects of air and noise pollution; produce benefits such as relaxation and creative thinking; may be appropriate areas for sport and physical activities; are place for social cohesion in which to organize concerts, exhibitions, and leisure activities; and finally can be space for art exhibitions and educative performances. In particular, urban parks are open spaces in which to teach and learn about natural processes as well as developing important spontaneous or managed educational training events, economically convenient for every age class of urban societies. In many cities, especially in North America, the matrix of residential yard affects urban biodiversity. For instance, Belaire et al. (2014) for the Chicago area (Illinois, USA) have found that the aggregation of residential yard has a greater importance

9.3 Farming Landscape

345

for birds than environmental characteristics at the neighborhood or landscape scale. The percentage of deciduous on evergreen trees as well as the percentages of trees and shrubs with fruits or berries are positive factors for encouraging bird diversity. On the contrary, the density of cats and dogs has a negative impact favoring nonnative birds. As cities extend and occupy more and more territory, they inevitably end up incorporating green areas, remnants of natural systems like small lakes, bogs, stretches of rivers, woodlots, etc. These natural remnants contribute to biodiversity maintenance in a hybrid nature (Farina, 2019). Isolation of green spaces in urban context is a predominant process. Unlike of what happens in rural areas, green patches in the cities are more stable and their use is regularized. In fact, city parks are the result of a design that should persist for long time. The biological structure is persisting with the contribution of an active human maintenance that substitutes resilient to resistant dynamics. In green areas converge people, plants, and animals, and their “pacific cohabitation” is possible because resources are differentiated; however, human intrusion and passive involuntary disturbance may reduce success in many species, transforming these areas in pseudo-sink systems (Watkinson & Sutherland, 1995). Greens areas offer immaterial and therapeutic resources for people (Winchester & McGrath, 2017). Urban landscape is one of the more “semiological” areas on the Earth, where iconic symbols are spread everywhere along roads, in front of buildings, on the vehicles, etc. This symbolic jungle accompanies and assists the inhabitants all day long. Movements of people, goods, energy, and information can be done very fast inside urban landscapes thanks to surface and underground collective facilities (trains, taxis, subways) and to a capillary electrical, telephonic, heating networks. Urban landscape often occurs in biodiversity hotspots around the world, and this biodiversity requires a strong effort of conservation, assuming an important goal like the conservation of remote areas. An extra value in this action is represented by social and educational benefits (Miller & Hobbs, 2002). However, urban nature is increasing in importance and concern and more demand is issued for the ecosystem services provided just to small fragment of nature (Chiesura, 2004).

9.3 9.3.1

Farming Landscape Introduction

Farming landscapes may be defined as a landscape in which crops and livestock are artificially concentrated with the goal to produce food and related services. Today about 24% of the Earth’s terrestrial surface is occupied by cultivated systems. Due to the extreme heterogeneity of farming systems around the world, a synthesis about the main character of the farming landscape is not easy to depict.

346

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

A generic classification could be the distinction between plane and hilly or montane farming systems. The most productive areas of the world are the plains where cultivation is still economic thanks to the use of mechanization. Hilly and montane farming may be very specialized for crop varieties but at low economic income and with high costs to produce crops and maintain soils. Spatial heterogeneity represents a drive of all the ecological processes. In a farming landscape, such heterogeneity is not only in space but also in time (temporal heterogeneity due to crop rotation cycles). This double heterogeneity is an important element of uncertainty in farming landscape, and as suggested by Vasseur et al. (2012), requires an incorporation of more natural system to accept the challenge of biodiversity conservation. At the same time, the synchronization between human crop production and the natural rhythms is a prerequisite to assure the preservation of biodiversity. Farming landscape is a high dynamic system that can be reduced to four main models: (a) Increase of cultivation surface (extension dynamics for economic reasons by intrusion in forested or in rangeland areas) producing forest fragmentation and biodiversity losses. (b) Reconversion of farming areas with new typology of agricultural practices (longterm turnover of cultivation). (c) Increase of cultivated surfaces by reclamation of neglected urbanized areas. (d) Reduction of cultivated surfaces as a consequence of land abandonment for social/economic reasons, desertification by climate change, spread of a secondary succession, desertification.

9.3.2

Some Farming Landscape Characters

The farming landscape has been shaped, at least in lowlands, according to a geometric regular design to facilitate the agricultural practices. The Roman centuriated landscape is an example that survives at least from 2000 years in southern Europe (Palet & Orengo, 2011). The patchiness of the farming landscape largely depends on the cultural/social history of a territory. Large estates produce a more coarse mosaic of fields; individual family estates produce a fine-grained mosaic (Fig. 9.2). This last asset may favor a strict relation between soil fauna and landscape heterogeneity. For instance, soil fauna assemblage presents a strong turnover of species along a human-created soil gradient. High diversity of soil fauna is commonly found in heterogeneous landscapes. Collembola and Lumbricidae are positively correlated with habitat richness at the landscape scale, specifically to plant diversity and mosaics of forest and open field habitats (Vanbergen et al., 2007). The regularity of farming landscape contrasts with the irregularity, often fractallike shape of natural landscapes. The linear shape of fields is the result of the usage of

9.3 Farming Landscape

347

Fig. 9.2 Example of fine-grained cadastral division in a Mediterranean mountain farming system (Vinca, Fivizzano commune, Italy). The numbers indicate the properties.

working animals (until the recent past) and especially of semiautonomous machineries (present time). Irrigated versus not irrigated farming and sedentary versus nomadic farming are the two commonest patterns worldwide. Nomadic farming is a model applied by people that are not permanent residents in an area. The pre-Columbian human footprint in Amazonia has been the result of a shifting mosaic (nomadic) practices (Barlow et al., 2012). Modern farming is par excellence the contrary of nomadism and has been a very successful model in the evolution of human societies.

9.3.3

Farming and Reclamation in Tropics

The lesson from the past in the Tropics may be useful to mitigate or incorporate severity on the forest biodiversity conservation. The application of a good model to

348

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Fig. 9.3 Model of organization of the farm mosaic and its evolution in a agroforestry system. Green parcels represent areas planted with trees. Orange areas are covered by crops or pasture. (From Paul et al., 2017, with permission)

reclaim tropical forests after logging and fire impacts is strategic for the survival of several life forms in these tropical areas. Conservation of tropical forest could be achieved by creating large nature reserves without human population in the interior. In reality, people are present everywhere in tropical forested areas and it is not easy to promote such a policy. Moreover, the presence of small and sparse community of indigenous people inside the forest may have positive results for many species of plants and animals due a moderate disturbance regime obtained by local farming (Schwartzman et al., 2000). Agroforestry model, where a combination of crops and cultivated trees coexist, seems more sustainable and economically efficient (Paul et al., 2017) than a policy to separately maintain these two components (Fig. 9.3). This model is well represented by the coltura mista in the Italian Apennine landscape (Farina, 2018). A similar model based on home gardening, fallow practices, and forest management to produce timber and nontimber resources using domesticated, semidomesticated, and wild germ plasm has been adopted by the indigenous people in neotropics. Home gardens are a mixture of trees, shrubs, vines, and herbaceous plants that are maintained as an annex of the house. These nontechnological practices in neotropical forests escaped from observation of untrained eyes for many years. Nature seems intact because there are no signs of bulldozer, trails, road, vegetation planted on line. A shifting-cultivation habitat mosaic has been described by Andrade and RubioTorgler (1994) in Colombian Amazon. Abandoned crop fields can recover after a secondary succession, and if the size of such patches are modest and mimic the natural disturbance occurring in the forest we expect, at least for birds, beneficial effects.

9.3 Farming Landscape

9.3.4

349

Modern Versus Traditional Farming Systems

In every ecoregion of the Earth, see Bailey (1998) for a well-illustrated introduction, at least two different models of farming – modern and traditional – are adopted. Usually modern farming is confined to plain or in terrains with gentle slope (rolling hills) and into con ict with nature conservancy and biodiversity (FAO, 2011). This system requires large homogeneous areas for crops like corn fields, sugar plantations, cotton fields, etc., and the access to machineries to manage soil and species of hybrids cultivars often improved by genetical modification. Modern farming is less labor intensive and involves a massive use of machinery for managing soil and to distribute or spray fertilizers and pesticides. In the modern farming, multiscale factors that operate between nations, between farms and regions, between fields, and within fields are responsible of the observed homogeneity (Benton et al., 2003). The traditional farming is a system that has an important role in biodiversity conservation. Heterogeneous farming system, like “coltura mista” in temperate Mediterranean is the key to assure or to recover biodiversity (Farina, 2018). In the following sections we succinctly describe a few examples of traditional farming and we discuss their importance in terms of biodiversity conservation.

9.3.4.1

Dark Earths: A Contribution to the Amazonian Landscapes

Also called Terra Petra de Indio or Terra Mulata (Brown Earths), Amazonian Dark Earth (ADE) (Lehmann et al., 2007; Iriarte et al., 2020) is a soil of dark color that can be found in Amazon area and that is the result of artificial manipulation of soil by pre-Columbian populations (from the start of Holocene) to increase the fertility of soils (Woods & McCann 1999; de Oliveira et al., 2020). It results from a mixture of charcoal, bone, broken pottery, compost, and manure, and is considered like the legacy of the former settlers sites of pre-Columbian farmers. This practice has been dated between 450 BCE and 950 CE. Soil is about 2 m in depth and the process of regeneration is of 1 cm per year (Erickson, 2003; Lehmann et al., 2003). The investigation on the landscape characterized by this soil is particularly important because it sheds light on past events that have strongly affected the biodiversity of tropical regions and that can help for future management at low impact. This model that was diffuse in Amazonian basin is an example of human stewardship, of ecological niche construction in this case farming soil, and represents a possible model to develop soil fertility in contemporary tropical agriculture. The application of this model, based on the sequester of carbon, is a win–win opportunity in times of climate change and improvement of Amazon farming capacity, thereby reducing the demand of new farming soil that could obligate new deforestation.

350

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Amazonian Dark Earths (ADE) differs from the soils that they develop. ADE has about three times the soil organic matter, and the content of charred residuals of incomplete combustion (carbons and charcoal) is 70 times greater (Glaser et al., 2001). Form and depth of deposit seem linked to the different settlement patterns, population size, and settlement duration. This effect produced a mosaic of ADE and an example how soil patchiness has been created by human stewardship. In Fig. 9.6, it is reported the evolution of ADE associated with multiple longhouse communities and urban house lots (Erickson, 2003). The addition of biochar (charcoal produced by pyrolysis of biomass in a deficit of oxygen) in the soil is a well-noted amendment practice that increases structural stability and fertility of soils, sequestering a great amount of carbon and nutrient retention. For this, the practice to amend soil with biochar creates new typology of soil called Terra Petra Nova (Lehmann, 2009). Similarities are reported for soils described in some regions of Central and West Africa, and this opens new perspectives about the utilization of old human-modified soils to develop a modern agriculture in this continent (Fairhead & Leach, 2009).

9.3.5

Agroforestry in the Tropics

Agroforestry is the integration of trees and other large woody perennials into a farming system by the maintenance and stewardship of existing trees and their active planting and maintenance. Agroforestry means intermediate-intensity land-use forms, where trees still cover a significant surface of landscape with in uence on microclimatic, matter, and energy cycles, and biotic processes. Agroforestry is defined by the World Agroforestry Center (ICRAF, 2000) “as a dynamic ecologically based natural resource management practice that, through the integration of trees and other tall woody plants on farms and in the agricultural landscape, diversifies production for increased social economic, and environmental benefits.” Agroforestry may be an optimum tradeoff between the necessity to produce food and to conserve nature at the same time and may be a model system for tropical ecology (Greenberg et al., 2008). An agroforestry system offers several ecosystem services like soil, water, and biodiversity conservation, but the system is working well at large scale and the landscape scale seems appropriate to assess the importance of such a system. In Africa, farmers created park-like (savanna) landscapes retaining tall trees between crop fields (Boffa, 1999). We propone few examples of agroforestry in tropical areas where the maintenance of tree coverage is strategic for the biodiversity.

9.3.5.1

Cacao Plantations

Cacao (also called cocoa) (Theobroma cacao) is a plant cultivated in humid lowland tropics by small farmers under a tree canopy that assures a high level of biodiversity (Rice & Greenberg, 2000; Faria et al., 2006; Wanger et al., 2009; Batsi et al., 2020).

9.3 Farming Landscape

351

Three systems are utilized: (a) The “rustic model,” where cacao is planted under the shade of thinned primary or old secondary forest. (b) “Planted shade model,” where cacao is planted under a polyculture for commercial utilization. (c) “Technified model,” where plants have no tree cover but receive directly sunrays. The shaded cacao plantations offer important resources in the conservation of many invertebrates like ants and beetles, but the presence of natural forest adjacent to shaded cacao agroforests remains of primary importance for their conservation (Bos et al., 2007). This system, despite the economic thrusts, is resisted because farmers are more confident of the shaded system that reduces risk factors than a reduction of tree cover (Johns, 1999). However, climatic change with an increase of drought periods represents a serious menace to cacao production in some areas of Brazil (GateauRey et al., 2018). The importance of this cultivation for biodiversity requires precise guidelines to preserve cacao landscapes. Recently Schroth and Harvey (2007) have presented the following recommendations: 1. “The conservation of existing forest remnants in production landscapes should be of highest priority.” 2. “Where traditional, high diversity agroforests still exist, special efforts should be made to protect them from intensification and simplification of the shade canopy, and from conversion into other land uses.” 3. “Where cocoa agroforests have already been simplified, programs to diversify these systems both through tree planting and facilitation of natural regeneration will have positive impacts on biodiversity.” 4. “Efforts to maintain or increase the biodiversity of cocoa production landscapes need to consider also non-cocoa agricultural areas within the landscape.” 9.3.5.2

Coffee Plantations

Coffea is a genus of owering plants belonging to Rubiaceae family. These plants are native of tropical Africa and Asia. Some species (approximately 120 species) are cultivated to produce coffee beans. Coffea arabica and C. canephora are the main species used to produce coffee (60–80% and 20–40% of the world production respectively). Coffee agroforestry seems a promising method for sequestering carbon and biodiversity conservation (Dossa et al., 2008). For instance, in Mexico at least five typologies of coffee cultivation are in use, ranging from the coastal slopes of the central and southern parts of the country: Two typologies of traditional shaded

352

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

40 35 30 25 20 15

Rustic

10 0 40 Shaded cof fee system

Traditional

5

35 30 25 20

Traditional polyculture (”cof fee garden”)

15 10 5 0 15 10

Commercial polyculture

5 0

5

Shaded monoculture

0 Unshaded

Modern

10

5

Unshaded monoculture

0

Fig. 9.4 Five coffee-growing systems utilized in Mexico. (From Moguel & Toledo, 1998, with permission)

agroforests (with native trees), one polyspecific shaded system, and two “modern” systems (shaded and unshaded monocultures) (Moguel & Toledo, 1998) (Fig. 9.4). The traditional shaded coffee is cultivated by community-based growers, in prevalence belonging to some indigenous culture groups. This system allows the maintenance of biological richness for groups such as trees and epiphytes, mammals, birds, reptiles, amphibians, and arthropods. For instance, in Mexico, 60% and 70% of coffee areas are under traditional management, and are distributed along a biogeographically and ecologically strategic elevational belt where tropical and temperate elements enter into contact.

9.3 Farming Landscape

353

Approximately 10% of priority regions (14 on 155) for biological conservations are near or inside traditional coffee-growing areas. This means that in the future a modification toward a more simplified cultivation of coffee may represent a great risk of extinction for many species. The expansion of the coffee production could reach in few years 2.7 million ha of modern coffee cultivations in highly biologically diverse countries, with severe threat for biodiversity especially in the areas where traditional coffee plantation represents the last refuge for biodiversity (Perfecto et al., 1996; Potvin et al., 2005; Jha et al., 2014). It is really important to recognize that traditional coffee farms organized in agroforestry systems may contribute to greenhouse gases mitigation and biodiversity conservation and to increase climate resilience and ecosystem stabilization. Public awareness about the benefit to manage coffee in forest has been confirmed in West Java (Indonesia) in a recent study (Supangkat et al., 2018). This cultural landscape represents, especially in poor countries, more than an agroforestry practice but also a tool to regulate and balancing the social and economic development of societies.

9.3.5.3

Oasis Landscape

Oasis are areas of variable size in which the availability of fossil water allows vegetation to grow and people to cultivate the desert for some extension (Fig. 9.5). In areas where water is limited like in semi-arid lands and deserts, farming is developed where there is the availability of springs and long rivers. In particular, in deserts, oasis are green areas that benefit for the presence of underground water in some way utilized to irrigate and for domestic use. Despite the limited extension, oasis landscape is strategic for human population and biodiversity of arid regions (Amuti & Luo, 2014). For instance, trans-Saharan migratory birds oasis are stopover landscapes essential to their survivals (Chiheb et al., 2021) and to complete their migratory impulse and migratory strategies (Safriel & Lavee, 1988; Lavee et al., 1991; Chernetsov et al., 2007). Oasis landscapes play a central role of food security and ecosystem services in arid regions. However, the recent increase of cultivation in the oasis, facilitated by the access to fossil aquifers, exposes this fragile and localized landscape to the risk of environmental crisis, reducing its sustainability (Liu et al., 2018). In oasis, a traditional agriculture is not for market, and extension of farming outside the oasis by irrigation using aquifer water remains a project as in the case recently described for Algeria by Hamamouche et al. (2017). The oasis model has been recently exported to plan “oasis city” where the water ows in the falaj canalization system, the traditional method for supplying water for irrigation and domestic use, were simulated to evaluate the amount of water necessary to maintain green areas (El Amrousi et al., 2018).

354

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Fig. 9.5 A satellite image of the Hotan Oasis, Taklamakan Desert, China. (From Google Earth)

9.3.5.4

Palm Oil Plantation Landscape

The oil palm (Elaeis guineensis Jacq.), a plant native of West Africa, requires a tropical climate and abundance of water. This crop is a relatively recent cultivation in the tropics, the impacts of which are overwhelmingly negative on biodiversity and local peoples (Vijay et al., 2016), causing the replacement of tropical forests in 20 countries (Vijay et al., 2016).

9.3 Farming Landscape

355

Palm oil is an important product used in food, cosmetics, cleaning products, and biofuel, and represents an important item for global food security and economic development. Palm oil plantation is in great expansion (increased 15-fold between 1984 to 2014 from 4.5 million tons to 70 million tons, with an annual increase of 1.7%, and for this is one of the main drivers of land-use change and deforestation in the tropics. Oil palm cultivation is extended on an area of 18.7 million hectares worldwide (as of October 2017). The increase production provides significant economic earnings for smallholders and corporations. Most (85%) of global palm oil supply comes from Indonesia and Malaysia, followed by Thailand, Colombia, and Nigeria. Unfortunately the cost of negative externalities is quite high. Business-as-usual scenario of industrial palm oil plantation is detrimental for biodiversity. The diffusion of this plant in tropics affect 54% of threatened mammals and 64% of threatened birds globally (Fig. 9.6). The reason of the diffusion of this plantation depends on a great yield more than all other oil crops and cannot be replaced without the risk of a local economic crisis. Due to a great impact on biodiversity, palm oil needs to be produced more sustainably possible by avoiding deforestation and to reduce the nonfood palm oil use (e.g., biofuel). The expansion of this plantation, like in Indonesia, requires a great attention to maintain a sustainable farming. For instance,

Fig. 9.6 Negative effect of palm oil plantation on ecosystem functions. (From Dislich et al., 2017, with permission)

356

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

on the island of Borneo, at least 50% of all deforestation between 2005 and 2015 was caused by oil palm development (https://www.iucn.org/resources/issues-briefs/ palm-oil-and-biodiversity). Oil palm has a great ability to provide food and raw materials, alleviating poverty in tropical areas. Oil palm expansion is a major cause of deforestation and degradation of natural habitats in parts of tropical Asia and Central and South America, behind cattle ranching, and local and subsistence agriculture altering the delicate balance between functional groups like ants and termites that are organisms that perform key roles in predation, decomposition, nutrient cycling, and seed dispersal (Luke et al., 2014). About 11 of 14 ecosystem functions of oil palm plantation are lower when compared with forests (Dislich et al., 2017). Unfortunately, its impact on biodiversity has created a great concern worldwide. Less is known about the effect on the landscape in terms of water dynamics. But in a recent research, Manoli et al. (2018) have shown that young plantations produce a decrease of evapotranspiration resulting hotter and drier climatic conditions. Mature plantations (age >8–9 years) have a higher gross primary production and high transpiration (+7.7) than the forests that they have been replaced. Oil palm plantations at a mature stage are highly productive at the expense of water consumption. To mitigate the dramatic impact of extensive palm oil plantations on biodiversity and local culture, Azhar et al. (2017) suggest a smallholder production that is more biodiversity-friendly and productions certificated to reduce the 15–20% of Indonesian oil palms produced without a legal basis, as recently reported by Purwanto et al. (2020).

9.4 9.4.1

The Cultural Landscape Characteristics of a Cultural Landscape

There are many definitions of Cultural Landscape according to the different perspectives with which human–nature interactions are considered. Here, we define a cultural landscape a landscape that has been changed due to long-term human disturbance by which it presents unique assemblages of patterns, species, and processes that are not created by nature (e.g., in subtropics the Japanese Satoyama, in temperate zone the Portuguese Montado). Farming landscapes certainly create cultural landscapes, especially in the case of traditional and ancient farming practices. However, it is necessary for us to consider whether the farming practices, like modern farming, enter into the realm of the people where their cultural practices, ethical ideology, and religious values interact with ecological systems. This process requires time but probably modern farming

9.4 The Cultural Landscape

357

systems, like some agroforestry systems in the tropics (e.g., shaded coffee and cocoa plantations), will become in the future cultural landscapes. The process requires time and stability in the process to be incorporated in the local communities culture. Cultural landscape re ects the interactions between people and their natural environment, and is a complex phenomenon with a tangible and an intangible identity (Plachter & Rossler, 1995). In 1991, UNESCO Secretariat proposed guidelines to identify a valuable and endangered cultural landscape: “be an outstanding example of a cultural landscape resulting from associations of cultural and natural elements significant from the historical, aesthetic, ethnological or anthropological points of view and evidencing a harmonious balance between nature and human activity over a very long period of time which is rare and vulnerable under the impact of irreversible change” (reported in von Droste et al., 1995). A list of cultural landscapes and relative references are listed in UNESCO web page: https://whc. unesco.org/en/culturallandscape/#4. Recently, the Council of Europe has proposed an European Landscape Convention (http://www.coe.int/t/e/Cultural_Cooperation/Environment/Landscape/) in order to promote the preservation of valuable cultural landscape across the entire Europe exposed to an unprecedented human disturbance regime. In the preamble, the Convention recognizes the role of the landscape as producer of “. . .local cultures and that it is a basic component of the European natural and cultural heritage, contributing to human well-being and consolidation of the European identity.” This declaration can be extended to all the landscapes across the world in which people have for long time interacted in a “sustainable way” with local bioecological entities and aggregations. Generally, these landscapes have a complex structure represented by a fine-grained mosaic in which physiotopes have been well localized and utilized in different way from agriculture, forestry, and pastoralism. Often slopes are transformed in “terracettes” that strongly reduce soil erosion and facilitate the agriculture practice, improving nutrient retention available for crops. Cultural landscapes represent a legacy for the majority of people. The “read” of a cultural landscape allows to trace back history and evolution of the relationships between people and nature providing scenic, economic, ecological, social, recreational, and educational opportunities for societies. The land abandonment and the substitution of such landscapes with hybridscapes pose at risk the heritage of the territories and reduce the opportunity of amenity and of naturalness. Cultural landscapes can represent a good model to test the possibility to expand humanity in natural environment without dramatic resource depletion and irreversible habitat perturbation (Antrop, 1997). This could represent a utopic perspective but the lesson that cultural landscapes around of the world is teaching should not be ignored. These perspectives are an urgent necessity and not an option to find a balance between human healthy gradual development and sustaining ecosphere (Halladay & Gilmour, 1995; Firmino, 1999).

358

9.4.2

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Interaction Between Natural and Cultural Landscapes

Any cultural landscape is the product of changes that have occurred in natural landscapes by long-term human in uence. The human intervention in a landscape can be observed directly by the modifications that the landscape experiences directly. Often human actions do not have a constant intensity and “efficiency” in landscape shaping but still remain in a “nongenetic” memory. For instance, the place’s name and the name of geographic emergences are an heritage that tells about the past use, traditions, and economies (Higham & Ryan, 2011; Gelling & Cole, 2014). For instance, Sousa and Garcia-Murillo (2001) have used the place’s name as an indicator of landscape changes in the Doñana National Park (Spain). A cultural landscape requires the human stewardship to be maintained, and for this reason it is fragile and comes back to a “natural” shape when human interference vanishes or is reduced. When land abandonment occurs in a cultural landscape, transformations in uence the land form. For instance, in hilly regions “terracettes” are progressively broken and fertile soil lost by water erosion and livestock trampling. Livestock are generally used after agriculture abandonment to maintain some openness in the landscape; this greatly concurs with trampling to terrace wall degradation. In the Mediterranean, horses, deer, and wild boar participate to the demolition of such landforms. Relevant differences can be found on comparing natural and cultural landscapes. The structure of a cultural landscape is often more patchy (fine grained) than natural landscapes. In cultural landscape, it is the intermediate level that is often lacking because ecological succession often is prevented by seasonal stewardship. In effect, it is not possible to make a generalization for the cases. Cultural landscapes have more linear structures such as hedgerows or open spaces than nature-shaped landscapes. But in some cases it is exactly the opposite. A desert oasis has more plants than a natural desert spring, and a mountain farmland has less trees than a mountain forest but more trees than a natural mountain prairie. There is an infinite number of types of cultural landscapes around the world but all have in common patterns created by local traditional land use, paying attention to the preservation of resources. However, the sustainability in such landscapes is a matter of time and can persist for a limited time lag that varies according to different regional history. Definitively, cultural landscapes are synonym of traditional farming landscapes, and more categories should be added to the list of UNESCO as proposed by Sirisrisak and Akagawa (2007). The Food and Agriculture Organization of the United Nations (FAO) since 2005 has designated 62 Globally Important Agricultural Heritage Systems (GIAHS) in 22 countries to which are added 15 new proposals from 8 different countries (https:// www.fao.org/giahs/giahsaroundtheworld/en/). Cultural landscapes are generally created by a feedback of trials and errors of sedentary human populations, but there are no reasons to exclude landscapes used by nomadic populations as in the Mongolian steppes. In this last case, sustainability is assured by a shifting grazing mosaic of livestock, and the stewardship is realized not

9.4 The Cultural Landscape

359

by digging the soil or pruning trees but by maintaining lightly livestock grazing, thus reducing trampling, and biomass consumption impacts. Some regions of the Earth, like the Mediterranean, can be considered in their entirety cultural landscapes. In the Mediterranean the long-term interactions of populations and environment have produced irreversible changes in biological and in ecological diversity. In many regions, land-use changes and/or land abandonment have produced heavy modifications in the cultural landscape and the disruption and the vanishing of these valuable landscapes is a concern for policymakers and stakeholders. The birth of countryside heritage centers is a timid reply to a diffuse problem of land management that in the present technological-oriented landscape seems of hard solution (Naveh, 1995; Antrop, 2005). Often the cultural landscape supports, at least in the Mediterranean Europe or in Japan, more species than a “wild” unmanaged landscape and in other cases the degradation of the cultural landscape is producing low-quality landscapes that support a lower species diversity (Farina, 1995). We have to be careful when we consider the “natural” value of the landscapes. In regions like the Mediterranean, the biodiversity has been depleted thousand years ago and there are very few species that can colonize empty “mosaic” niches. For instance, it is a recent story, the red-billed leiothrix, also known as Japanese nightingale (Leiothrix lutea), has spread its colonization across the Mediterranean Europe (Farina et al., 2013; Pereira et al. 2017). This species seems to compete in this region with blackcap (Sylvia atricapilla) at least for the acoustic habitat. Similar effects have been found by Sato (2006) in Japan. Traditional management of landscapes, is indicated by Moreno and Villafuerte (1995) as an important land practice to conserve large predators, especially in the Mediterranean countries. The value importance of reconnecting human culture with natural values to recreate a familiar valuable landscape has been discussed by Meurk and Swaffield (2000) for a rural area of New Zealand. These authors recognize the importance of reducing the gap between a mentality and a policy that maintains separate agribusiness lands (composing by exotic species) from natural protected ranges. This allows the creation of a new landscape in which local identity (cultural and biological) can be integrated with the need of a marketing agriculture. This conceptual model looks very interesting and could find applications in many other countries af icted by the oversimplification of landscape mosaics caused by agricultural intensification.

9.4.3

The Fragility of the Cultural Landscapes

Cultural landscapes are fragile with temporary ecological configurations, and require a continuous human stewardship to be maintained. During the land abandonment process, especially in dry regions, the transaction from an anthropogenic mosaic to a more natural mosaic is disturbed by human-caused fire occurrences. Fires are a very common disturbance factor in the Mediterranean basin, and in general in all dry regions around the world. Although most of the Mediterranean plants are fire tolerant

360

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

and many require fire to complete the life cycle, in most of the Mediterranean basin the increasing frequency of the human-induced fires dramatically reduces the capacity of the ecosystems to incorporate such disturbance. In many cases, burned areas have soils exposed to erosion in particular during heavy rain periods. How to control fires? In the Mediterranean, fires can be controlled by evaluating the economic income from woodland products. This seems a very simple remedy, and in many cases it is the reality. For instance, along the northern Apennines, there are numerous evidences that the fire frequency has collapsed in the last decades when, due to oil crisis, the logging activity is back again cheap and woodlands have been reevaluated as an important economic income. Logging activity in this region consists in clearing young stands (25–50 years) of variable size, generally between a few hectares to a maximum of 20–30 ha. In this manner, at one time a limited portion of soil is exposed, and in the following year a luxuriant secondary succession recovers the undergrown, preventing water ashing and loss of fertile soil. In this manner, dense homogeneous woodlands are transformed in a check board of woodlots at different ages, structures, and resource availability. The availability of cadastral maps that indicate location and land ownership allows to predict the heterogeneity of woodland in many parts of the northern Apennines. The confrontation between actual cadastral maps with of two- or three-centuries-old cadastral maps using GIS methodologies represents an important tool to investigate the landscape evolution.

9.4.4

The Cultural Keystone Species

Recently, Garibaldi and Turner (2004) have added a new category of keystone species to the ecological narrative: the cultural keystone species (CKS). These species play a special role in the local culture, and are used by people to broadly characterize habits, food, handcraft, language, typology of settlements, tradition, etc. On the concept of cultural keystone species, some criticisms have been address by Nuñez and Simberloff (2005). For these authors, some CKS are the result of an invasion or an introduction. Examples include the eucalyptus plantation in California. In these plantations, native plants, birds, and insects collapse and can be restored only after the removal of these trees. In some cases, cultural keystone species are the main drivers for the cultural landscape structuring. For instance, the sweet chestnut (Castanea sativa) is a CKS across the Mediterranean that requires the spatial arrangement of specific groves and infrastructures to dry and grid the fruits (Fig. 9.7). The same is for olive groves in Mediterranean region, coffee traditional plantation in tropical regions, or highland prairies in Apennines and Alps.

9.4 The Cultural Landscape

361

Fig. 9.7 Montane rural landscape characterized by sweet chestnut orchards (Castanea sativa) (Sassalbo, Fivizzano Commune, Northern Apennines, Italy)

9.4.5

Ethnographic Landscape

An ethnographic landscape is a place that has peculiar elements like artefacts, paints, engineering mechanisms of the use of resources like aqueducts, that are important landmarks for the human culture. Ethnographic landscape represents a subdivision of cultural landscape and is designed as a place that contemporary cultural groups consider meaningful, important for beliefs and history explicitly stratified (Evans et al., 2001; Anschuetz et al., 2002). Stonehenge is an example of ethnographic landscape.

9.4.6

Two Examples of Cultural Landscape

9.4.6.1

Satoyama

Satoyama is a traditional agricultural practice in Japan (Doshita, 2010; Koike, 2014). Satoyama, from the word “sato” village and “yama” mountain or hill, means an area between agricultural land and forest, and used to harvest firewood (Indrawan et al., 2014). For the Japanese people, this landscape has a symbolic and heritage value.

362

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Biodiversity in this system is well represented by wild plants and small animals. About 40% of Japanese biodiversity is conserved in such rural areas that are true hot spots for species preservation (Iwata et al., 2011; Fukamachi et al., 2011). Satoyama in the past was managed by individual families; today, due to population aging the system may have a collective management. Satoyama is characterized by multiple habitat patches created by periodic cutting, coppicing, mowing, harvesting, irrigating, draining, harvesting of firewood. These patches are composed by pine forests, coppice forests, oak forests, bamboo forests, thatch field, grasslands, shifting cultivation land, ploughed and crop fields, rice paddy and terraces, streams ponds, reservoirs, and settlements (Jiao et al., 2019) (Fig. 9.8). The future of Satoyama system is uncertain as any other traditional rural landscapes around the world (Fukamachi et al., 2001; Morimoto, 2011). Satoyama landscapes represent examples of landscapes that provide resources for people and wildlife. Unfortunately this system is at risk of deep changes that may have dramatic effects on biodiversity. The vanishing of connectivity between the different patches due to the spread of human infrastructures is one factor that increases the risk of a collapse of the biodiversity present in the Satoyama (Jiao et al., 2019). Variety of habitat and

Fig. 9.8 Schematic representation of the different successional stages in the Satoyama system. (From Jiao et al., 2019, with permission)

9.4 The Cultural Landscape

363

connectivity are both important factor to maintain this unique landscape (Katoh et al., 2009).

9.4.6.2

Montado

“Montado” is an agro-silvo-pastoral system of the Alentejo, a southern region of Portugal, with an extension of 800,000 ha. This system is similar to the Spanish dehesa that has an extension of 1.25 M ha in Extremadura and 700,000 ha in Andalusia (Olea & San Miguel-Ayanz, 2006). Cork (Quercus suber) and holm oak (Quercus ilex rotundifolia) are the dominants evergreen trees in this parkland system, where the cork production is the main yield. This system supports an important biodiversity and represents a multifunctional silvo-pastoral system (Costa et al., 2009). The specificity of montado is represented by high biodiversity, high aesthetics, and identity values, and is an important recreation area that attracts millions of tourists each year (Berrahmouni & Regato, 2007; Surová & Pinto-Correia, 2008; Surová et al., 2011). Montado is considered an agro-silvo-pastoral system of anthropogenic origin of high nature value and for this is included in annex I of the European Union Habitat Directive (92/43/CEE). The combination of agriculture, pasture, grazing, and animal stock are the ingredients that drive a high environmental complexity and that support a high biodiversity with an internal variability due to local heterogeneity, especially at the soil/climate/topography levels (Pinto-Correia, 1993; Serrano et al., 2020). From an economic point of view, sustainability, at least in the past, is based on a variety of products, not just cork, but also livestock, and other products such as wood and charcoal (Díaz et al., 2003; Pinto-Correia & Fonseca, 2009; Plieninger & Wilbrand, 2001). Soil is cultivated with cereals in long rotation with fallowing that combined with extensive livestock raising of cows, sheep, goats, cattle, and the Iberian pigs create a mosaic of great vegetational diversity (Gaspar et al., 2007; Plieninger, 2007). Trees that are outdistanced from each other to optimize soil water scarcity provide in autumn and winter acorns and leafy branches, when the herbage biomass is insufficient to sustain livestock. Tree crowns offer shelter from heat in summer and mitigate the cold days effect in winter (Cañellas et al., 2007; Moreno-Marcos et al., 2007). The montano is a cultural landscape in which human adaptation has created conditions to evolve with natural dynamics (Plieninger & Schaar, 2008). The plant diversity is quite high with a richness of 60–100 owering plant species per 0.1 ha (Díaz et al., 2003). This diversity is the result of human stewardship that after shrub clearing (every 3 years), has created a fine-grained mosaic of micro-environments (Canteiro et al., 2011) (Fig. 9.9). The tree crowns and their distribution represent important factors for the maintenance of many species of animals. In particular, the montado represents an

364

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Fig. 9.9 Conceptual model of the dynamics of montado. (From Ferraz de Oliveira et al., 2013, with permission)

important habitat for wild ungulates, like red deer (Cervus elaphus hispanicus) and wild boar (Sus scrofa) (Vargas et al., 1995). Particularly rich is the avifauna favored by the structure at parkland of montado (Tellería, 2001; Godinho & Rabaça, 2011). Small and medium-sized mammals like the endangered Cabrera vole (Microtus cabrerae) (Mira et al., 2008; Rosalino et al., 2009) and the wild rabbit (Oryctolagus cuniculus) represent food for the Iberian imperial eagle (Aquila adalberti), the Iberian lynx (Lynx pardinus) and necrophages like black vulture (Aegypius monachus) (Olea & San Miguel-Ayanz, 2006). These systems are under the processes of land abandonment and extensification that favor the invasion of shrubs and other oaks producing a strong competition with a reduction of cork and a significant increase of risk of forest fires (Pinto-Correia & Mascarenhas, 1999; Pinto-Correia, 2000). Climate change with prolonged and severe droughts facilitate the spread of wildfires that reduce in turn the mosaic diversity.

9.5 9.5.1

Freshwater Landscape Introduction

With freshwater landscape, we intend body mass of waters at low salt concentration (pond and lakes, stream and rivers, wetlands). These landscapes have a reduced dimension compared with other landscapes like forests or crop fields; however, the ecological role of these systems is fundamental for assuring and maintaining life. Lakes and wetlands in general are of reduced extension and are distributed in a really patchy way. Streams and their aggregation until large rivers are linear systems

9.5 Freshwater Landscape

365

with a considerable edge with other terrestrial systems. Streams can cross every type of landscape maintaining unique and distinct characters compared with the neighboring systems. The high contrast of these systems often creates ecotonal effects that are beneficial for biodiversity.

9.5.2

Stream and River Landscapes

Streams and rivers have owing waters that move according to an altitudinal gradient. The ecological characters of streams and rivers change during their journey. For instance, the amount of oxygen decreases moving from the source to the mouth. Streams and rivers form a complex system of connections and are very common in every part of terrestrial area of the Earth and also in very arid regions. The main character of these landscapes is connected with slope of terrain. The aspect is the mainly driver of these landscapes that have a reduced lateral extension but that can have a longitudinal fractal like structure ranging from linear to extremely convoluted shape. The drainage of stream and river landscapes is essential for the functioning of all terrestrial and aquatic systems. Streams and river landscapes metaphorically are the road by which water, nutrients, organisms, and pollutants move according to a passive owing from elevated terrains to lowlands. Streams and river landscapes represent the connection between terrestrial and aquatic systems. Their importance to assure connectivity and continuity in the detritivorous cycle is primary for several organisms. The interest for streams and river landscapes overpasses the ecological cycles and landing in the socioeconomic realm. The river continuum concept seems difficult to be incorporated into the landscape ecology paradigm where heterogeneity and patchiness are dominant patterns. A river, from the spring (headwater) to mouth in lake or sea, presents a continuum gradient of physical and biological conditions (Vannote et al., 1980). Biological communities change in a continuum of biotic adjustments with distinct processes of loading, transport, utilization, and storage of organic matter. Biological components cope the position and mean state of the physical system. In natural conditions, biological communities are expected to change their composition according to the position along streams and rivers. The importance of streams and river landscapes in many scientific discipline has created a huge literature on this subject. From a landscape ecology view point we have selected and shortly discussed two main themes: the fractal dimension and the riparian corridors.

366

9.5.2.1

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

The Fractal Dimension

The fractal dimensions of streams and river landscapes play a prevalent and important role for the control of subsurface and surficial movement of water, sediments, nutrients, and organisms. At every scale, streams and rivers have a strict relationship with the surroundings, and the patterns created by underground aquifers and percolating waters are re ected on the soil surface. The fractal methodology sounds important to understand the geological constraint on network evolution across a range of spatial scale (Phillips, 1993; Nikora, 1991; Rinaldo et al., 1993; Jahangiri, 2012). Streams and river landscapes have a fractal dimension because their patterns have a self-similarity at every scale of observation. The fractal approach to measuring the length of the rivers and their multiscale complexity (sinuosity index) represents a good indicator of the level of disturbance and dynamics (Montgomery, 1996). For instance, Tian et al. (2013) have introduced the fractal dimension values at the lower Yellow River (China), demonstrating that the wandering feature of this river is becoming weaker and that the river regime has been becoming stable by intervention of the Sanmenxia Reservoir and other interventions along the river. The study of the fractal structure has been applied to the suspended sediment time series at the Yellow River basin at Tongguan, Shanxi, China (Shang & Kamae, 2005). This character has important implications to better understanding the discharge dynamics and the transaction from streams to rivers.

9.5.2.2

The Riparian Corridors

A riparian corridor is a transition zone between the land, also known as the “terrestrial environment,” and the river or watercourse or “aquatic environment”. Riparian corridors are defined as ecotonal areas that include both terrestrial and aquatic ecosystems. From a structural point of view, a riparian corridor is a strip of vegetation strongly affected by the adjacency with abundance of water. From a functional point of view, a riparian corridor is a complex structure that is perceived differently according to the species. Riparian corridors perform important environmental functions such as the following: (a) Acting to provide bed and bank stability; reducing bank and channel erosion, and protecting water quality by trapping sediments, nutrients, and contaminants, thereby assuring channel stability. (b) Assuring diversity of habitat for terrestrial, riparian, and aquatic plants and animals. (c) Assuring connectivity between wildlife habitats. (d) Conveying ood ows and controlling the direction of ood ows. (e) Offering a passive recreational uses.

9.5 Freshwater Landscape

367

Riparian corridors have an important role in water and landscape planning, in the restoration of aquatic systems, and in catalyzing institutional and societal cooperation for these efforts (Naiman et al., 1993; Pinay et al., 2018). Despite their limited extension, riparian corridors have a large effect on the ecological ow and represent key habitats. The presence of vegetation along streams and rivers assures healthy conditions to aquatic ecosystems. The riparian vegetation is represented by plants that require abundance of water along the year. Temperature uctuations, increased sediments from streambank erosion and overland ows, less woody debris, higher water velocities, and increased pollution are the main consequences of the reduction of riparian corridors. Along a farming system, the nutrient cycle across the riparian fringes is of great importance for the “decontamination” of surrounding croplands and the release of high-quality water inside stream and rivers. Riparian corridors extend their functions down into the groundwater, up above the canopy, outward across the oodplain, up the near-slopes that drain into the water, laterally into the terrestrial ecosystem, and along the watercourse at a variable width. There is a vast terminology that refers with riparian zones (Fischer et al., 2001). Many recommendations have been produced with the goal to improve or protect water quality and wildlife (Fischer et al., 2000) and precise recommendations to preserve an adapt strip of vegetation are suggested. Riparian corridors have been recognized as important habitats for birds, but it is necessary that these strips are sufficiently wide. For instance, Darveau et al. (1995) recommend a width of at least 60 m. This dimension has been assessed larger of 100 m by Hodges and Krementz (1996). For instance, from the analysis of 117 riparian corridors of Maryland and Delaware (Keller et al., 1993), it was reported that the several areas sensitive to neotropical migrants increased most rapidly from 25 to 100 m. In South Carolina, Kilgo et al. (1998) suggest that a riparian forest should be at least 500 m wide. Spackman and Hughes (1995) recommend to manage stream corridors to take into account the species-specific requirements because a standard corridor width to conserve species is not enough to assure individual, stream-specific assessment of species’ distribution.

9.5.3

Ponds and Lakes

Ponds and lakes have a high variable range from few square meters to thousand square kilometers. They are distributed in every part of the Earth, and several are remnants from Pleistocene glaciation. The seasonality of ponds varies from few days to months. Lakes are more permanent, lasting hundreds of years or more before they are filled with sediments. Ponds and lakes have an intrinsic complexity due to the depth and waters. Littoral, limnetic, and profundal zones are the three main subdivisions. In lakes,

368

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

the temperature varies according to the climatic zone. A seasonality in temperature is observed from the bottom and the top, with a narrow zone of thermocline, where the water temperature changes rapidly. In this section, we will focus on ponds that represent an important tool to manage pollution and biodiversity in areas strongly in uenced by humans (e.g., urban, industrial, and farming systems).

9.5.3.1

Natural Ponds

In North Europe and North America, natural ponds or kettle holes are the result of a Pleistocenic landscape. They are considered hotspot from a biological and biogeochemical point of view. Due to the reduced dimensions, ponds are strictly dependent on water from the surrounding landscape. External drivers and internal dynamics create unique conditions in these heterogeneously distributed landscapes. Low-light tolerance and a differentiated nutrient use determine the composition of the autotrophic assemblage. The release of nutrients from sediments and the decay of macrophytes are the main factors of nutritional cycling. Ponds are source of CO2 to the atmosphere on an annual scale (Onandia et al., 2019). Denitrification and phosphorus release from sediments can create seasonal boost of primary production, as argued by Lischeid et al. (2018) after an investigation of 62 kettle holes in Quillow catchment in the Uckermark region close to Berlin (Germany). The shallow aquifer is connected with the distribution of kettle holes and the connectivity between ponds is assured by the system of drainage. In this way, pollution, especially in areas at high farmland density, can move on long distance. These authors have also observed hypoxic periods during which a denitrification and phosphorous release boost primary production with hydrophytes and helophytes. In forest landscape, ponds acidity is the main factor that controls diversity. For instance, in Poland, Spyra (2017) inspecting 26 forest ponds found that gastropod fauna is in uenced by values of pH. High levels of acidity depress the diversity, while neutral ponds support diversity.

9.5.3.2

Artificial Ponds

It is common practice in urban and farming landscapes to construct artificial ponds to manage surface waters, and to collect water and contaminants. Very common are the stormwater ponds that are man-made features generally located near developed areas to collect and treat runoff from impervious surfaces. These ponds are important sink for a temporary excess of water, reducing oods and soil runoff and at the same time represent reservoirs for rare plants and animals. In fact, as argued by Hassall (2014), stormwater ponds improve biodiversity, reduce pollution and negative effect of runoff, and finally have a strong educational role for people.

9.5 Freshwater Landscape

369

Often stormwater ponds are designed without paying attention to an ecological role, but their role to maintain biodiversity is relevant because, as demonstrated by Hassall and Anderson (2015) in the Ottawa region (Canada), stormwater ponds and wetlands contained similar levels of biodiversity and similar macroinvertebrate community structure. These systems are a hybrid version of nature and constructed to control the quality and runoff of water of roads and highways. Highway stormwater detention ponds are new elements in the hybrid landscape close to European highways. These ponds are the result of the application, in Europe, of a directive voted in 1991, in which is made a precise obligation by the highway companies to create ponds close roads to capture the waste waters that are collected on the impervious surfaces of the roads. Scher et al. (2004) have investigated six ponds in Provence (France), collecting every 4 weeks from March 2002 to March 2003 the following physical and chemical parameters: temperature ( C); conductivity (μS/cm); dissolved oxygen (% saturation); water level (cm); main dissolved ions (mg/l); trace metals, such as cadmium, copper, lead, and zinc (μg/l in water column and mg/kg in dry sediment); total hydrocarbons (mg/l, mg/kg); and herbicides, such as glyphosate and atrazine (μg/l). The results have demonstrated the presence in waters of glyphosate and in the sediments of copper and zinc. The level of contamination was found less severe than in other regions. The ponds were colonized by different species of dragon ies. The contribution to the regional overall diversity remains an open matter to investigate. A recent review on the literature produced in these last years (Clevenot et al., 2018) confirms the importance of stormwater ponds, but their variability due to the size and shape of the ponds, the presence of vegetation, the abiotic factors like luminosity or water level, and the presence of pollutants assign a differentiate role for these ponds that in some cases and for some species are ecological traps and for other are high-quality breeding sites. For instance, in Canada, Bishop et al. (2000) confirmed the presence of a differentiate wildlife in these ponds but found a low or moderate richness along 15 stormwater ponds investigated. Artificial reservoirs used to irrigate, as water reservoir, or for fire suppression, represent an important source of biodiversity and valuable habitat for many species (Deacon et al., 2018; Frankowski et al., 2019). Farm ponds are key for agricultural functions on private lands. In several cases, Swartz et al. (2019) investigating Grand River Grasslands of southern Iowa and northern Missouri have found that the majority of ponds were permanently accessible to cattle with a limited cover of wetland spontaneous vegetation. The landowners have little interest in managing ponds for wildlife, but the abundance of ponds in this area represents a potentiality to develop policies of nature conservation for the future. Ponds are elements in landscaping, and different techniques can be applied to create new ponds or restore old ones. Detailed guidelines are available (f.i. AA, 1997) (Fig. 9.10). Ponds are important components of a landscape to recreate sustainable development of multifunctional pond fish farming, associated recreation, therapeutic landscape, and hot spot for a multitude of plants and animals (Popp et al., 2019).

370

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Fig. 9.10 Conceptual diagrams describing habitat features and resources for farmland birds at typical of open managed ponds (a) and overgrown ponds (b). (From Davies et al., 2016, with permission)

Ponds are important landscapes for ood control, limiting water pollution, and for aesthetics, enhancing property value. There are evidences that ponds have been used in the pre-Columbian Amazonian oodplain as temporary traps for fishes. In this region ponds were also utilized to concentrate fishes coming from rivers during ood period (Blatrix et al., 2019). Biodiversity decline in farmlands requires new actions to remediate and compensate. In farmlands, like in the UK, several ponds have been “territorialized” since 1960s by filling or by transforming them in wet woodlands. Pond management by removing overhanging scrubs and sediments was found extremely successful in enhancing freshwater biodiversity. In particular, according to an investigation conducted by Davies et al. (2016) in North Norfolk (UK) birds resulted more abundant in open-canopy ponds than in overgrown ponds. Across the pond-scape, the gamma diversity exceeded all individual pond alfa diversity, confirming the great importance of pond-scape network quality for the maintenance of bird diversity.

9.5.4

Wetlands

Wetlands are areas with standing waters that have a deep in uence on vegetation and soil composition and biogeochemistry. Wetlands are menaced worldwide by reclamation: 64% of the world’s wetlands have disappeared since 1900. For instance, in New Zealand, Clarkson et al. (2013) documented that 90% of wetland area has been removed in the last 150 years, the highest loss rate in the world. The residual wetlands are menaced from drainage, nutrient enrichment, invasive plants and animals, and encroachment from urban and agricultural development.

9.5 Freshwater Landscape

371

Their importance from an ecological perspective consists in water improving, providing fish and wildlife habitats, storing oodwaters, and maintaining water during dry periods. Wetlands are the most productive ecosystems of the world, comparable to tropical forests, but occupy only 6% of the Earth’s surface. The most important and famous wetlands around the world are Camargue (Rhone River Delta, France), Wasur National Park (Indonesia, Papua province), Greater St. Lucia Wetlands Park (South Africa), Mekong Delta (southern Vietnam), Kakadu Wetlands (Northern Territory, Australia), Kerala Backwaters (southern India), Everglades (Florida, US), Okavango Delta (Botswana), and Pantanal (the largest wetlands in western Brazil, Bolivia, and Paraguay). Wetlands have a central role in the ecology of the watersheds. There are different type of wetlands (tidal zones, marshes, bogs, or swamps). Wetlands in forested areas may be originated by stream and river meandering ( uvial wetlands), by lake ooding (lacustrine wetlands), shallow open waters or marsh systems (ponds and potholes wetlands), abundant near-surface seepage originated by groundwater or precipitation (slopes wetlands), water bodies in depressed sites (basin wetlands). They are areas where water is above or near the surface of the soil for some time during the year. The presence of water drives pedology and vegetation. Several ecosystem resources are provided by wetlands, like the recharge of the aquifers. In 1979, the US Fish and Wildlife Service defined wetlands as follows (Cowardin et al., 1979): “Wetlands are lands transitional between terrestrial and aquatic systems where the water table is usually at or near the surface, or the land is covered by shallow water. . . . Wetlands must have one or more of the following three attributes: 1) at least periodically, the land supports predominantly hydrophytes; 2) the substrate is predominantly undrained hydric soil; and 3) the substrate is saturated with water or covered by shallow water at some time during the growing season of each year.”

Wetlands are generally distributed close to coastline where the aspect of terrain impedes a owing. Wetlands are important landscapes as nutrient sinks from farmlands and areas of great biodiversity. Reclamations along the human history have reduced these landscapes. For instance, in Italy the wetlands have been reclaimed many times since the Roman Empire until the recent days after periods of wetland expansion linked to wars or human pandemics crises. Restoring wetlands is an important passage to increase the overall diversity at landscape scale and to promote a better linkage between ecosystems and socialdriven processes (Olsson et al., 2004). This process requires a lot of competencies and a deep knowledge of the different factors impacting on the final success like habitat type, hydrological regime, soil properties, topography, nutrient supplies, disturbance regime, invasive species, and seed banks (Zedler, 2000). Rapid assessment about the ecological values of wetlands is necessary to maintain active protection programs (Fennessy et al., 2007). The conservation of wetlands assures ood attenuation and control (functioning like a sponge), regulation of stream ow, trap of sediments, pollution filters by removal of phosphates and nitrates, water purification, storm and wind buffer, erosion control, and finally maintenance of biodiversity. Wetlands close to sea

372

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

mitigate the rise of salty water level. Wetland microorganisms and vegetation capture the contaminants carried out by surface run-off (bacteria, pesticides, and nutrients) and at the same time vegetation control the run-off reducing the erosion and the transport of sediments across the aquifers.

9.5.5

Mangrove Forests

A special space and importance must be assigned to coastal mangroves forests composed of 80 halophytes throughout the world in 112 countries and extending for 181,000 km2. These special wetlands are present only at tropical and subtropical latitudes. Mangroves grow in areas with low-oxygen soil, where slow-moving waters accumulate fine sediments. In the last 50 years, one-third of these forests has disappeared. Causes of destruction are urban development, aquaculture, mining, and overexploitation for timber, fish, crustaceans, and shellfish. In fact, about 40% of the human population lives in the coastal regions (CIESIN, 2007). These plants are used for food, timber, fuel, medicine. The mangrove forests serve as a biogeomorphological agents interacting with tidal ows entrapping and stabilizing sediments, creating humus, and modifying coastal tropical environment (Valiela et al., 2001). As other wetlands, mangrove forests provide salts and food products, filtration of pollutants, and retention of oodwaters, shield to juvenile aquatic organisms, support biodiversity, support aquatic food chains, and sequester greenhouse gases. The removal of mangrove reduces ecosystems’ complexity and resilience of coastal ecosystems, exposing coastal areas to natural hazards produced by seasonal storms (Ostling et al., 2009) and occasional tsunami (NOAA, 2013). The protection of the coasts by engineering infrastructures is extremely costly especially for poor countries. Cyclones like Katrina in the Gulf of Mexico in 2005 and Typhoon Haiyan in the Philippines and Southeast Asia in 2013 are two examples of the destructing power of natural events. The conservation, restoration, and planting of mangrove forests may represent a good and relatively inexpensive solution to prevent or to mitigate the effect of such climatic events (Marois & Mitsch, 2015). This vegetation is distributed along the coasts in the intertidal region in the tropical and subtropical regions of the world between 30 N and 30 S latitude (Giri et al., 2010). The global distribution is in relation with ocean currents of the 20  C isotherm of seawater in winter. This forest grows at high salinity concentration, high temperature, extreme tides, and anaerobic soils and in areas of high sedimentation. Along the historic period, mangrove extension has been strongly reduced and most of the actual forests are degraded due to heavy population that inhabit coastal regions. The major causes of their decline are due to agriculture, aquaculture, tourism, urban development, and overexploitation (Alongi, 2002). The predicted sea level rise due to the melting of polar ices may represent a further element of uncertainty about the future of costal line stability (Nicholls & Cazenave, 2010). Mangrove forests are characterized by low plant diversity and structural simplicity but offer several ecosystem resources to local communities like sheltering coastal

9.6 Mining and Energy Landscape

373

socioeconomic systems from high-energy storms. For instance, in Caribbean area, the mangrove forests play an important role to protect against hurricane. In particular, black mangrove (Avicennia germinans) was found to be more resistant than red mangrove (Rhizophora mangle) to these events (Imbert, 2018). Mangroves, thanks to their capacity to trap mineral and organic sediments, in uence vertical land development (Lee et al., 2014). In 2004 tsunami event that hit the Indian Ocean on the coasts close of Cuddalore district (Tamil Nadu, India), the highest damages occurred especially were mangrove forests were degraded or not present. Despite this limited experience mangrove extension represents a good indicator about their roles to attenuate and buffering the tsunami effects (Danielsen et al., 2005). But, about this evaluation, the lack of scientific evidences seems to reduce the importance of mangrove forests against tsunami especially when sea waves are very high, as argued by Kerr and Baird (2007). In extreme events like after the Krakatoa eruption in 1883, the tsunami penetrated 8 km of full-canopy rainforest (Simkin & Fiske, 1983).

9.6 9.6.1

Mining and Energy Landscape Mining Landscape

Mining landscape refers to surficial or subterraneous mines from which minerals (e.g., bauxite, pyrite) and rocks (e.g. marble, granite) are extracted. Surface mining is an activity that has a great impact on the landscape (Dulias, 2016), and in the majority of cases, such impact is irreversible due to modification of the soils like in the open-cast diamond mines. In addition to industrial mining, also artisanal and small-scale mining represents especially in developing countries source of concern for environmental sustainability and landscape functioning (Byizigiro et al., 2015). Impressive morphological modifications are observed in every location in which mineral resources are extracted. For instance open-cast mines create holes and artificial hills, impacting on underground and surficial hydraulic systems with consequences on vegetation. Reclamation is a necessity to recover degraded postmining terrains and it requires resources and competencies (Kuter, 2013; Vrablikova et al., 2016; Festin et al., 2019; Abad, 2019; Mentis, 2020; Turrión et al., 2021).

9.6.2

Energy Resources Landscape

Energy landscapes are created as consequences of extraction and transport of fossil, solar, nuclear, and wind energy. At any time and all energy sources have impacted on the geographical and ecological processes at local and regional scale from peat extraction to wind farms (de Jong & Stremke, 2020). Energy landscapes have in common a great ecological and aesthetic impact on the environment with long-term

374

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

consequences on ecosystems and landscapes exacerbated by the effect of climate change. Some landscape fundamentals like core areas, connectivity, and resilience are strongly menaced in energy landscapes. We can categorize these landscapes in the following: 1. Oil landscapes (oil spill machineries, pipelines, refineries and transportation logistic (oil tankers, oil terminal)). 2. Coal landscape (coal mines). 3. Wind landscape (wind farms). 4. Geothermic landscape (power plants and pipelines). 5. Electrical landscape (power plants and electric lines). Often these landscapes occur close each other creating a hybrid condition from which it remains hard to distinguish the individual effects of infrastructures on the environment. Every typology of energy landscapes has in common punctual resources from which energy is produced and a vast network of connection to transport and/or to distribute energy (electrical) or raw material (crude oil, gas). It is not possible to arrange a list about the severity of impacts of the energy landscape, but only a description of the potential impacts because these impacts largely depend on the density of structure and activities and by the region in which these structures are located. For instance, energy landscapes in arctic areas have a greater impact than in temperate areas, and especially the wilderness contamination is more evident associated to an extremely slow environmental recovery. The visual impacts are the first consequences to be evaluated and assessed. For instance, wind towers and electrical lines represent visual modification of the landscapes (Fig. 9.11). Apparently associated to a more modest impact but more diffuse is the electrical landscape represented by power plants and electrical corridors. These facilities require the cutting of trees and shrub along the trail. A list of topics related to voltage lines is not easy to be conducted; however, tentatively here are some elements on which powerlines have an impact: 1. Visual-scape. 2. Agricultural lands. 3. Airports and airstrips. 4. Archeological and historical resources. 5. Cultural values. 6. Electric and magnetic fields. 7. Implantable medical devices and pacemakers. 8. Invasive species. 9. Noise and light impact. 10. Radio and television receptions. 11. Recreation areas. 12. Safety. 13. Stray voltage.

9.6 Mining and Energy Landscape

375

Fig. 9.11 Wind farm in Tanzania, an example of hybridscape where the impact of technology is massive without possibility to incorporate it into the natural processes

Negative effects have been discussed by Luken et al. (1991) for power-line corridors in northern Kentucky. These authors warn about the additional effect of fragmentation in forests already disturbed by human presence suggesting to locate new lines far from forest areas. The passage of pipelines or electrical transmission lines produce a fragmentation in the forest landscapes at every latitude and this has a major impact on the species that are area sensitive and that perceive these lines as hostile edges. However, large electrical corridors may have attractive effects due the secondary succession that occurs after the repeated vegetation cutting in the corridor (Kroodsma, 1982a; Kroodsma, 1982b).

9.6.2.1

Power Lines

The transmission network of electricity of at least 220 kV cover about 300,000 km in Europe (European Network of Transmission System Operators for Electricity, 2012), and 250,000 km in the USA (Abraham, 2002). The effects of these infrastructures on animals are complex and depend on several factors (see Bartzke et al., 2014, for a review). Power lines have effects on ungulates but the size of these effects are not easy to be ascertained. The electromagnetic fields seems to disturb cattle and roe deer (Capreolus capreolus) (Burda et al., 2009). Power lines may have effects as barriers and corridors although these effects vary according to the availability of cover. The secondary succession along power corridors attracts edge

376

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

species, and predators and the role of sink or of source by power-line corridors largely depends on local configuration of the landscape and species. Contradictory results emerge on electrical corridors and their potential impact on the environment. The secondary succession, maintained by vegetation cutting at regular intervals, allows a large set of species to establish in such areas. This is true for insects, lizards, snakes, mammals, and birds, and may represent a new habitat for many organisms. Early successional birds have suffered a long-term decline across North America and the presence of managed power-line corridors represents attracting habitats for several species in particular in corridors that cross forests. Conversely, corridors in more manipulated landscapes may represent sink habitats for early successional birds. The width of corridors play a different role in attracting birds. For instance, Anderson et al. (1977) have found that corridors larger than 30 m have a high bird species diversity attracting inside forest open-country birds. Powerline corridors if managed to maintain a secondary succession can improve the overall biodiversity, especially in regions like Australia where to reduce the fire risk vegetation is completely removed under powerlines (Clarke et al., 2006).

9.6.2.2

Oil Pipelines

Barriers like oil pipelines in Arctic may be considered semipermeable barriers. Mule deer (Odocoileus hemionus) use mechanism like detouring, increased movement rates, reduced stopover use, confirming the capacity for this species to migrate through moderate level of development (Sawyer et al., 2013). Moose (Alces alces) seem not to be affected along the Trans-Alaska Oil Pipeline when the pipes were at 1.5 m above ground, although a height of 2.7 m was preferred as reported by Sopuck and Vernam (1986). The oil development in Arctic has a deep in uence on the calving habits of caribou (Rangifer tarandus granti). Females exposed to petroleum development consumed less forage during the calving period with consequences of poorer body condition at the breeding time. The climatic extreme of Artic and the recent variability have to be considered as cumulative effects of caribou herds in areas of oil development (Nellemann & Cameron, 1998; Cameron et al., 2005). Moreover, the construction of power lines and roads has been observed to reduce the possibility of reindeer (Rangifer tarandus tarandus) to move across their home range (Vistnes et al., 2004). For instance, an elevated pipeline in a farming landscape has a different impact than an elevated pipeline in the tundra along caribou migration. The behavior of a large group of caribou during seasonal migration have been reported by Smith and Cameron (1985) in the Kuparuk Development Area near Prudhoe Bay, Alaska, in 1981 and 1982. In 1981, 46% of a group of 917 crossed beneath the infrastructure after 26 separate attempts, 22% trotted parallel for 32 km without crossing, and 19% separated by the group were not accounted. In the successive year, a group of 617 individuals attempted to cross the same structure, but only 26% crossed after

9.7 The Hybridscape

377

36 attempts, 37% cross in one attempt a buried section, and 37% left the main group and were not accounted. This study demonstrates the extreme sensitivity of this species living in wild Arctic to landscape modification for energy use.

9.7 9.7.1

The Hybridscape Introduction

The hybridscape is the result of (modern) human technological infrastructures and logistics interplayed with natural patterns, where the dynamic of the entire system is in part assured by an input of additional energy (fossil carbon and oil, wind, solar, and nuclear) (Farina, 2019). The word “hybrid” means a combination of two different characters or properties. In ecology, hybrid nature means a blending of synthetic and natural “objects” (e.g., a light pole covered by ivy (Hedera helix)). At microcosmic level, a hybridscape would be, for instance, the condition in a small temporary pond created by water ephemeral residual in an abandoned tire and populated by the larvae of mosquitos. Hybridscape also refers to “industrialized” ecosystems, such as sewage treatment plants that use both technologies for the physical movement of liquids and organisms to digest and purify sewage (Schneider, 2011). The hybridscape is the result of a mix of technological infrastructures and natural ecosystems, where environmental fundamentals such as spatial patterns and resources, complexity, uncertainty, information, and meaning are constrained (Farina et al., 2005). This new system may produce dramatic effects on the semiosis of several species. The ux of information could be maintained in hybrid nature when some natural patterns and processes are replaced by human artifacts and associated dynamics, but the process of signification (meaning) could be strongly modified. Human activity in ecosystems produces new spatial configurations that generally have a reduced ecological complexity, causing in species a reduction of their performances due to a higher level of habitat and resource uncertainty. The strict contact between people and wild organisms in hybrid nature requires new types of strategies to guarantee the continuation of the natural dynamic of populations and communities and to assure a higher level of health safety for people (Grandcolas & Justine, 2020). The recent COVID-19 pandemic events are an example of a modified relationships between people and nature (Lorentzen et al., 2020). Their effects on people and biodiversity require a great attention in the future where economic crisis during and after COVID-19 pandemic time may affect the effort to protect biodiversity and people health at the same time (Corlett et al., 2020). The habitat destruction and the climate change may force species to shift habitat and to find more favorable conditions close to human settlements, in seminatural

378

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

conditions. In particular, bats that are reservoir of different corona viruses and the handling of bats for food, in unsanitary conditions, probably are the cause of the recent transmission of COVID-19 to human population. The interactions of humanity with environment date back hundreds of thousands of years, during which human population has had demographic expansions alternating to regression as a consequence of climatic change, insurgence of plagues, long periods of famine, and wars (Chester, 1997). Different historical periods have been characterized by different social and political models that have changed the environment and in large measure depleted resources creating continuously “novel ecosystems” (Morse et al., 2014; Truitt et al., 2015). The major impact of these anthropogenic transformations has regarded the modifications of the forest cover that according to the societal model have been maintained intact (low level of modification), or transformed in variegated (low-high level of modification), in fragmented (low-high level of modification), and in relictual (mostly high level of modification) conditions (Fischer & Lindenmayer, 2007). The human intrusion, especially in more favorable climatic conditions for life, has produced a dramatic competition between humans and a great number of organisms (Roser et al., 2019), resulting in a decrease of richness and diversity of life forms (Lotze et al., 1991). However, some organisms have found benefits in living close to human settlements favored by new conditions in urban and peri-urban ecosystems. For instance, historical metropolitan areas of Rome have a great richness of plants as a consequence of a long duration of urbanization that has favored the expansion for several ruderal plants (Celesti Grapow, 1995; Celesti Grapow et al., 2001). There are at least three levels of possible modifications at the landscape level connected to human intrusion: (1) landscapes that maintain their historical configuration, (2) landscapes that are represented by hybrid systems in which old and new components are mixed, and finally (3) completely novel landscapes. There are evident differences between natural and hybridscapes. Hybridscapes are less complex, have a minor capacity to recover after disturbances, and require a continuous input of external energy and information to maintain their functionality. In hybridscape, due to a chronic human disturbance, rarely species have the possibility to successfully accomplish their reproductive cycle and assuming in this way characters of “sink populations” (Pulliam, 1988). A “sink population” has been defined as a population in which the ratio between newborn and local extinction is less than one. The survival of a population largely depends on the immigration of individuals from outside (source) areas. This sink asset may become prevalent in hybridscapes, and in the future, hybridscapes will probably replace natural systems extending at planetary scale. This new condition will require a revision of conservation strategies and norms to maintain a viable population for several species to reduce the risk of extinction (Hobbs et al., 2009).

9.7 The Hybridscape

9.7.2

379

Spatial Patterns and Resources in Hybridscape

Animals have a species-specific and individual perception of the spatial arrangement of natural objects, such as trees, rocks, ponds, fields. This perception is strictly connected to their embodiment (Hirose, 2002) and their metabolic rate (West et al., 1997). In hybridscapes, the spatial arrangement are often manipulated by humans to produce new spatial configurations according to productive, residential, leisure, or therapeutic purposes (Farina et al., 2007), which can inadvertently modify the eco-fields, the carrier of meaning of spatial arrangements used by species to track resources (Farina & Belgrano, 2004; Farina & Belgrano, 2006). For instance, forest logging or farming development alters the structure of landscapes, transforming closed systems in open parklands with an increase of the isolation of patches used by species to search food or to nest. This isolation requires more energy investment in movements and navigation, exposing species to a higher risk of predation during inter-patch transfer (Prugh et al., 2008).

9.7.3

Complexity in Hybridscape

Environmental complexity is the result of a net of organismic relationships and exchange of matter, energy, information, and associated feedback. These inter- and intra-system relationships that occur either at large-scale resolution (macrocosm) or at small-scale resolution (microcosm) delay the entropic processes (Lloyd, 1990). However, the number of interrelationships in a system is not per se sufficient to assess the value of the complexity because it is expressed more by the unicity and peculiarity of the relationships than by their number. A system is complex not when all parts are connected to all (all know all: “knowledge”) but when there are exclusive connections between parts (all do not know all: “ignorance”). A trade-off must exist between “knowledge” and “ignorance.” An excess of “knowledge” (species are connected by a dense network of redundant equiponderant relationships) produces an increase of competition, while isolation by “ignorance” reduces competition (Ulanowicz, 1983). The reduction of complexity in a hybridscape due to human intervention or as a follow-up of natural disaster ( ooding, eruptions, tornados) has consequences on uncertainty, information, and meaning processes, affecting the semiosis of species. For instance, forest logging is a process that rejuvenates a forest, and impedes the completion of climax cycles (Clements, 1936). In order to avoid the complete loss of the climax configurations due to logging of old growth forests, Harris (1984) has proposed a model of logging rotation in which the old-grow stands are maintained at the center of an ideal circle, with a second ring occupied by a long rotation of cutting sequences that maximize the difference in age. However, this system of forest management contributes to the establishment of ecotones that may affect richness,

380

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

distribution, and abundance of species, reducing the resilient properties of the forests (Risser, 1995), and favoring the creation of ecological traps (Schlaepfer et al., 2002). Some actions very common in hybridscapes may have deep effects of the functioning of the ecosystems. For instance, plowing and fertilization modify the cycle of nutrients available to domesticated plants (Lal et al., 2007). Fire prevention interrupts the periodic removal of dead biomass, reducing the provision of mineral nutrients to soils with effects on the structure and dynamics of vegetation (Busse et al., 2014). The reduction of seasonal ooding by river embankments reduces the ecotonal role of river margins (Vought et al., 1994). Breakwaters and wave barriers impact on the dynamics of the erosion–sedimentation cycle of the seacoast, producing modifications in the coastline morphology and in animal and plant community composition (Colantoni et al., 1997).

9.7.4

Uncertainty in Hybridscape

The unpredictable modification of the environmental conditions produces uncertainty considered “a common character of the environment caused by the unpredictability of external events or produced by internal processes” (Farina, 2019). Uncertainty can have negative effects on the ecological knowledge of a species that should prevent surprises by reducing the level of uncertainty in their habitats. In a hybridscape, uncertainty can be increased by the presence of new objects and their spatial configurations and behaviors that can be completely unknown to the resident species. For instance, vehicles in transit on roads and not well perceived as a threat by species may increase the risk of crashes (Legagneux & Ducatez, 2013; Adams & Geis, 1983; Davie et al., 1987; Spellenberg, 1998). High-tension electrical lines are unexpected and unrecognized objects of an hybridscape, to which birds are particularly exposed to a casual collision (Bernardino et al., 2018). In the Netherlands, Koops (1987) has estimated that 750,000 to 1 million birds end up being killed annually by collision along 4600 km of high-tension electrical lines. Bird collision with tall buildings and glass windows at residential houses is estimated to be between 98 to 980 million individuals (Erickson et al., 2001). Communication towers and wind farms can be cause of death for birds when located along their migration routes (Erickson et al., 2001; Longcore et al., 2013; Thaxter et al., 2017).

9.7 The Hybridscape

9.7.5

381

Information and Meaning in Hybridscape

Information, considered a polymorphic phenomenon and a polysemantic concept, can be defined as a measure of the distance from a reference system (e.g., the chromatic distance between black and white colors, the metric distance between two objects, the number of times in which a referent object is contained in another, etc.). Bateson (1970) has defined information as the difference that makes a difference, like in an infinite chain of attributes and an ecological fundamental to reduce the level of uncertainty, allowing organisms to survive to the uncertain events (Dall & Johnstone, 2002). In hybridscape, the information deficit is compensated by other new pieces of information added by human activity. However, often species have no capacity to assign meaning to new sources of information provided by humans. For instance, in urban parks, planted trees are often represented by non-native species that cannot be recognized by local animal communities posing new complex challenges (Gaertner et al., 2017). Meaning is a process performed by all animals, not only to humans (Von Uexküll, 1982[1940]; Menant, 2003) and is fundamental for any ecosemiotic cycle. The attribution of meaning to the ux of information perceived by the senses requires an encoding/decoding procedure that, in the majority of the cases, is genetically fixed (Barbieri, 2019). Many artifacts dispersed in nature by humans, such as lamp posts, metallic fences, advertising boards, cranes, and monuments have no direct meaning for the animals and in some cases are utilized for their involuntary affordance for roosting or nesting (Gibson, 1966), where affordance is considered the property of the animal–environment system (Stoffregen, 2003). The consequences of a deficit of meaning on species life remains not easy to be assessed. A hybrid system contains more unknown objects and new spatial arrangements than natural systems at which organisms can attribute a precise meaning. The ecosemiotic deficit that pervades hybrid landscapes represents the major obstacle to the accomplishment of several ecological and biological functions. In hybridscape, some information from natural structure and processes can disappear, replaced by others linked to human intrusion but only a minor portion of additional information can be converted into meaning. When a natural system is used by humans, some parts of the information ux are no longer available. For instance, there is evidence that marsh reclamation reduces frog populations that in turn produces few choruses that usually are used by migrating birds as reference points to navigate in spring toward northern territories (Griffin & Hopkins, 1974; Griffin, 1976). Technophonies characterize many hybridscapes. The presence of sources of noise disseminated in nature by humans may be the cause of failure in the communication process between soniferous species (e.g., along pipeline gas pumping stations, close proximity to industrial settlements, and in peri-urban landscapes (Antze & Koper, 2018)).

382

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

The problem of noise is particularly important in aquatic systems, where sound propagates at a speed five-times faster than in air. The effect of noise in aquatic systems is so far poorly investigated, but from the first results emerge the importance of the acoustic quality of the waters for the life of aquatic organisms (Whitefield & Becker, 2014). For instance, noise produced by trade and cruise ships and their sonars interfere negatively with the communication system of whales and dolphins (Au & Green, 2000; Erbe et al., 2018, 2019; McDonald et al., 2006; Holt et al., 2008; Kunc et al. 2016).

9.7.6

Conclusions

The characteristics of hybridscape have deep effects on the ecosemiotic mechanisms of organisms at different temporal and spatial scales. The spatial arrangement of human artefacts changes the permeability of the landscape and creates unexpected barriers and frictions to the free circulation of several animals and plants. Fragmentation and insularization of natural habitats create edge effects that in turn encourage prey-and-predator concentration, transforming such fragmented habitats into sink systems for the high rate of predation. The ecological complexity in the hybridscape is reduced by human intervention that, for instance in agro-forest systems, simplifies the food webs due to the maximization of the primary production. Changes of landscape configurations and gaps in the trophic chain are the byproducts of hybridscapes in uencing the ecosemiosis of species due to an impairment of the effectiveness of species-specific eco-fields. This causes the impossibility to find spatial arrangements as a carrier of meaning and reducing the chance to track the associated resources. In hybridscape, the closeness of several wild organisms to humans is often managed by new social model inspired by biophilia. For instance, feeding animals in hybridscape is very common practice, at least in northern Europe and North America (Dunn & Tessaglia-Hymes, 1999; Cannon et al., 2005; Reynolds et al., 2017), although this practice has negative consequences due to pathogen infestations favored by the concentration of species on foraging posts (Becker et al., 2015). In hybridscape, the 30–40% of the food produced is transformed in refuses that become available to several opportunistic species (FAO, 2019). For instance, the increase of dumps and fishing discards has favored opportunistic species modifying the relationships in the communities. Wild animals, such as gulls, crows, coyotes, foxes, and turtles, and some feral animals, such as cats, dogs, and pigs, are species favored by the abundance of uncontrolled dumps (Donovan, 2015). Today, an intact nature is nothing more than an utopian vision of the world (Clayton & Opotow, 2003). Natural system have been contaminated by people usage for long time at different level and intensity. For instance, the Mediterranean ecosystems have been modified by human activity since glaciers receded (di Castri, 1981; Blondel & Aronson, 1999; Blondel, 2006). In the Middle East, fires have been managed by hunter-gatherers about least 500,000 years ago (Naveh

9.8 Therapeutic Landscape

383

& Dan, 1973). In Australia aboriginals have used fires by 47,000 years producing deep modification in the landscapes and Native Americans also used fires to manage prairies and forests for millennia until colonial times (Stewart, 2002; Vale, 2002). There is a substantial difference between hybridscape and cultural landscape. Cultural landscape is an area in which material resources are actively provided by agricultural sustainable practices and in which the territory becomes a spiritual source of inspiration, beliefs, identity, and belonging for local human populations. An hybridscape is represented by the lack of integration between the resources necessary to sustain people and natural components, at least at a small scale. For instance, an important infrastructure, such as a high-speed train line, or a wind farm deeply modify the environment without to pay attention to actions of integration or mitigation with the environment and with the socioeconomic conditions of local people. Future generations will be loaded with heavy responsibilities to maintain and restore biodiversity in hybrid nature, and this action will require a lot of energies and economic resources. Probably there is not a panacea for these actions and to remedy to disinterestedness or indifference will be a hard task to achieve. Hybridscape is a new typology of landscape that requires an adapted management model aimed to reassign resilient character indispensable to face the uncertainty of the climatic events and economic constraints. This is the present challenge for a sustainable future, where an urbanized population could probably be better educated to live in more strict contact with natural events understanding difficulties, risks, and limits better (Krasny et al., 2013). The inherent capacity of the ecosystem processes to increase their complexity is more than a simple hope for the future of the humanity.

9.8

Therapeutic Landscape

The landscape can be considered a complex cognitive entity composed of material and non-corporeal elements interacting within a network of energy, matter, and information (Farina, 2006, 2021). The landscape becomes the structure through which humans perceive their Umwelt, and it is the agency of important ecosystem resources. In particular, humanity requires to live in a favorable environment in which all the necessary resources can be found with a reasonable effort. Natural processes provide several resources that people usually recognize in terms of goods (e.g., food, clean water, air, chemical molecules from medical plants); however, nature provides also immaterial resources that are beneficial for the mental health and that contribute to the maintenance of the human well-being. Unfortunately, development of new models of human societies force people to live in unbalanced habitats dominated and driven by economic and social con ictual mechanisms. Environmental deterioration as consequence of a massive use of energy invested in the production of goods and services and the associated stresses of an accelerated lifestyle reduce the quality of human physical and social habitats.

384

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

As consequences, this fact generates a frequent and persisting status of physical and psychological sufference that decreases the quality of human life at cognitive level (Monroe, 2008; Driskell & Salas, 2013). Humans possess in the genetic mechanisms to access to a wide variety of natural resources that the modern lifestyle often inhibits or limits. The impossibility to achieve such resources produces stress reactions that contribute to a person’s “illbeing.” This problem, which is very common in Western human societies, is a “social illness” that demands remediation (“therapeutic”) actions. Moreover, modern humanity has strongly reduced a lot of symbolic religious behavior like the collective ceremonies, the visitation and pilgrimage of sacred areas inside their common living space, and by contrast these sites assume the role of touristic appeal worldwide (di Castri & Balaji, 2002). To remedy and recovery these disfunctions specific actions are requested. Usually, natural landscapes provide mental resources creating a true, real therapeutic-scape able to solve different problems created in people for a style of life at growing rate further and further away from natural contexts, and to satisfy the necessity for human life to have a positive sensory world capable of supporting bodies’ functions (Lynch, 1981). The “therapeutic science” that etymologically arises from the Greek word Therapeytikè (the art of assistance) recently considers some landscapes as important areas in which the human mind finds refuge, feels happy, realizes ecological identity, recovers concentration, or discovers a sense of place (Thomashow, 1995; HudsonRodd, 1998; Ingold, 2000; Clayton, 2003; Wilson, 2003). For instance, for the achievement of human well-being, visual (scenery and aesthetics) (Bourassa, 1991) and acoustic scapes (Schafer, 1977; Truax, 2001; Farina, 2014) are considered valuable elements actively searched across the landscape (Gould & White, 1986). This process is performed by the utilization of bio- and eco-semiotic families of cognitive “objects.” In an eco-semiotic perspective, we may define human well-being as the accomplishment of a variety of functions that in turn requires specific eco-fields. According to the eco-field model, a therapeutic scape is a special template carrier of meaning that returns some cognitive resources like amenity, peacefulness, sense of place, relaxation, etc. (Farina & Belgrano, 2004, 2006). The majority of touristic activities can be considered therapeutic activities and holiday places like mountains and beaches become therapeutic locations where modern societies receive benefit against stress (Conradson, 2005; Yan & He, 2020). A therapeutic scape may be found in a secluded natural environment, but often such areas are remote, are not easily accessible, or do not fulfill enough requirements to facilitate the emergence of human well-being. Consequently, most “therapeutic scapes” are the result of an anthropogenic direct action, as well as a combination of creativity, engineering, and scientific knowledge (Fig. 9.12). The therapeutic landscapes function like an active interface that surrogates the necessary connection between humanity and natural components. In fact, is the distance of the everyday landscape to a natural one that impedes to find indispensable spiritual resources. Too often, the therapeutic benefits are considered a

9.8 Therapeutic Landscape

385

Fig. 9.12 A therapeutic landscape is a landscape composed with natural objects disposed to increase human perception in a peaceful context of peaceful and acoustic quiet

psychological product and are not considered in an ecological sense. This fact forces the separation between people and nature, and although under a psychological perspective, the experience of natural environments is well recognized (e.g., Kaplan & Kaplan, 1989; Appleton, 1996; Kaltenborn, 1998; Jorgensen & Stedman, 2001). A therapeutic scape can be considered as a particular case of the nicheconstruction process carried out by humans, to receive spiritual benefits whereby a cultural inheritance overrides the genetic inheritance and therefore modifies the human evolutionary process (Odling-Smee et al., 2003, pp. 264). In conclusion, a therapeutic landscape uses symbols as substitutes of genuine physical and material resources that are necessary to perform vital functions, but which are no longer available in individual’s native surroundings after an indiscriminate human impact of wild resources. The simplification of natural systems cannot provide the entire set of material and immaterial resources that guarantee human well-being. Therefore, humanity must invest substantially more energy and knowledge into the conservation and “production” of therapeutic landscapes, both to assure social sustainability and individual and social well-being with the final goal to guarantee a better living conditions. A recreational area, like a city park or a tourist path, in a protected area is full of signs that represent biosemiotic symbols of fundamental resources no longer available to us elsewhere. We instinctively perceive the environment as being “richer in vital resources” than elsewhere. Such perception stimulates cognitive images that recall ancestral feelings connected to vital survival functions, and this experience translates into psychological benefits.

386

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

However, just as botanical gardens and zoos are not sufficient to preserve the biodiversity, we must be aware that in the same way urban parks and green infrastructures are insufficient to guarantee a sustainable, durable healthy life to people. Urban parks and green infrastructures are tools to be used as temporary remedy to acute disorders created by a human lifestyle on the edge of nature. A diffuse “more natural” landscape associated to better life conditions are indispensable ingredients to assure long-term human well-being based on not only temporary therapeutic events, but also on durable links with nature. For this, landscape ecology may help to design and manage better environmental mosaics in which humans can spend life in harmony with creation.

References AA. (1997). Agriculture handbook 590. USDA. Abad, C. J. P. (2019). Environmental recovery of abandoned mining areas in Spain: Sustainability and new landscapes in some case studies. Journal of Sustainability Research, 1(3). Abraham, S. (2002). National transmission grid study. Report from U.S. Department of Energy, Washington, DC, USA, 93 pp. Adams, L. W., & Geis, A. D. (1983). Effects of roads on small mammals. Journal of Applied Ecology, 20. Alongi, D. M. (2002). Present state and future of the world’s mangrove forests. Environmental Conservation, 29, 331–349. Amuti, T., & Luo, G. (2014). Analysis of land cover change and its driving forces in a desert oasis landscape of Xinjiang, Northwest China. Solid Earth, 5(2), 1071–1085. Anderson, S. H., Mann, K., & Shugart, H. H. (1977). Effect of transmission-line corridors on bird populations. American Midland Naturalist, 97(1), 216–221. Andrade, G. I., & Rubio-Torgler, H. (1994). Sustainable use of the tropical rain forest: Evidence from the avifauna in a shifting-cultivation habitat mosaic in the Colombian Amazon. Conservation Biology, 8(2), 5. Anschuetz, K. F., Ferguson, T. J., Francis, H., Kelley, K. B., & Scheick, C. L. (2002). That place people talk about: The petroglyph national monument ethnographic landscape report (pp. 7–1). US National Park Service, Petroglyph National Monument. Antrop, M. (1997). The concept of traditional landscapes as a base for landscape evaluation and planning. The example for Flanders Region. Landscape and Urban Planning, 38, 105–117. Antrop, M. (2005). Why landscapes of the past are important for the future. Landscape and Urban Planning, 70, 21–34. Antze, B., & Koper, N. (2018). Noisy anthropogenic infrastructure interferes with alarm responses in Savannah sparrows (Passerculus sandwichensis). Royal Society Open Science, 5. Appleton, J. (1996). The experience of landscape. Wiley. Asarpota, K., & Nadin, V. (2020). Energy strategies, the urban dimension, and spatial planning. Energies, 13(14), 3642. Au, W. W. L., & Green, M. (2000). Acoustic interaction of humpback whales and whale watching boats. Marine Environmental Research, 49, 469–481. Azhar, B., Saadun, N., Prideaux, M., & Lindenmayer, D. B. (2017). The global palm oil sector must change to save biodiversity and improve food security in the tropics. Journal of Environmental Management, 203, 457–466. Bailey, R. G. (1998). Ecoregions. The ecosystem geography of the oceans and continents. Springer. Barbieri, M. (2019). A general model on the origin of biological codes. Biosystems, 181, 11–19.

References

387

Barlow, J., Gardner, T. A., Lees, A. C., Parry, L., & Peres, C. A. (2012). How pristine are tropical forests? An ecological perspective on the pre-Columbian human footprint in Amazonia and implications for contemporary conservation. Biological Conservation, 151(1), 45–49. Bartzke, G. S., May, R., Bevanger, K., Stokke, S., & Roskaft, E. (2014). The effects of power lines on ungulates and implications for power line routing and rights-of-way management. International Journal of Biodiversity and Conservation, 6(9), 647–662. Bateson, G. (1970). Form, substance, and difference. General Semantic Bulletin, 37, 5–13. Batsi, G., Sonwa, D. J., Mangaza, L., Ebuy, J., & Kahindo, J. M. (2020). Biodiversity of the cocoa agroforests of the Bengamisa-Yangambi Forest Landscape in the Democratic Republic of the Congo (DRC). Forests, 11(10), 1096. Becker, D. J., Streicher, D. G., & Altizer, S. (2015). Linking anthropogenic resources to wildlifepathogen dynamics: A review and meta-analysis. Ecology Letters, 18, 483–495. Belaire, J. A., Whelan, C. J., & Minor, E. S. (2014). Having our yards and sharing them too: The collective effects of yards on native bird species in an urban landscape. Ecological Applications, 24(8), 2132–2143. Benton, T. G., Vickery, J. A., & Wilson, J. D. (2003). Farmland biodiversity: Is habitat heterogeneity the key? TREE, 18, 182–188. Bernardino, J., Bevanger, K., Barrientos, R., Dwyer, J. F., Marques, A. T., Marques, A. T., et al. (2018). Bird collisions with power lines: State of the art and priority areas for research. Biological Conservation, 222, 1–13. Berrahmouni, N., & Regato, P. (2007). Cork oak forest landscapes: A whole world beyond cork. Beyond cork – A wealth of resources for people and nature: Lessons from the Mediterranean. WWF Mediterranean Programme, Madrid, 8–16. Bessey, K. M. (2002). Structure and dynamics in an urban landscape: Toward a multiscale view. Ecosystems, 5(4), 360–375. Bishop, C. A., Struger, J., Barton, D. R., Shirose, L. J., Dunn, L., Lang, A. L., & Shepherd, D. (2000). Contamination and wildlife communities in stormwater detention ponds in Guelph and the Greater Toronto Area, Ontario, 1997 and 1998. Part I – Wildlife Communities. Water Quality Research Journal of Canada, 35/3, 399–435. Blatrix, R., et al. (2019). The unique functioning of a pre-Columbian Amazonian oodplain fishery. Scientific Reports, 8, 5998. https://doi.org/10.1038/s41598-018-24454-4 Blondel, J. (2006). The “design” of Mediterranean landscapes: A millennial story of human and ecological systems during the historic period. Human Ecology, 34, 713–729. Blondel, J., & Aronson, J. (1999). Biology and wildlife of the Mediterranean region. Oxford University Press. Boffa, J. M. (1999). Agroforestry parklands in sub-Saharan Africa. Food and Agriculture Organization of the United Nations. Bos, M. M., Steffan-Dewenter, I., & Tscharntke, T. (2007). The contribution of cacao agroforests to the conservation of lower canopy ant and beetle diversity in Indonesia. Biodiversity and Conservation, 16(8), 2429–2444. Bourassa, S. C. (1991). The aesthetics of landscape. Belhaven Press. Broto, V. C. (2017). Energy landscapes and urban trajectories towards sustainability. Energy Policy, 108, 755–764. Broto, V. C. (2019). Urban energy landscapes. Cambridge University Press. Brown, E. C., et al. (2017). Documentation for the global man-made impervious surface (GMIS) dataset from Landsat. NASA Socioeconomic Data and Applications Center (SEDAC). Center for International Earth Science Information Network (CIESIN) Columbia University. Burda, H., Begall, S., Cerveny, J., Neef, J., & Nemec, P. (2009). Extremely low-frequency electromagnetic fields disrupt magnetic alignment of ruminants. PNAS, 106(14), 5708–5713. Busse, M. D., Hubbert, K. R., & Moghaddas, E. Y. (2014). Fuel reduction practices and their effects on soil. MIT Press.

388

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Byizigiro, R. V., Raab, T., & Maurer, T. (2015). Small-scale opencast mining: An important research field for anthropogenic geomorphology. DIE ERDE–Journal of the Geographical Society of Berlin, 146(4), 213–231. Cameron, R. D., Smith, W. T., White, R. G., & Griffith, B. (2005). Central Arctic caribou and petroleum development: Distributional, nutritional, and reproductive implications. Arctic, 58(1), 1–9. Cañellas, I., Roig, S., Poblaciones, M. J., Gea-Izquierdo, G., & Olea, L. (2007). An approach to acorn production in Iberian dehesas. Agroforestry Systems, 70(1), 3–9. Cannon, A. R., Chamberlain, D. E., Toms, M. P., Hatchwell, B. J., & Gaston, K. J. (2005). Trends in the use of private gardens by wild birds in Great Britain 1995–2002. Journal of Applied Ecology, 42, 659–671. Canteiro, C., Pinto-Cruz, C., Simões, M. P., & Gazarini, L. (2011). Conservation of Mediterranean oak woodlands: Understorey dynamics under different shrub management. Agroforestry Systems, 82(2), 161–171. Celesti Grapow, L. (1995). Atlante della Flora di Roma. La distribuzione delle piante spontanee come indicatore ambientale. Comune di Roma-Ufficio Tutela Ambiente. Università di Roma “La Sapienza”-Dip. to di Biologia Vegetale. Quad. Ambiente, 3. Argos Ed. Celesti Grapow, L., Caneva, G., & Pacini, A. (2001). The ora of Colosseum (Rome). Webbia, 56(29), 321–342. Chernetsov, N., Bulyuk, V. N., & Ktitorov, P. (2007). Migratory stopovers of passerines in an oasis at the crossroads of the African and Indian yways. Ringing & Migration, 23(4), 243–251. Chester, J. W. (1997). The great famine: Northern Europe in the early fourteenth century. Princeton University Press. Chiesura, A. (2004). The role of urban parks for the sustainable city. Landscape and Urban Planning, 68, 129–138. Chiheb, L., Ettayib, B., Yassine, N., & Ramzi, H. (2021). Spatio-temporal variation patterns of bird community in the Oasis ecosystem of the North of Algerian Sahara. Journal of Bioresource Management, 8(1), 2. CIESIN. (2007). Center for International Earth Science Information Network. National aggregates of geospatial data collection: Population, landscape, and climate estimates, Version 2 (PLACE II). NASA Socioeconomic Data and Applications Center (SEDAC). Cinzano, P., Falchi, F., & Elvidge, C. D. (2001). The first world atlas of the artificial night sky brightness. Monthly Notices of the Royal Astronomical Society, 328, 689–707. Clarke, D. J., Pearce, K. A., & White, J. G. (2006). Powerline corridor: Degraded ecosystems or wildlife havens? Wildlife Research, 33, 615–626. Clarkson, B. R., Ausseil, A. G. E., & Gerbeaux, P. (2013). Wetland ecosystem services. Ecosystem services in New Zealand: Conditions and trends (pp. 192–202). Manaaki Whenua Press. Clayton, L. W. (2003). Identity and the natural environment: The psychological significance of nature. MIT Press. Clayton, S., & Opotow, S. (2003). Introduction: Identity and the natural environment. In S. Clayton & S. Opotow (Eds.), Identity and the natural environment (pp. 1–24). MIT Press. Clements, F. E. (1936). Nature and structure of the climax. The Journal of Ecology, 24, 252–284. Clevenot, C., Carré, C., & Pech, P. (2018). A review of the factors that determine whether stormwater ponds are ecological traps and/or high-quality breeding sites for amphibians. Frontier in Ecology and Evolution, 6, 40. Colantoni, P., Gabbianelli, G., Manici, F., & Bertoni, W. (1997). Coastal defence by breakwaters and sea-level rise: The case of the Italian northern Adriatic Sea. In F. Briand & A. Maldonado (Eds.), Transformations and evolution of the Mediterranean coastline (n special 18, CIESM Science Series n 3) (pp. 133–150). Bulletin de l’Institute océanographique. Conradson, D. (2005). Landscape, care and the relational self: Therapeutic encounters in rural England. Health & Place, 11(4), 337–348.

References

389

Corlett, R. T., Primack, R. B., Devictor, V., Maas, B., Goswami, V. R., Bates, A. E., . . . Cumming, G. S. (2020). Impacts of the coronavirus pandemic on biodiversity conservation. Biological Conservation, 246, 108571. Costa, A., Pereira, H., & Madeira, M. (2009). Landscape dynamics in endangered cork oak woodlands in Southwestern Portugal (1958–2005). Agroforestry Systems, 77(2), 83. Cowardin, L.M., Carter, V., Golet, F.C., LaRoe, E.T. (1979) Classification of wetlands and deepwater habitats of the United States. U.S. Department of the Interior. Fish and Wildlife Service. Office of Biological Services. Dall, S. R. X., & Johnstone, R. A. (2002). Managing uncertainty: Information and insurance under the risk of starvation. Philosophical Transactions of the Royal Society B, 357, 1519–1526. Danielsen, F., Sorensen, M. K., Olwig, M. F., Selvam, V., Parish, F., Burgess, N. D., Hiraishi, T., Karunagaran, V. M., Rasmussen, M. S., Hansen, L. B., et al. (2005). The Asian tsunami: A protective role for coastal vegetation. Science, 310, 643–643. Darveau, M., Beauchesne, P., Belanger, L., Huot, J., & Larue, P. (1995). Riparian forest strips as habitat for breeding birds in boreal forest. Journal of Wildlife Management, 59, 67–78. Davie, J. M., Roper, T. J., & Shepherdson, D. J. (1987). Seasonal distribution of road kills in the European badger (Meles meles). Journal of Zoology, 211, 525–529. Davies, S. R., Sayer, C. D., Greaves, H., Siriwardena, G. M., & Axmacher, J. C. (2016). A new role for pond management in farmland bird conservation. Agriculture, Ecosystems and Environment, 233, 179–191. de Jong, J., & Stremke, S. (2020). Evolution of energy landscapes: A regional case study in the western Netherlands. Sustainability, 12(11), 4554. de Oliveira, E. A., Marimon-Junior, B. H., Marimon, B. S., Iriarte, J., Morandi, P. S., Maezumi, S. Y., . . . Feldpausch, T. R. (2020). Legacy of Amazonian Dark Earth soils on forest structure and species composition. Global Ecology and Biogeography, 29(9), 1458–1473. Deacon, C., Samways, M. J., & Pryke, J. S. (2018). Artificial reservoirs complement natural ponds to improve pondscape resilience in conservation corridors in a biodiversity hotspot. PLoS One. https://doi.org/10.1371/journal.pone.0204148 Dearborn, D. C., & Kark, S. (2010). Motivations for conserving urban biodiversity. Conservation Biology, 24(2), 432–440. di Castri, F. (1981). Mediterranean-type shrublands of the world. In F. di Castri, D. W. Goodall, R. L. Specht, F. Castri, & V. Balaji (Eds.), Tourism, biodiversity and information (pp. xiii–xxi). Backhuys Publishers. di Castri, F., & Balaji, V. (2002). Tourism, biodiversity and information. Annals of Tourism Research, 1(29), 269–270. Díaz, M., Pulido, F. J., & Marañón, T. (2003). Diversidad biológica y sostenibilidad ecológica y económica de los sistemas adehesados. Revista Ecosistemas, 12(3). Dislich, C., et al. (2017). A review of the ecosystem functions in oil palm plantations, using forest as reference system. Biological Review, 92, 1539–1569. Donovan, T. (2015). Feral cities: Adventures with animals in the urban jungle. Chicago Review Press. Doshita, M. (2010). Rural landscape and tourism development in Japan: A case study of Kita village, Miyama Town, Kyoto. Senri Ethological Studies, 76, 177–191. Dossa, E. L., Fernandes, E. C. M., Reid, W. S., & Ezui, K. (2008). Above-and belowground biomass, nutrient and carbon stocks contrasting an open-grown and a shaded coffee plantation. Agroforestry Systems, 72(2), 103–115. Driskell, J. E., & Salas, E. (Eds.). (2013). Stress and human performance. Psychology Press. Dulias, R. (2016). The impact of mining on the landscape: A study of the Upper Silesian Coal Basin in Poland. Springer. Dunn, E. H., & Tessaglia-Hymes, D. L. (1999). Birds at your feeder: A guide to feeding habits, behavior, distribution, and abundance. W.W. Norton & Company.

390

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

El Amrousi, M. E., Paleologos, E. K., Caratelli, P., & Elhakeem, M. (2018). Are garden cities in the desert sustainable? The oasis city of Al Ain in the Emirate of Abu Dhabi. IRSPDA International, 6A(1), 79–94. Ellis, D., & Schwartz, R. (2016). The roles of an urban parks system. www.worldurbanparks.org Erbe, C., Dunlop, R., & Dolman, S. (2018). Effects of noise on marine mammals. In H. Slabbekoorn, R. J. Dooling, A. N. Popper, & R. R. Fay (Eds.), Effects of anthropogenic noise on animals. Springer. Erbe, C., Marley, S. A., Schoeman, R. P., Smith, J. N., Trigg, L. E., & Embling, C. B. (2019). The effects of ship noise on marine mammals – A review. Frontiers in Marine Science, 6, 606. Erickson, C. (2003). Historical ecology and future explorations. In J. Lehmann, D. C. Kern, B. Glaser, & W. I. Woods (Eds.), Amazonian dark earths (pp. 455–500). Kluwer. Erickson, W. P., Johnson, G. D., Strickland, M. D., Young, D. P., Jr., Sernka, K. J., & Good, R. E. (2001). Avian collision with wind turbines: A summary of existing studies and comparisons to other sources of avian collision mortality in the United States. National Wind Coordinating Committee (NWCC) Resource Document. European Network of Transmission System Operators for Electricity. (2012). Excell attachment “YS & AR 2012 Table 1 operational data 2012” of yearly statistics & adequacy retrospect 2012. Report from ENTSOE, Brussels, Belgium, 62 pp. Evans, M. J., Roberts, A., & Nelson, P. (2001). Ethnographic landscapes. CRM-WASHINGTON, 24(5), 53–56. Faeth, S. H., Bang, C., & Saari, S. (2011). Urban biodiversity: Patterns and mechanisms. Annals of the New York Academy of Sciences, 1223(1), 69–81. Fairhead, J., & Leach, M. (2009). Amazonian dark earths in Africa? In W. I. Woods et al. (Eds.), Amazonian dark earths: Wim Sombrek’s vision. Springer. Falchi, F., Cinzano, P., Duriscoe, D., Kyba, C. C. M., Elvidge, C. E., Baugh, K., Portnov, B. A., Rybnikova, N. A., & Furgoni, R. (2016). The new world atlas of artificial night sky brightness. Science Advances, 2(6), e1600377. https://doi.org/10.1126/sciadv.1600377 FAO (2011) TH estate of the world’s land and water resources for food and agriculture. . FAO. (2019). https://www.fao.org. Accessed May 2019. Faria, D., Laps, R. R., Baumgarten, J., & Cetra, M. (2006). Bat and bird assemblages from forests and shade cacao plantations in two contrasting landscapes in the Atlantic Forest of southern Bahia, Brazil. Biodiversity & Conservation, 15(2), 587–612. Farina, A. (1995). Cultural landscapes and fauna. In B. van Droste, H. Plachter, & M. Rossler (Eds.), Cultural landscapes of universal value (pp. 60–77). Gustav Fischer. Farina, A. (2006). Il paesaggio cognitivo. Una nuova entità ecologica. Franco Angeli. Farina, A. (2014). Soundscape ecology. Springer. Farina, A. (2018). Rural sanctuary: An ecosemiotic agency to preserve human cultural heritage and biodiversity. Biosemiotics, 11(1), 139–158. Farina, A. (2019). Hybrid nature: Effects on environmental fundamentals and species’ semiosis. Biosemiotics, 1–20. Farina, A. (2021). Ecosemiotic landscape: A novel approach to reconcile environment and humanities. Cambridge Elements. Cambridge University Press. Farina, A., & Belgrano, A. (2004). The eco-field: A new paradigm for landscape ecology. Ecological Research, 19(1), 107–110. Farina, A., & Belgrano, A. (2006). The eco-field hypothesis: Toward a cognitive landscape. Landscape Ecology, 21(1), 5–17. Farina, A., Bogaert, J., & Schipani, I. (2005). Cognitive landscape and information: New perspectives to investigate the ecological complexity. Biosystems, 79, 235–240. Farina, A., Scozzafava, S., & Napoletano, B. (2007). Therapeutic landscapes: Paradigms and application. Journal of Mediterranean Ecology, 8, 9–13. Farina, A., Pieretti, N., & Morganti, N. (2013). Acoustic patterns of an invasive species: The Red-billed Leiothrix (Leiothrix lutea Scopoli 1786) in a Mediterranean shrubland. Bioacoustics, 22(3), 175–194.

References

391

Fennessy, M. S., Jacobs, A. D., & Kentula, M. E. (2007). An evaluation of rapid methods for assessing the ecological condition of wetlands. Wetlands, 27(3), 543–560. Ferraz de Oliveira, M. I., Lamy, E., Bugalho, M. N., Vaz, M., Pinheiro, C., Abreu, M. C., . . . SalesBaptista, E. (2013). Assessing foraging strategies of herbivores in Mediterranean oak woodlands: A review of key issues and selected methodologies. Agroforestry Systems, 87, 1421–1437. https://doi.org/10.1007/s10457-013-9648-3 Festin, E. S., Tigabu, M., Chileshe, M. N., Syampungani, S., & Odén, P. C. (2019). Progresses in restoration of post-mining landscape in Africa. Journal of Forestry Research, 30(2), 381–396. Firmino, A. (1999). Agriculture and landscape in Portugal. Landscape and Urban Planning, 46, 83–91. Fischer, J., & Lindenmayer, D. B. (2007). Landscape modification and habitat fragmentation: A synthesis. Global Ecology and Biogeography, 16, 265–280. Fischer, R., Martin, C. O., & Fischenich, J. C. (2000). Improving riparian buffer strips and corridors for water quality and wildlife. In International conference on riparian ecology and management in multi-land use watersheds. American Water Resource Association. Fischer, R. A., Martin, C. O., Ratti, J. T., & Guidice, J. (2001). Riparian terminology: Confusion and clarification. ERDC TN-EMRRP-SR-25. Frankowski, P., Zbierska, J., Staniszewski, R., & Kayzer, D. (2019). Effect of newly created water reservoirs on agricultural landscape stability. Polish Journal of Environmental Studies, 28(5), 3173–3178. Fukamachi, K., Oku, H., & Nakashizuka, T. (2001). The change of a satoyama landscape and its causality in Kamiseya, Kyoto Prefecture, Japan between 1970 and 1995. Landscape Ecology, 16(8), 703–717. Fukamachi, K., Miki, Y., Oku, H., & Miyoshi, I. (2011). The biocultural link: Isolated trees and hedges in Satoyama landscapes indicate a strong connection between biodiversity and local cultural features. Landscape and Ecological Engineering, 7(2), 195–206. Gaertner, M., Wilson, J. R., Cadotte, M. W., MacIvor, J. S., Zenni, R. D., & Richardson, D. M. (2017). Non-native species in urban environments: Patterns, processes, impacts and challenges. Biological Invasions, 19(12), 3461–3469. Garibaldi, A., & Turner, N. (2004). Cultural keystone species: Implications for ecological conservation and restoration. Ecology and Society, 9(3), 1. Gaspar, P., Mesías, F. J., Escribano, M., De Ledesma, A. R., & Pulido, F. (2007). Economic and management characterization of dehesa farms: Implications for their sustainability. Agroforestry Systems, 71(3), 151–162. Gaston, K. J., Davies, T. W., Bennie, J., & Hopkins, J. (2012). Reducing the ecological consequences of night-time light pollution: Options and developments. Journal of Applied Ecology, 49(6), 1256–1266. Gateau-Rey, L., Tanner, E. V., Rapidel, B., Marelli, J. P., & Royaert, S. (2018). Climate change could threaten cocoa production: Effects of 2015-16 El Niño-related drought on cocoa agroforests in Bahia, Brazil. PLoS One, 13(7), e0200454. Gelling, M., & Cole, A. (2014). The landscape of place-names. Paul Watkins Publishing. Gibson, J. J. (1966). The senses considered as perceptual systems. Cornell University Press. Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2010). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography. https://doi.org/10.1111/j.1466-8238.2010. 00584.x Glaser, B., Haumaier, L., Guggenberger, G., & Zech, W. (2001). The ‘Terra Preta’ phenomenon: A model for sustainable agriculture in the humid tropics. Naturwissenschaften, 88(1), 37–41. Godinho, C., & Rabaça, J. E. (2011). Birds like it Corky: The in uence of habitat features and management of ‘montados’ in breeding bird communities. Agroforestry Systems, 82(2), 183–195. Gould, P., & White, R. (1986). Mental maps. Allen & Unwin.

392

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Grandcolas, P., Justine, J.-L. (2020). Covid-19 or the pandemic of mistreated biodiversity. The Conservation, 2020. Greenberg, R., Perfecto, I., & Philpott, S. M. (2008). Agroforests as model systems for tropical ecology. Ecology, 89, 913–914. Griffin, D. R. (1976). The audibility of frog choruses to migrating birds. Animal Behaviour, 24, 421–427. Griffin, D. R., & Hopkins, C. D. (1974). Sound audible to migrating birds. Animal Behaviour, 22, 672–678. Halladay, D., & Gilmour, D. A. (Eds.). (1995). Conservation biodiversity outside protected areas. The role of traditional agro-ecosystems. IUCN. Hamamouche, M. F., Kuper, M., Amichi, H., Lejars, C., & Ghodbani, T. (2017). New reading of Saharan agricultural transformation: Continuities of ancient oases and their extensions (Algeria). World Development, 107, 210–223. Harris, L. D. (1984). The fragmented forest. The University of Chicago Press. Hassall, C. (2014). The ecology and biodiversity of urban ponds. Wiley Interdisciplinary Reviews: Water, 1(2), 187–206. Hassall, C., & Anderson, S. (2015). Stormwater ponds can contain comparable biodiversity to unmanaged wetlands in urban areas. Hydrobiologia, 745(1), 137–149. Higham, N. J., & Ryan, M. J. (2011). Place-names, language and the Anglo-Saxon landscape. Pubns Manchester Centre for Anglo-Saxon studies (Vol. 10, p. 260). Boydell & Brewer, Boydell Press. Hirose, N. (2002). An ecological approach to embodiment and cognition. Cognitive Systems Research, 3(3), 289–299. Hobbs, R. J., Higgs, E., & Harris, J. A. (2009). Novel ecosystems: Implications for conservation and restoration. Trends in Ecology & Evolution, 24(11), 599–605. Hodges, M. F., & Krementz, D. G. (1996). Neotropical migratory breeding bird communities in riparian forests of different widths along the Altamaha River, Georgia. Wilson Bulletin, 108, 496–506. Holt, M. N., Noren, D. P., Veirs, V., Emmons, C. K., & Veirs, S. (2008). Speaking up: Killer whales (Orcinus orca) increase their call amplitude in response to vessel noise. Journal of the Acoustical Society of America, 125(1), EL27–EL32. Hudson-Rodd, N. (1998). Nineteenth century Canada: Indigenous place of dis-ease. Health and Place, 4, 55–66. ICRAF. (2000). Strategy to the year 2000. World Agroforestry. Imbert, D. (2018). Hurricane disturbance and forest dynamics in East Caribbean mangroves. Ecosphere, 9(7), e02231. Indrawan, M., Yabe, M., Nomura, H., & Harrison, R. (2014). Deconstructing satoyama – The socio-ecological landscape in Japan. Ecological Engineering, 64, 77–84. Ingold, T. (2000). The perception of the environment. Routledge. Iriarte, J., Elliott, S., Maezumi, S. Y., Alves, D., Gonda, R., Robinson, M., . . . Handley, J. (2020). The origins of Amazonian landscapes: Plant cultivation, domestication and the spread of food production in tropical South America. Quaternary Science Reviews, 248, 106582. Iwata, Y., Fukamachi, K., & Morimoto, Y. (2011). Public perception of the cultural value of Satoyama landscape types in Japan. Landscape and Ecological Engineering, 7(2), 173–184. Jahangiri, M. H. F. (2012). The relations between the fractal properties of the river networks and the river ow time. International Journal of Geological and Environmental Engineering, 6(3), 111–116. Jha, S., Bacon, C. M., Philpott, S. M., Mendez, V. E., Laderach, P., & Rice, R. A. (2014). Shade coffee: Update a disappearing refuge for biodiversity. Bioscience, 64(5), 416–428. Jiao, Y., Ding, Y., Zha, Z., & Okuro, T. (2019). Crisis of biodiversity and ecosystem services in Satoyama landscape of Japan: A review on the role of management. Sustainability, 11, 454. Johns, N. D. (1999). Conservation in Brazil’s chocolate forest: The unlikely persistence of the traditional cocoa agroecosystem. Environmental Management, 23(1), 31.

References

393

Jorgensen, B. S., & Stedman, R. C. (2001). Sense of place as an attitude: Lakeshore owners attitudes toward their properties. Journal of Environmental Psychology, 21, 233–248. Kabisch, N., Korn, H., Stadler, J., & Bonn, A. (Eds.). (2017). Theory and practice of urban sustainability transitions. Nature-based solutions to climate change adaptation in urban areas (2017). Springer. Kaltenborn, B. P. (1998). Effects of sense of place on responses to environmental impacts. Applied Geography, 18, 169–189. Kaplan, R., & Kaplan, S. (1989). The experience of nature. A psychological perspective. Cambridge University Press. Katoh, K., Sakai, S., & Takahashi, T. (2009). Factors maintaining species diversity in satoyama, a traditional agricultural landscape of Japan. Biological Conservation, 142(9), 1930–1936. Keller, C. M. E., Robbins, C. S., & Hatfield, J. S. (1993). Avian communities in riparian forests of different widths in Maryland and Delaware. Wetlands, 13, 137–144. Kerr, A. M., & Baird, A. H. (2007). Natural barriers to natural disasters. Bioscience, 57(2), 102–103. Kilgo, J. C., Sargent, R. A., Chapman, B. R., & Miller, K. V. (1998). Effect of stand width and adjacent habitat on breeding bird communities in bottomland hardwoods. Journal of Wildlife Management, 62, 72–83. Koike, O. (2014). Rural landscape conservation in Japan: Lessons from the Satoyama conservation program in Kanagawa Prefecture. In N. Kaneko, S. Yoshiura, & M. Kobayashi (Eds.), Sustainable living with environmental risks (pp. 253–262). Springer. Koops, F.-B.J. (1987). Collision victims of high-tension lines in the Netherlands and effects of marking. KRMA report 01282-MOB 86-3048. Krasny, M. E., Lundholm, C., & Plummer, R. (Eds.). (2013). Resilience in social-ecological systems: The role of learning and education (KRMA report 01282-MOB 86-3048). Routledge. Kroodsma, R. L. (1982a). Edge effect on breeding forest birds along a power-line corridor. Journal of Applied Ecology, 19(2), 361–370. Kroodsma, R. L. (1982b). Bird community ecology on power-line corridors in East Tennessee. Biological Conservation, 23, 79–94. Kunc, H. P., McLaughlin, K. E., & Schmidt, R. (2016). Aquatic noise pollution: Implications for individuals, populations, and ecosystems. Proceedings of the Royal Society B: Biological Sciences, 283(1836), 20160839. Kuter, N. (2013). Reclamation of degraded landscapes due to opencast mining. In Advances in landscape architecture. IntechOpen. La Sorte, F. A., Aronson, M. F., Williams, N. S., Celesti-Grapow, L., Cilliers, S., Clarkson, B. D., . . . Winter, M. (2014). Beta diversity of urban oras among European and non-European cities. Global Ecology and Biogeography, 23(7), 769–779. Lal, R., Reicosky, D. C., & Hanson, J. D. (2007). Evolution of the plow over 10,000 years and the rationale for no-till farming. Soil & Tillage Research, 93, 1–12. Lavee, D., Safriel, U. N., & Meilijson, I. (1991). For how long do trans-Saharan migrants stop over at an oasis? Ornis Scandinavica, 33–44. Lee, S. Y., Primavera, J. H., Dahdouh-Guebas, F., McKee, K., Bosire, J. O., Cannicci, S., . . . Record, S. (2014). Ecological role and services of tropical mangrove ecosystems: A reassessment. Global Ecology and Biogeography, 23(7), 726–743. Legagneux, P., & Ducatez, S. (2013). European birds adjust their ight initiation distance to road speed limits. Biology Letters, 9(5), 20130417. https://doi.org/10.1098/rsbl.2013.0417 Lehmann, J. (2009). Terra Petra Nova – Where to from here? In W. Woods, W. G. Teixeira, J. Lehman, C. Steiner, A. M. G. A. WinklerPrins, & L. Rebellato (Eds.), Amazonian dark earths: Wim Somroek’s vision (pp. 473–485). Springer. Lehmann, J., Kern, D. C., Glaser, B., & Woods, W. I. (Eds.). (2003). Amazonian dark earths. Kluwer. Lehmann, J., Kern, D. C., Glaser, B., & Woods, W. I. (Eds.). (2007). Amazonian dark earths: Origin properties management.

394

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Lischeid, G., Kelettka, T., Hollander, M., Steidl, J., Merz, C., Dannowski, R., Hohenbrinìk, T., Leher, C., Onandia, G., Reverey, F., & Patzigìg, M. (2018). Natural ponds in an agricultural landscape: External drivers, internal processes, and the role of the terrestrial-aquatic interface. Limnologica, 68, 5–16. Liu, Y., Xue, J., Gui, D., Lei, J., Sun, H., Lv, G., & Zhang, Z. (2018). Agricultural oasis expansion and its impact on oasis landscape patterns in the southern margin of Tarim basin, Northwest China. Sustainability, 10(6), 1957. Lloyd, S. (1990). The calculus of intricacy. The Sciences, 30(5), 38–44. Longcore, T., Rich, C., Mineau, P., MacDonald, B., Bert, D. G., Sullivan, L. M., et al. (2013). Avian mortality at communication rowers in the United States and Canada: Which species, how many, and where? Biological Conservation, 158, 410–419. Lorentzen, H. F., Benfield, T., Stisen, S., & Rahbek, C. (2020). COVID-19 is possibly a consequence of the anthropogenic biodiversity crisis and climate changes. Danish Medical Journal, 67(5). Lotze, H. K., Lenihan, H. S., Bourque, B. J., Bradbury, R. H., Cooke, R. G., Kay, M. C., Kidwell, S. M., Luken, J. O., Hinton, A. C., & Baker, D. G. (1991). Forest edges associated with powerline corridors and implications for corridors siting. Landscape and Urban Planning, 20(4), 315–324. Luke, S. H., Fayle, T. M., Eggleton, P., Turner, E. C., & Davies, R. G. (2014). Functional structure of ant and termite assemblages in old growth forest, logged forest and oil palm plantation in Malaysian Borneo. Biodiversity and Conservation, 23(11), 2817–2832. Luken, J. O., Hinton, A. C., & Baker, D. G. (1991). Forest edges associated with power-line corridors and implications for corridors siting. Landscape and Urban Planning, 20(4), 315–324. Lynch, K. (1981). Managing the sense of a region. The MIT Press. Manoli, G., Meeijide, A., Huth, N., Knohl, A., Kosugi, Y., Burlando, P., Ghazoul, J., & Fatichi, S. (2018). Ecohydrological changes after tropical forest conversion to oil palm. Environmental Research Letters, 13, 064035. Marois, D. E., & Mitsch, W. J. (2015). Coastal protection from tsunamis and cyclones provided by mangrove wetlands – A review. International Journal of Biodiversity Science, Ecosystem Services & Management, 11(1), 71–83. McDonald, M. A., Hildebrand, J. A., & Wiggins, S. M. (2006). Increases in deep ocean ambient noise in the Northeast Pacific west of San Nicolas Island, California. Journal of the Acoustical Society of America, 120(2), 711–718. McGrane, S. J. (2016). Impacts of urbanization on hydrological and water quality dynamics, and urban water management: A review. Hydrological Sciences Journal, 61(13), 2295–2311. Menant, C. (2003). Information and meaning. Entropy, 5, 193–204. Mentis, M. (2020). Environmental rehabilitation of damaged land. Forest Ecosystems, 7(1), 1–16. Meurk, C. D., & Swaffield, S. R. (2000). A landscape ecological framework for indigenous regeneration in rural New Zealand-Aotearoa. Landscape and Urban Planning, 50(1–3), 129–144. Miller, J. R., & Hobbs, R. J. (2002). Conservation where people live and work. Conservation Biology, 16(2), 330–337. Mira, A., Marques, C. C., Santos, S. M., Rosário, I. T., & Mathias, M. L. (2008). Environmental determinants of the distribution of the Cabrera vole (Microtus cabrerae) in Portugal: Implications for conservation. Mammalian Biology, 73(2), 102–110. Moguel, P., & Toledo, V. M. (1998). Biodiversity conservation in traditional coffee systems of Mexico. Conservation Biology, 13(1), 11–21. Monroe, S. M. (2008). Modern approaches to conceptualizing and measuring human life stress. Annual Review of Clinical Psychology, 4, 33–52. Montalvo, J., Ruiz-Labrador, E., Montoya-Bernabeu, P., & Acosta-Gallo, B. (2019). Rural-urban gradients and human population dynamics. Sustainability, 11, 3107. Montgomery, K. (1996). Sinuosity and fractal dimension of meandering rivers. Area, 28(4), 491–500.

References

395

Moreno, S., & Villafuerte, R. (1995). Traditional management of scrubland for the conservation of rabbits Oryctolagus cuniculus and their predators in Donana National Park, Spain. Biological Conservation, 73, 81–85. Moreno-Marcos, G. M., Obrador, J. J., García, E., Cubera, E., Montero, M. J., Pulido, F., & Dupraz, C. (2007). Driving competitive and facilitative interactions in oak dehesas through management practices. Agroforestry Systems, 70(1), 25–40. Morillas, J. M. B., Gozalo, G. R., González, D. M., Moraga, P. A., & Vílchez-Gómez, R. (2018). Noise pollution and urban planning. Current Pollution Reports, 4(3), 208–219. Morimoto, Y. (2011). What is satoyama? Point of discussion on its future direction. Landscape Ecological Engineering, 7(2), 163–171. Morse, N. B., Pellissier, P. A., Cianciola, E. N., Brereton, R. L., Sullivan, M. M., Shonka, N. K., . . . McDowell, W. H. (2014). Novel ecosystems in the Anthropocene: A revision of the novel ecosystem concept for pragmatic applications. Ecology and Society, 19(2). Naiman, R. J., Decamps, H., & Pollock, M. (1993). The role of riparian corridors in maintaining regional biodiversity. Ecological Applications, 3(2), 209–212. Naveh, Z. (1995). Interactions of landscapes and cultures. Landscape and Urban Planning, 32, 43–54. Naveh, Z., & Dan, J. (1973). The human degradation of Mediterranean landscapes in Israel. In F. di Castri & H. A. Mooney (Eds.), Mediterranean type ecosystems. Ecological studies (analysis and synthesis) (Vol. 7, pp. 373–390). Springer. Nellemann, C., & Cameron, R. D. (1998). Cumulative impacts of an evolving oil-field complex on the distribution of calving caribou. Canadian Journal of Zoology, 76, 1425–1430. Nicholls, R. J., & Cazenave, A. (2010). Sea-level rise and its impact on coastal zones. Science, 328, 1517–1520. Nikora, V. I. (1991). Fractal structures of river plan forms. Water Resources Research, 27(6), 1327–1333. NOAA. (2013). National Oceanic and Atmospheric Administration. 2013. Natural Hazards Viewer. [Internet]. [cited 2013 Nov 9]. Norton, B. A., Evans, K. L., & Warren, P. H. (2016). Urban biodiversity and landscape ecology: Patterns, processes and planning. Current Landscape Ecology Reports, 1(4), 178–192. Nuñez, M. A., & Simberloff, D. (2005). Invasive species and the cultural keystone species concept. Ecology and Society, 10(1). Odling-Smee, F. J., Laland, K. N., & Feldman, M. W. (2003). Niche construction. The neglected process in evolution. Princeton University Press. Olea, L., & San Miguel-Ayanz, A. (2006). The Spanish dehesa. A traditional Mediterranean silvopastoral system linking production and nature conservation. In 21st general meeting of the European Grassland Federation. Olsson, P., Folke, C., & Hahn, T. (2004). Social-ecological transformation for ecosystem management: The development of adaptive co-management of a wetland landscape in southern Sweden. Ecology and Society, 9(4). Onandia, G., Lischeid, G., Kalettka, T., Kleeberg, A., Omari, M., Premke, K., & Arhonditsis, G. B. (2019). Biogeochemistry of natural ponds in agricultural landscape: Lessons learned from modeling a kettle hole in Northeast Germany. Science of the Total Environment, 634, 1615–1630. Ostling, J. L., Butler, D. R., & Dixon, R. W. (2009). The biogeomorphology of mangroves and their role bin natural hazards mitigation. Geography Compass, 3(5), 1607–1624. Palet, J. M., & Orengo, H. A. (2011). The Roman centuriated landscape: Conception, genesis, and development as inferred from the Ager Tarraconensis case. American Journal of Archaeology, 115(3), 383–402. Papa, R., Gargiulo, C., & Zucaro, C. (2014). Urban systems and energy consumptions. TEMA, Journal of Land Use Mobility and environment. Springer.

396

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Paul, C., Weber, M., & Knoke, T. (2017). Agroforestry versus farm mosaic systems – Comparing land-use efficiency, economic returns and risks under climate change effects. Science of Total Environment, 587–588, 22–35. Pereira, P. F., Godinho, C., Vila-Viçosa, M. J., Mota, P. G., & Lourenço, R. (2017). Competitive advantages of the red-billed leiothrix (Leiothrix lutea) invading a passerine community in Europe. Biological Invasions, 19(5), 1421–1430. Perfecto, I., Rice, R. A., Greenberg, R., & Van der Voort, M. E. (1996). Shade coffee: A disappearing refuge for biodiversity. BioSCiemce, 46(8), 598–608. Phillips, J. D. (1993). Interpreting the fractal dimension of river networks. Fractals in Geography, 7, 142–157. Pinay, G., Bernal, S., Abbott, B. W., Lupon, A., Marti, E., Sabater, F., & Krause, S. (2018). Riparian corridors: A new conceptual framework for assessing nitrogen buffering across biomes. Frontiers in Environmental Science, 6, 47. Pinto-Correia, T. P. (1993). Threatened landscape in Alentejo, Portugal: The ‘montado’ and other ‘agro-silvo-pastoral’ systems. Landscape and Urban Planning, 24(1–4), 43–48. Pinto-Correia, T. (2000). Future development in Portuguese rural areas: How to manage agricultural support for landscape conservation? Landscape and Urban Planning, 50(1–3), 95–106. Pinto-Correia, T., & Fonseca, A. M. (2009). Historical perspective of montados: The example of Évora. In Cork oak woodlands on the edge: Ecology, adaptive management, and restoration. Society for Ecological Restoration International. Pinto-Correia, T., & Mascarenhas, J. (1999). Contribution to the extensification/intensification debate: New trends in the Portuguese montado. Landscape and Urban Planning, 46(1–3), 125–131. Plachter, H., & Rossler, M. (1995). Cultural landscapes: Reconnecting culture and nature. In B. van Droste, H. Plachter, & M. Rossler (Eds.), Cultural landscapes of universal value. Gustav Fischer. Plieninger, T. (2007). Compatibility of livestock grazing with stand regeneration in Mediterranean holm oak parklands. Journal for Nature Conservation, 15(1), 1–9. Plieninger, T., & Schaar, M. (2008). Modification of land cover in a traditional agroforestry system in Spain: Processes of tree expansion and regression. Ecology and Society, 13(2), 25. Plieninger, T., & Wilbrand, C. (2001). Land use, biodiversity conservation, and rural development in the dehesas of Cuatro Lugares, Spain. Agroforestry Systems, 51(1), 23–34. Popp, J., Bekefi, E., Duleba, S., & Olah, J. (2019). Multifunctionality of pond fish farms in the opinion of farm managers: The case of Hungary. Review in Aquaculture, 11, 830–847. Potvin, C., Owen, C. T., Melzi, S., & Beaucage, P. (2005). Biodiversity and modernization in four coffee-producing villages of Mexico. Ecology and Society, 10(1). Prugh, L. R., Hodges, K. E., Sinclair, A. R. E., & Brashares, J. S. (2008). Effect of habitat area and isolation on fragmented animal populations. Proceedings of the National Academy of Sciences, 105, 20770–20775. Pulliam, R. (1988). Sources-sinks, and population regulation. American Naturalist, 132, 652–661. Purwanto, E., Santoso, H., Jelsma, I., Widayati, A., Nugroho, H. Y., & van Noordwijk, M. (2020). Agroforestry as policy option for forest-zone oil palm production in Indonesia. Land, 9(12), 531. Reynolds, S. J., Galbraith, J. A., Smith, J. A., & Jones, D. N. (2017). Garden bird feeding: Insights and prospects from a north-south comparison of this global urban phenomenon. Frontiers of Ecology and Evolution. https://doi.org/10.3389/fevo.2017.00024 Rice, R. A., & Greenberg, R. (2000). Cacao cultivation and the conservation of biological diversity. Ambio, 29, 167–173. Rinaldo, A., Rodriguez-Iturbe, I., Rigon, R., Ijjasz-Vasquez, E., & Bras, R. L. (1993). Selforganized fractal river networks. Physical Review Letters, 70(6), 822–825. Risser, P. G. (1995). The status of the science examining ecotones: A dynamic aspect of landscape is the area of steep gradients between more homogeneous vegetation associations. Bioscience, 45(5), 318–325.

References

397

Rosalino, L. M., do Rosario, J., & Santos-Reis, M. (2009). The role of habitat patches on mammalian diversity in cork oak agroforestry systems. Acta Oecologica, 35(4), 507–512. Roser, M., Ritchie, H., & Ortiz-Ospina, E. (2019). https://ourworldindata.org/world-populationgrowth. Accessed May 2019. Russart, K. L., & Nelson, R. J. (2018). Artificial light at night alters behavior in laboratory and wild animals. Journal of Experimental Zoology Part A: Ecological and Integrative Physiology, 329(8–9), 401–408. Safriel, U. N., & Lavee, D. (1988). Weight changes of cross-desert migrants at an oasis – Do energetic considerations alone determine the length of stopover? Oecologia, 76(4), 611–619. Sato, S. (2006). Influence of the invasion of Leiothrix lutea on a native avifauna in a natural beech forest on Mt. Karimata, Kyushu [Japan]. Bulletin of the Forestry and Forest Products Research Institute (Japan). Sawyer, H., Kauffman, M. J., Middleton, A. D., Morrison, T. A., Nielson, R. M., & Wyckoff, T. B. (2013). A framework for understanding semi-permeable barrier effects on migratory ungulates. Journal of Applied Ecology, 50, 68–78. Schafer, R. M. (1977). The tuning of the world. McClelland and Steward Limited. Scher, O., Chavaren, P., Despreaux, M., & Thiery, A. (2004). Highway stormwater detention ponds as biodiversity islands? Archives des Sciences, 57, 121–130. Schlaepfer, M. A., Runge, M. C., & Sherman, P. W. (2002). Ecological and evolutionary traps. Trends in Ecology & Evolution, 17(10), 474–480. Schneider, D. (2011). Hybrid nature: Sewage treatment and the contradictions of the industrial ecosystem. MIT Press. Schroth, G., & Harvey, C. A. (2007). Biodiversity conservation in cocoa production landscapes: An overview. Biodiversity and Conservation, 16(8), 2237–2244. Schwartzman, S., Moreira, A., & Nepstad, D. (2000). Rethinking tropical forest conservation: Perils in parks. Conservation Biology, 14(5), 1351–1357. Serrano, J., Shahidian, S., Marques da Silva, J., Paixão, L., Carreira, E., Pereira, A., & Carvalho, M. (2020). Climate changes challenges to the management of Mediterranean montado ecosystem: Perspectives for use of precision agriculture technologies. Agronomy, 10(2), 218. Shang, P., & Kamae, S. (2005). Fractal nature of time series in the sediment transport phenomenon. Chaos, Solitons & Fractals, 26(3), 997–1007. Simkin, T., & Fiske, R. S. (1983). Krakatau 1883: The volcanic eruption and its effects. Smithsonian Institution Press. Sirisrisak, T., & Akagawa, N. (2007). Cultural landscape in the world heritage list: Understanding on the gap and categorisation. City & Time, 2(3), 11–20. Smith, W. T., & Cameron, R. D. (1985). Reactions of large groups of caribou to a pipeline corridor on the Arctic Coastal Plain of Alaska. Arctic, 53–57. Sopuck, L. G., & Vernam, D. J. (1986). Distribution and movements of moose (Alces alces) in relation to the Trans-Alaska oil pipeline. Arctic, 39(2), 138–144. Sousa, A., & Garcia-Murillo, P. (2001). Can place names be used as indicators of landscape changes? Application to the Doñana Natural Park (Spain). Landscape Ecology, 16, 391–406. Spackman, S. C., & Hughes, J. W. (1995). Assessment of minimum stream corridor width for biological conservation: Species richness and distribution along mid-order streams in Vermont, USA. Biological Conservation, 71, 325–332. Spellenberg, I. F. (1998). Ecological effects of road and traffic: A literature review. Global Ecology and Biogeography Letters, 7, 317–333. Spyra, A. (2017). Acidic, neutral and alkaline forest ponds as. A landscape element affecting the biodiversity of freshwater snails. Science of Nature, 104, 73. https://doi.org/10.1007/s001141495-z Stewart, O. C. (2002). Forgotten fires: Native Americans and the transient wilderness. University of Oklahoma Press. Stoffregen, T. A. (2003). Affordances as properties of the animal-environment system. Ecological Psychology, 15(2), 115–134.

398

9

Human-Dependent Landscapes Around the World – An Ecological Perspective

Supangkat, B., Iskandar, J., Partasasmita, R., & Alfian, R. L. (2018). Planting coffee and take care of forest: A case study on coffee cultivation in the forest carried out among people of Palintang, Highland of Bandung, West Java, Indonesia. Biodiversitas Journal of Biological Diversity, 19(6), 2183–2195. Surová, D., & Pinto-Correia, T. (2008). Landscape preferences in the cork oak Montado region of Alentejo, southern Portugal: Searching for valuable landscape characteristics for different user groups. Landscape Research, 33(3), 311–330. Surová, D., Surový, P., de Almeida Ribeiro, N., & Pinto-Correia, T. (2011). Integrating differentiated landscape preferences in a decision support model for the multifunctional management of the Montado. Agroforestry Systems, 82(2), 225–237. Swartz, T. M., Coon, J. J., Mattes, J. R., & Miller, J. R. (2019). Identifying opportunities to conserve farm ponds on private lands: Interaction of social, ecological, and historical data. Land, 8, 127. https://doi.org/10.3390/land8090127 Tellería, J. L. (2001). Passerine bird communities of Iberian dehesas: A review. Animal Biodiversity and Conservation, 24(2), 67–78. Thaxter, C. B., Buchanan, G. M., Carr, J., Butchart, S. H. M., Newbold, T., Green, R. E., et al. (2017). Bird and bat species’ global vulnerability to collision mortality at wind farms revealed through a trait-based assessment. Proceedings of the Royal Society B. https://doi.org/10.1098/ rspb.2017.0829 Thomashow, M. (1995). Ecological identity. Becoming a reflective environmentalist. MIT Press. Tian, S., Wang, W., Shang, H., & Peng, H. (2013). Comparison of traditional methods and fractal dimension method in river patterns discrimination. Research Journal of Applied Sciences, Engineering and Technology, 5(23), 5450–5456. Truax, B. (2001). Acoustic communication. Ablex Publishing. Truitt, A. M., Granek, E. F., Duveneck, M. J., Goldschmidt, K. A., Jordan, M. P., & Yazzie, K. C. (2015). What is novel about novel ecosystems: Managing change in an ever-changing world. Environmental Management, 55, 1217–1226. Turrión, D., Morcillo, L., Alloza, J. A., & Vilagrosa, A. (2021). Innovative techniques for landscape recovery after clay mining under Mediterranean conditions. Sustainability, 13(6), 3439. Ulanowicz, R. E. (1983). Ecology, the ascendant perspective. Columbia University Press. UN. (2019). https://www.unenvironment.org/regions/asia-and-pacific/regional-initiatives/ supporting-resource-efficiency/sustainable-cities Vale, T. R. (2002). The pre-European landscape of the United States: Pristine or humanized? In T. R. Vale (Ed.), Fire, native peoples, and the natural landscape (pp. 1–39). Island Press. Valiela, I., Bowen, J. L., & York, J. K. (2001). Mangrove forests: One of the world’s threatened major tropical environments. Bioscience, 51, 807–815. Vanbergen, A. J., Watt, A. D., Mitchell, R., Truscott, A.-M., Palmer, S. C. F., Ivits, E., Eggleton, P., Jones, T. H., & Sousa, J. P. (2007). Scale-specific correlations between habitat heterogeneity and soil fauna diversity along a landscape structure gradient. Oecologia, 153, 713–725. Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R., & Cushing, C. E. (1980). The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences, 37, 130–137. Vargas, J. D., Calvo, J. C., & Aparicio, M. A. (1995). Red deer (Cervus elaphus hispanicus) management in the dehesa system in Central Extremadura, Spain. Agroforestry Systems, 29(1), 77–89. Vasseur, C., Joannon, A., Aviron, S., Burel, F., & Meynard, J.-M. (2012). The cropping system mosaic: How does the hidden heterogeneity of agricultural landscapes drive arthropod populations? Agriculture, Ecosystems and Environment, 166, 3–14. Vijay, V., Pimm, S. L., Jenkins, C. N., & Smith, S. J. (2016). The impacts of oil palm on recent deforestation and biodiversity loss. PLoS One, 11(7), e0159668. Vistnes, I., Nellemann, C., Jordhoy, P., & Strand, O. (2004). Effects of infrastructure on migration and range use of wild reindeer. Journal of Wildlife Management, 68(1), 101–108. Vladimir, M., & Madalina, C. (2019). Optimizing urban landscapes in regard to noise pollution. Procedia Manufacturing, 32, 161–166.

References

399

von Droste, B., Plachter, H., & Rossler, M. (Eds.). (1995). Cultural landscapes of universal value. Gustav Fischer. Von Uexküll, J. (1982 [1940]). The theory of meaning. Semiotica 42, 25–82. Vought, B.-M., Dahl, J., Pedersen, C. L., & Lacoursière, J. O. (1994). Nutrient retention in riparian Ecotones. Ambio, 23(6), 342–348. Vrablikova, J., Wildova, E., & Vrablik, P. (2016). Sustainable development and restoring the landscape after coal mining in the northern part of the Czech Republic. Journal of Environmental Protection, 7(11), 1483–1496. Waite, M., Cohen, E., Torbey, H., Piccirilli, M., Tian, Y., & Modi, V. (2017). Global trends in urban electricity demands for cooling and heating. Energy, 127, 786–802. Wanger, T. C., Saro, A., Iskandar, D. T., Brook, B. W., Sodhi, N. S., Clough, Y., & Tscharntke, T. (2009). Conservation value of cacao agroforestry for amphibians and reptiles in SouthEast Asia: Combining correlative models with follow-up field experiments. Journal of Applied Ecology, 46(4), 823–832. Watkinson, A. R., & Sutherland, W. J. (1995). Sources, sinks and pseudo-sinks. Journal of Animal Ecology, 64, 126–130. West, G. B., Brown, J. H., & Enquist, B. J. (1997). A general model for the origin of allometric scaling laws in biology. Science, 276(5309), 122–126. Whitefield, A. K., & Becker, A. (2014). Impacts of recreational motorboats on fishes: A review. Marine Pollution Bulletin, 83, 24–31. Wilson, K. (2003). Therapeutic landscapes and first nations peoples: An exploration of culture, health and place. Health and Place, 9, 83. Winchester, M., & McGrath, J. (2017). Therapeutic landscapes. Medicine Anthropology Theory, 4(1), i–x. https://doi.org/10.17157/mat.4.1.472 Woods, W. I., & McCann, J. M. (1999). The anthropogenic origin and persistence of Amazonian dark earths. In Yearbook. Conference of Latin Americanist geographers (pp. 7–14). Wu, J. (2014). Urban ecology and sustainability: The state-of-the-science and future directions. Landscape and Urban Planning, 125, 209–221. Yan, X., & He, S. (2020). The co-evolution of therapeutic landscape and health tourism in bama longevity villages, China: An actor-network perspective. Health & Place, 66, 102448. Zambrano, L., Aronson, M. F. J., & Fernandez, T. (2019). The consequences of landscape fragmentation on socio-ecological patterns in a rapidly developing urban area: A case study of the National Autonomous University of Mexico. Frontiers in Environmental Science, 7, Article 152. Zedler, J. B. (2000). Progress in wetland restoration ecology. TREE, 15(10), 402–407.

Chapter 10

Methods in Landscape Ecology

Synthesis The geographical dimensions of landscapes require the collection and the processes of the geometric attributes of patches like perimeters, shape, size, distance between other elements, fractal dimension, etc. The geographical data are obtained using images from satellites, airplanes, or drones converted in numerical matrices and managed by geographical information systems (GIS). Several metrics from Euclidean geometry to fractal geometry are today available. In particular, we consider nonspatial metrics like richness, diversity, evenness associated with relevant characters of the patches. Spatial metrics represent the focus of a landscape quantitative analysis. Spatial metrics are represented by relative patchiness, entropy, contagion. The shape of patches is a character that has a relevant role in animal movements and plant dispersion. Perimeter–area ratio, area–perimeter ratio, related circumscribing circle, shape indices, patch density, largest patch size, border contrast, total and relative core area are some of the utilized metrics. Distance between patches is measured by proximity indices. Texture metrics are utilized to analyze patterns of brightness variations within an image, and boundaries metrics are used to evaluate the level of contrast between ecotones. Finally, fractal geometry is applied to analyze at a multiscale level the properties of shape, size, and distance of patches. Spatial analysis in landscape ecology finds a powerful tool in the Geographic Information Systems. These systems perform spatial manipulation at a broad range of geographical scales and connect field data to a geographic platform implementing the capacity of landscape ecology to manage and design in the real world.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Farina, Principles and Methods in Landscape Ecology, Landscape Series 31, https://doi.org/10.1007/978-3-030-96611-9_10

401

402

10.1

10 Methods in Landscape Ecology

Introduction

This chapter represents an attempt to describe, using different approaches, the geometrical attributes of landscapes and the methods to collect quantitative information. The argument remains extremely difficult to be reduced to a simple presentation due to the plethora of approaches and methods used in very different circumstances, as argued by Hargis et al. (1997). The landscape approach may be so diverse that it is not possible to comprehensively review all the methods and to indicate standard methodologies. Many of those have been lend by geobotanic, animal population analysis, behavior ecology, etc. and the need to integrate environmental information like topography into the landscape analysis, as emphasized by Dorner et al. (2002). This chapter would not be as complete as the books devoted specifically to the landscape ecology methods (see suggested readings), but aims to introduce and orient the reader toward the main quantitative approaches to measuring landscape features. Consequently, it is important to immediately clarify that the study of the landscape requires not only metrics, but also additional tools like the availability of Geographical Images Databases (GID), Spatial Statistics Metrics (SSM), Geographic Information Systems (GIS), Remote Sensing Techniques (RST), and the Global Positioning Systems (GPS) that are used in many other circumstances. Survey from air (satellites, airplanes, balloons, helicopters) has been facilitated by the extensive use of low-altitude drones (unmanned aerial vehicles – UAV), a recent technology that is the result of an integration between visual sensors, phones, GPS, and more (Tal & Altschuld, 2021). Drones are becoming a popular instrument to collect aerial images at high resolution that can be used to draw detailed maps to investigate the relationship between land cover and focus species (e.g., Anderson & Gaston, 2013; Habel et al., 2016) (Fig. 10.1). The advancement of the technologies in the remote sensing, like the use of threedimensional (3D) laser scanners (e.g., Terrestrial LiDAR (TLS) and Aerial LiDAR (ALS) (e.g., Richardson et al., 2014), allow to obtain detailed models of terrain and vegetation. In fact, these methodologies are successfully applied in geology, geography, navigation, agronomy, climatic economics, social sciences forecasting, epidemiology, etc. The chapter has been divided into two main parts: Landscape Metrics and Geographic Information Systems. The last argument is described only in terms of principles without entering into the technical details due to the economy of the book.

10.2

Metrics in Landscape Ecology

403

Fig. 10.1 Example of use of a drone to evaluate habitat suitability of two Lycaenid butter ies: Polyommatus icarus and P. bellargus. (A) High-resolution aerial picture, (B) habitat suitability model for larval habitats, and (C) larval host plant occurrences occupied by larvae. (From Habel et al., 2016, with permission)

10.2

Metrics in Landscape Ecology

This section attempts to describe metrics to quantify and characterize the geometrical attributes of landscapes. It is difficult to reduce the plethora of metrics applied in landscape ecology to a simple presentation because of the variety of very different circumstances and scientific issues addressed by scholars (Hargis et al., 1997; Newman et al., 2019). This chapter is meant to compliment the more complete books devoted specifically to the landscape ecology methods (Gergel & Turner, 2002; McGarigal & Marks, 1995; Turner & Gardner, 1991) by simply introducing and orienting the reader toward the main quantitative approaches used to measure landscape features. Consequently, it is important to immediately clarify that the study of landscape ecology requires a variety of imperative tools like Geodatabases, Spatial Statistics, Geographic Information Systems (GIS), Remote Sensing

404

10 Methods in Landscape Ecology

Non Spatial analysis

Spatial analysis

Multiscalar analysis

Spatial modeling

Fig. 10.2 The principal approaches to landscape metrics

Techniques, and the Global Positioning Systems (GPS) (Groom et al., 2006). In fact, landscape ecology utilizes methodologies that are applied in geology, geography, navigation, cartography, agronomy, climatic economics, social sciences forecasting, epidemiology, and many other spatially explicit fields. In 2020, for example, the use of spatial data to track the transmission of COVID-19 cases across the world in relation to demography, national boundaries, and healthcare systems (https:// coronavirus.jhu.edu/map.html) is a perfect example of how landscape ecology can be linked to other scientific fields (e.g., epidemiology) and human-oriented services. Not surprisingly, there are multiple commercial and open access software platforms that are used in landscape ecology, the selection of which is largely based on the preference of the researcher/analyst. Some of these include FRAGSTAT, (McGarigal & Marks, 1995), LEAPII (Perrera et al., 1997), Patch Analyst (Elkie et al., 1999), and APACK (Mladenoff and DeZonia (2000) for spatial analysis and landscape metrics; MapInfo, ArcGIS, GRASS for GIS-based projects; and Trimble for GPS devices (Farrell & Barth, 1999; Trimble Navigation, 1994). Landscape is a complex entity embedded into a geographical dimension of the environment, and in which a large number of heterogeneous elements may be represented by points, lines, shapes, and distances that exhibit scale dependence, nonlinear dynamics, and emergent properties. For this, the metrics that are requested to collect data are the ones that quantify landscape features focusing on the geometric properties of the land mosaics. To the geometric features that are considered components of the spatial patterns, we must associate processes dealing with distance between elements. The application of spatial metrics to a landscape involves the choice of the more adaptable spatial and temporal scale. Often this scaling process requires the knowledge of the best scale of a specific organisms. For instance, the home range of a wolf requires to operate at a spatial resolution of kilometers, while the home rage of a mouse requires a sub-metric resolution. There are at least four methodological approaches that are applied to obtaining landscape metrics: nonspatial analysis, spatial analysis, multi-scalar analysis, and spatial modeling (Fig. 10.2). The nonspatial analysis uses metrics common to many other ecological approaches like community ecology, population dynamics, etc. The spatial

10.2

Metrics in Landscape Ecology

405

Fig. 10.3 Metrics in landscape ecology can be applied at individuals (A), patches (B), mosaic (C), and finally at landscape scale (D)

elaboration of data is a central affair in landscape ecology, and for this reason we will dedicate a large space to the argument. Many techniques have been borrowed from spatial statistics or geostatistics that are often used for image analysis and fractal geometry. The multi-scalar approach considers the changes in the structures and in functioning of a landscape when different spatial and/or temporal scales are adopted. Euclidean and non-Euclidean geometry are often combined to analyze the complexity of spatial processes and patterns across temporal and spatial scale (Jenerette & Wu, 2001; Lausch & Herzog, 2002). Several metrics are available today to describe landscape patterns, but often the metric sensitivity is not fully validated as argued by Trani and Giles (1999) (see also Baldwin et al., 2004; Bartel, 2000; Hargis et al., 1997; Li & Wu, 2004; McAlpine & Eyre, 2002; Riitters et al., 1995; Tischendorf, 2001). The landscape analysis can be performed at least at four levels of spatial resolution: individual, patch, mosaic and landscape resolution (Fig. 10.3). We intend an individual resolution (plants, animals) or a distinct abiotic object (houses, bridges, etc.). The patch resolution means that the analysis is restricted to a focal spatial unit like an urban garden, a forest gap, a woodlot, or an ecotope. The mosaic resolution represents a window selected for some reason, for instance, by a sampling technique, in which two or more patches are included as part of a system. Finally the landscape is considered a mosaic of patches delimited in extension by significant natural or human-perceived coherences. In this last case, more complicated analyses are necessary to explore the complexity contained into the landscape.

10.2.1 Nonspatial Metrics This section describes some numerical indexes that have a very broad utilization in ecology and biology as well. Specifically, richness, diversity and evenness are very useful to describe nonspatial attributes of the landscape.

406

10.2.1.1

10 Methods in Landscape Ecology

Richness

It is the simplest attribute measurable into a collection of individual species. Richness is defined as the number S of different objects (organisms, patches, etc.) ni present in a collection: S¼

10.2.1.2

X

ni

ð10:1Þ

Diversity

This attribute describes the uncertainty by which we can encounter a new object sampling at random a collection and it is the combination of richness and abundance. It does not exist a universal index but according to the typology of the collection some are more efficient than others to track the information. We present the Simpson and Shannon indices. The first index is particular sensible to the most abundant species and the second to the rarest. Simpson Diversity (Simpson, 1949) γ¼

S X

pi2

ð10:2Þ

i¼1

where S is the number of categories, pi is the relative abundance, pi ¼ ni/N where ni is the abundance of category i and N is the total abundance. Shannon Diversity (Shannon & Weaver, 1949) The variety and relative abundance of objects can be estimated using the Shannon index: H0 ¼

S X

ðpi ln piÞ

ð10:3Þ

i¼1

where pi is the relative importance of the category i.

10.2.1.3

Evenness

This attribute describes the deviance between a maximum equipartition of the objects into a collection (when every category has the same importance). Five indices are proposed:

10.2

Metrics in Landscape Ecology

407

Pielou Evenness (Pielou, 1975, 1977) E1 ¼

H0 ln ðSÞ

ð10:4Þ

where H0 is the Shannon diversity, S is the number of species. Sheldon Evenness (Sheldon, 1969) E2 ¼

eH0 S

ð10:5Þ

where H0 is the Shannon diversity, S is the number of species. Dominance (O’Neill et al., 1988) This index related to the Shannon diversity measures the value of dominance of one land cover on the others: D ¼ Ln n  H 0

ð10:6Þ

where H0 ¼ ∑ pi ln pi and pi is the proportion of the grid cells on the landscape for the land use i selected. n is the number of land-use categories. D is close to 0 when the land cover types present an equi-abundance and is close to 1 when most of the land cover type belongs to one cover type. Heip Evenness (Heip, 1974) E3 ¼

eH0  1 S1

ð10:7Þ

where H0 is the Shannon diversity and S is the richness. Hill Evenness (Hill, 1973) E4 ¼

1 λ

1 eH0

ð10:8Þ

where λ is the Simpson diversity and H0 the Shannon diversity.

10.2.2 Spatial Metrics The metrics that will be described in this section measure the spatial arrangement of the objects. Spatial configuration is recognized as important to assess habitat

408

10 Methods in Landscape Ecology

suitability. In fact, landscapes with different habitat configurations are expected to be used differently by species (McIntyre & Wiens, 2000). Relative Patchiness RP ¼ 100

X X Eij Dij Nb

ð10:9Þ

Eij is the number of edges between patch types i and j, Dij is the dissimilarity value for patch types i and j, and Nb is the total number of edges of pixels (each pixel has four edges). This index measures the contrast of neighboring patch types in a landscape mosaic. See Romme (1982) for an example of application. Entropy This index measures the disorder of pixels for each category. ENT ¼ 

XX

Pij ln Pij

ð10:10Þ

Pij is the probability of a grid point of land use i is adjacent to a grid point of land use j. The Pij value is calculated by dividing the number of cells of type i that are adjacent to j by the total number of cells of type i present in the matrix. Pij ¼ Nij/Ni, where Nij is the number of adjacency between pixels of patch type i and j. Ni is the total number of cells of type i. If Nij ¼ Ni, then Pij ¼ 1 (Cushman, 2018; Li & Reynolds, 1995; O’Neill et al., 1988; Turner et al., 1989). Contagion This metric derives from the information theory (Shannon & Weaver, 1949) and measures the degree of clumping of attributes on raster maps (Riitters et al., 1996). It represents the deviation of the entropy measure from its possible maximum value C ¼ 2 ln m  ENT

ð10:11Þ

where ENT ¼  ∑ ∑ Pij ln (Pij) (see Eq. 10.10). This index measures if cells are aggregated or clumped. 2 ln m represent the maximum possible of probability of adjacency. If value of contagion is high it means that contiguous patches are found on the landscape. If the value is low, the landscape is composed by small (isolated) patches (Fig. 10.4). Relative Contagion This index proposed by Li and Reynolds (1995) utilizes the same components of the Eq. 10.10 but the Entropy is divided by the Max Entropy so that the relative contagion varies between 0 and 1 and represents an evenness index.

10.2

Metrics in Landscape Ecology

409

Moderate contagion

Low contagion

High contagion

Fig. 10.4 Examples of three levels of contagion low, moderate, high in a simulated matrix

RC ¼

1  ENT 2 ln m

ð10:12Þ

Beta Organization Index This index proposed by Ernoult et al. (2003) measures the degree of deviation by which the spatial distribution of landscape entity (e.g., the meadow land cover) is independent from the distribution of another (e.g., soil type). Rs ðLÞ ¼

H ðLÞ þ H ðSÞ  H ðLxSÞ H ðLÞ

ð10:13Þ

where H(l) ¼  ∑ (i land use)pi log pi, where pi is the probability to find the i land use in the study area. H(S) is the marginal entropy of the (geographical or biological) character for which we try to find a spatial concordance H(S) ¼  ∑ (i geographical or biological character)pi log pi. H ðLxSÞ ¼

X

ði land useÞ

X

ð j soil typesÞpij log pij

This index reaches the value of 1 when all land uses area located according to specific environmental niches and consequently the co-occurrence is max, for instance, between land use and soil character.

10.2.3 Patch Shape Metrics Many indexes have been formulated to measure patch shape, especially in a geographical contest (e.g., patch size, perimeter, area–perimeter ratio, corrected perimeter–area [0.282*L/√S], related circumscribing circle [2*(S/π)1/2√longest axis]). These indices have to be adopted and used with caution because often the

410

10 Methods in Landscape Ecology

O

$

%

C

Fig. 10.5 Patch shape remains one of the most important morphological attributes to be measured in a landscape. It is easy to recognize an increase of the perimeter moving from a regular patch A (λx9) to B (λx14) until the most irregular C (λx18). Consequently, changes in the ratio between area and perimeter modify patch functioning and habitat quality

precise ecological relationship with the process investigated is not so easily found. In any way, the approach to the study of patch shape is important for the consequences that patch shape regularity/irregularity have on organisms. We assume as reference model a square or a round patch; the more a patch is irregular and has more edges, the less interior (core) area is available (Fig. 10.5). Irregular patches probably have more heterogeneous processes than regular ones. Habitat suitability, predation risk, edges, microclimatic stresses are some of the direct consequences of an irregular patch. Six indices calculating patch shape are described: Perimeter–Area Ratio The perimeter of each patch is simply divided by its area PARA ¼

L S

ð10:14Þ

where L is the perimeter and S the area. This index varies according to the size of the patch even the shape is constant. An increase in patch size will produce a decrease in the perimeter–area ratio. See Buechner (1989) for an application of field study of mammal dispersion. Area–Perimeter Ratio Three indices are available: pffiffiffiffiffiffi 2 πA γ1 ¼ P

ð10:15Þ

pffiffiffi 4 A γ2 ¼ P

ð10:16Þ

10.2

Metrics in Landscape Ecology

411

γ3 ¼

A P2

ð10:17Þ

γ2 and γ3 for patches represented in a raster format. Where A is the area and P the perimeter. Corrected Perimeter–Area (CPA) This index is corrected for solving the size problems of the equation 10.14 and varies between 0.0, a perfect circle to infinity for an infinitely long and narrow shape. CPA ¼

:282x L pffiffiffi S

ð10:18Þ

where L is the perimeter and S the area. Related Circumscribing Circle (RCC) This index compares the patch size with the size of a circle that can circumscribe the patch.  1=2 2x area π RCC ¼ longest  axis

ð10:19Þ

This index varies between 0.0 to 1.0 as the shape of the patch approaches to a circle. Shape Index S1 (Hulshoff, 1995) S1 ¼

1 X Li Ni Si

ð10:20Þ

where Ni is the number of patches of category i in a map, Li is the perimeter and Si the area of each patch in category i. A high value of this index indicates the presence of many patches with small interiors. Shape Index S2 (Hulshoff, 1995). This index measures of isodiametric attributes of patches S2 ¼

1 X Li pffiffiffiffi Ni 4 Si

ð10:21Þ

where Ni is the number of patches of category i, Li is the perimeter and Si is the size of each patch in the category. The more S2 is far from 1, the more do the patches deviate from an isodiametric shape.

412

10 Methods in Landscape Ecology

Patch Density PDi ¼

ni A

ð10:22Þ

where ni represents the number of patches of category i and A is total area of the matrix. Mean Patch Size n P

MPS ¼

aij

j¼1

ni

ð10:23Þ

where ni is the number of patches of category i and aij is the area of each patch of category i. Largest Patch Size LPS ¼

MaxðaijÞ 100 A

ð10:24Þ

aij is the area of each patch of category i and A is total area of the matrix. Border Length m X

BL ¼

eik

ð10:25Þ

k¼1

where eik is the total length (m) of edge in landscape between patch types (classes) i and k. Border Contrast m P

BC ¼

ðpijkxdik Þ

k¼1

pij

ð10:26Þ

where pijk is the length (m) of edge of patch ij adjacent to patch type (class) k. Total Core Area TCA ¼

n X

aijc

j¼1

aij is the area of each patch of category i and c is the buffer size.

ð10:27Þ

10.2

Metrics in Landscape Ecology

413

Relative Core Area n P

RCA ¼

aijc

j¼1 n P

ð10:28Þ aij

j¼1

aij is the area of each patch of category i and c is the buffer size.

10.2.4 Distance Metrics Distance of a patch or a group of patches from others is an important parameter in the mosaic analysis. Distance is a relevant element in the economy of species because means energy loss for moving, increasing predatory risk and decreasing transportation by vectors, etc. (van Dorp & Opdam, 1987). Distance also means connectedness and connectivity. The calculation of distance can be done according to a combination of possibilities, as discussed in detail by Baker and Cai (1992). The measure of distances can be done according to a different selection of possibilities: 1. 2. 3. 4.

From each patch to all the adjacent neighbors of each patch. From a patch to all others of the same group. From each patch to the single nearest patch of a different group. From a patch of a specific group to another patch of specific group.

The degree of isolation of the patches is measured with the proximity index PX (Gustafson & Parker, 1992): PX ¼

X Sk nK

ð10:29Þ

where Sk is the area of the patch and nK is the nearest neighbor distance of patch K. This index can be scaled as a proportion of the maximum value of PX, then PXs ¼ PX/PXmax where PXmax ¼ E/n, E is half of the total area of landscape separate by the minimum possible n. PXs cannot be used to compare two landscapes with different extents because PXmax changes according to the landscape extent.

414

10 Methods in Landscape Ecology

10.2.5 Texture Metrics The texture measures are adopted to analyze patterns of brightness variations within an image (see Haralick et al., 1973; Musick & Grover, 1991). These measures can be used profitably in landscape ecology to analyze the complexity of the mosaic and the contrast among patches. The spatial co-occurrence probability p(i,j,d,q) where a pixel or a cell of type i is separated by a pixel or a cell of type j by the distance d according to an angle direction q that may be 0  horizontal, 45  right diagonal, 90  vertical, 135  left diagonal. The comparison involves two reciprocal co-occurrences and the matrix produced is symmetric. In Fig. 10.6 are reported three examples of analysis of co-occurrences. Two indexes of homogeneity may be used in the analysis of landscape texture: the Angular Second Moment (ASM) and the Inverse Difference Moment (IDM). Angular Second Moment ASM is the sum of co-occurrences probabilities: ASM ¼

XX

½pði, jÞ2

ð10:30Þ

where p(i, j) is the relative abundance of the cells i that are in adjacency with the cells j. p(i, j) ¼ n(i, j)/tot, where n(i, j) is the number of occurrences of cell i adjacent to cell j. tot ¼ ∑ ∑ n(i, j). ASM increases with mosaic homogeneity because the co-occurrence of identical values have a strong in uence on this index. In homogeneous patches, the co-occurrence of cells with identical value are dominant and the squared probabilities enhance this value. ASM has value 1 when all co-occurrences are identical, but this index is not sensitive to the magnitude of difference between cells of different value.

22

24

23

22

24

23

22

24

23

22

21

22

22

21

22

22

21

22

21

21

21

21

21

21

21

A

21

B

21

C

Fig. 10.6 Three possibilities to calculate the co-occurrence probability between cells or pixels of a matrix. The number of cells indicate the different type of attribute, may be a land cover or vegetation or spectral attribute. In (A) the co-occurrences have been measured along the horizontal axis, in (B) according to four perpendicular directions, and in (C) in all directions. (From Musick & Grover, 1991, modified)

10.2

Metrics in Landscape Ecology

415

Inverse Difference Moment IDM ¼

XX

"

# 1 pði, jÞ 1 þ ði  jÞ2

ð10:31Þ

This index measures the co-occurrences weighted according to the difference between values of i and j. The index has maximum value 1 when all cells or pixel are identical. To be usefully applicable in land analysis, the differences of value of i and j must have some significance (intensity or interval type data). Contrast This index measures the contrast present in the landscape. CON ¼

XX

ði  jÞ2 x Pij

ð10:32Þ

Temporal Change This index measures the change of surface of a patch time along the years C ¼ ððpk2  pk1Þ=ðt2  t1ÞÞ=n

ð10:33Þ

where pk2 is the surface of category k in time 2 and pk1 the same category in time 1, t2 and t1 are respectively the date of the time lag.

10.2.6 The Semivariance Semivariograms are utilized to measure variance at many scales, comparing the values of a random variable at two points posed at a given lag distance. Semivariograms are mostly used in geostatistics (Isaaks & Srivastava, 1989) gðgÞ ¼ 1=2N ðgÞ

X

ðXj  Xj þ gÞ2

ð10:34Þ

where g(g) is the semivariance at lag g, N(g) is the number of pairwise comparisons at lag g and Xj is the random variate at position j. The plot of semivariance g against the g lag allows to see at which distance variance changes. The semivariance generally increases with increasing distance, although this is not true for all processes and is inversely related to the spatial autocorrelation of a variable.

416

10 Methods in Landscape Ecology

10.2.7 Boundaries Metrics The ecotones are important patterns in a landscape. These structures are inherent properties in a landscape but are also functioning as shaping factors in many processes. The detection of boundaries in a landscape is not a simple matter. In fact, the edges between two different habitats or land cover do not always have the function of true boundaries. On the other hand, especially in human-dominated landscape boundaries are so thin and the habitat constraint so high that is difficult to find a correlation between boundary structure and functions. It appears clearer of studying the behavior of animals that often a boundary is perceived in the neighborhood of a physical edge. Due to the importance recognized to boundaries in the dynamics and functioning of landscape, their measurement is a fundamental step to achieve a deep knowledge of the structure and functioning of the land mosaic (Young & Jarvis, 2001). Metzger and Muller (1996) have elaborated metrics to measure some relevant characters of a landscape assessed by remotesensing technology and presenting several index of boundary proportion, landcover boundary complexity, and landscape boundary complexity. (A) Indices of landcover and boundary proportion.

Proportion of Landcover Boundary qi ¼

Bi B

ð10:35Þ

where qi is the proportion of landcover i, Bi is the boundary area of landcover i, B is the landscape boundary area. Proportion of Boundary Area in Landcover i Fi ¼

Bi Ai

ð10:36Þ

where Bi is the boundary area of landcover i, and Ai is the area of the landscape. (B) Indices of landscape boundary complexity.

Proportion of Convergency (Covert) Points in Landscape C¼

Be A

ð10:37Þ

where Be is the area of coverts in landcover i and A is the area of landscape.

10.3

The Fractal Geometry Approach

417

Landscape Boundary Diversity Index HB ¼

NB X

qk log 2 qk

ð10:38Þ

k¼i

qk is the boundary area proportion in the landscape of each boundary type k, NB is the number of simple contacts (point where two landcovers converge).

10.2.8 Fragmentation Metrics In order to measure the fragmentation of a region, several indicators are available, like the number of undissected areas, the average area, and the density of the roads. We proposed the Bowen’s landscape dissection index LDI (Bowen & Burgess, 1981). Landscape Dissection Index n P

Pi i¼1 LDI ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n P 2 πAt Ai

ð10:39Þ

i¼1

where Pi ¼ perimeters of the n patches, Ai ¼ size of patches, At ¼ total size of the landscape.

10.3

The Fractal Geometry Approach

10.3.1 Introduction The heterogeneity is a common pattern of the environment and is visible, especially at the landscape scale. Organisms, populations, communities have a spatial distribution that re ects the heterogeneous nature of the land. The unequal distribution of natural phenomena as the geological nature of rocks, the rain distribution across a mountainous range, or the distribution of tree cover in a watershed, all create complicate mosaics at which organisms react. To measure this complexity, often the Euclidean geometry seems inadequate and new approaches are requested; in this the fractal geometry seems fit the issue (Mandelbrot, 1983; Feder, 2013; Milne, 1991a, b; Hastings & Sugihara, 1993). In manmade landscape, in which straight lines and regular geometric figures have been created transforming wild land in rural

418

10 Methods in Landscape Ecology

Fig. 10.7 Example of fractal objects (fern) with self-similarity

or urbanized area, the Euclidean geometry may be utilized to describe simple spatial patterns as perimeter–area ratio, patch area, and patch distance. When we move in a more natural landscape, such figures disappear and the irregularity of the patches reduces most of the descriptive capacities of the Euclidean geometry. Fractal geometry brings a new perspective to study and interpret the landscape complexity and dynamics across scales. The aim of this section is to introduce to the use of fractal geometry in the landscape research, producing a simplified view of a very complicated mathematical approach reporting examples from a large variety of scales from landscape to individuals. Fractal geometry is useful in landscape analysis because the hierarchical organization of the landscapes and their scaled patterns and processes need powerful tools to be investigated. Fractals can be observed not only in the patterns as forest patch shape and spatial arrangement, but also in behavioral processes like the distribution of animals in the space and their strategies to intercept resources. The word “fractal” has been coined in 1975 by Mandelbrot to describe an irregular object in which the irregularity is present at all scales, scale-invariant. Mandelbrot (1986) proposed this definition: “A fractal is by definition a set for which the Hausdorff Besicovitch dimension strictly exceeds the topological dimension.” A fractal is a shape made of parts similar to the whole in some way. Fractals can be considered objects or patterns that have non-integer dimensions. When a fractal object has qualities of the patterns at coarse scale that are repeated at finer and finer scale, this object has a self-similarity (Fig. 10.7).

10.3

The Fractal Geometry Approach

Fig. 10.8 Comparison between Euclidean dimension (0,1,2,3) (left) and fractal dimension (e.g., 0.4, 1.4, 1.8, 2.6) (right)

0

419

.

0.4

1

1.4

2

1.8

3

2.6

Two different types of fractals can be distinguished: regular and the random fractals. The first type is represented by scale invariant (self-similarity or self-affine) objects. Regular fractals have exact self-similarity: When an object is a rescaled copy of itself in all directions (isotropic). The object presents a self-affinity when the rescaling is anisotropic. Generally, most of the natural fractals (clouds, coast lines, organism abundance in the space, etc.) deviate from linear self-similarity and are called random fractals and display a statistical version of the self-similarity. Related to self-similarity is the concept of scale-dependence. For instance, the coast is a fractal object for which the total length depends on the scale of resolution at which the measure is done. The complexity is measured with the fractal dimension D that is the counterpart of the familiar Euclidean dimensions 0 (point), 1 (line and curves), 2 (surfaces), 3 (volumes), and it is never an integer. In a regular one-dimensional object, the mass increases in proportion to the length, say 2R (R ¼ radius) (Fig. 10.8). The mass in a two-dimensional disk with radius R increases in proportion to πR2, the area of a circle, in a three-dimensional object the mass increases of 4/3 πR3 that is the volume of a sphere. Adding dimensions, the mass increases according to the power of the number of dimensions. In fractal objects, the R is raised to some power D that is not an integer number. Fractal approach is intuitively easy to understand but is necessary to develop and apply this theory to the practice. For further information on fractal geometry, we recommend Mandelbrot (1983), Hastings and Sugihara (1993), Feder (2013), Frontier (1987), Gao et al. (2019). Fractal geometry finds a broad range of applications in different disciplines of the natural sciences as geology (Acuna & Yortsos, 1995; Loehle & Li, 1996), hydraulic (Donadio et al., 2015; Ichoku et al., 1996), soil composition (Perrier et al., 1995; Barak et al. (1996), dynamic (Perfect et al., 1996; Perfect & Kay, 1995; Rasiah, 1995) and microbial transport (Li et al., 1996), vegetation structure (Chen et al., 1994), ecoacoustics (Farina et al., 2021; Monacchi & Farina, 2019). Fractal geometry is particularly useful in the study of phenomena that have ambiguity according to the scalar properties. The coastline length is a classic example. The length of the coast depends on the measuring unit. In this case,

420

10 Methods in Landscape Ecology

increasing the size of measure unit from meter to kilometer, the total length of coast decreases. So, the length of a coast is scale dependent and relating this measure with the size of an organism as a sea otter or a crab, it is possible to adopt the right measure scale at which an organism copes the environmental characteristics. This consideration can be made for the human-related phenomena and, for instance, the number of suitable harbors along the coasts decreases as the size of ships increases. Many patterns and processes are scale dependent, and fractal model can describe their characteristics without the ambiguity of the Euclidean geometry. For this reason, fractals seems more and more important in landscape ecology and related sciences like the ecoacoustics (Farina et al., 2021). The scale properties of the objects measured using the fractal geometry require to clarify more on the scale attributes. Components of scale are lag, window, spatial, and/or temporal extension of observed quantities and the grain of resolution (Turner et al., 1991). Fractal models can be applied to measure landscape characters but also to measure perceived species-specific patterns (Johnson et al., 1992). Both approaches are extremely useful to understand the complexity of the environment and to predict species-specific replies to spatial configuration of resources. Examples of application to the riparian forest patches are shown by Rex and Malanson (1990). Leduc et al. (1994) combined fractal analysis to variogram techniques to estimate the fractal dimension of a fragmented landscape. van Hees (1994) measured the complexity of Alaska vegetation applying the fractal technique of the dividers method. Lathrop and Peterson (1992) used the fractal approach to identifying structural self-similarity in a mountainous landscape measuring the area– perimeter relationship. In the next points, we will discuss example of application of fractal geometry to landscape structures and to animals movements in the landscape.

10.3.2 The Fractal Dimension of the Edges Many processes and organisms are sensible to patch shape but the measure of the patch convolution is difficult to accomplish using the Euclidean geometry. The fractal approach to study edge complexity takes in account the scale at which an edge is measured and the length of the ruler we use to measure (Fig. 10.9). In other words, the length of objects like coastlines, rivers, mountain ridges depends on measurement scale L. Assuming C(L) the number of steps necessary to cover the total length C(L ) ¼ kLD, the total length will be TOTðLÞ ¼ C ðLÞxL then

TOTðLÞ ¼ kLD xL ¼ kL1D

ð10:40Þ

According to a simple power low where TOT(L) is the length of the object (e.g., coastline, rivers, etc.) measured at scale L, D (2 > D < 1) is the fractal dimension and

10.3

The Fractal Geometry Approach

Simple fractal dimension D= 1.006

Complex border line D= 1.139

421

Complex mosaic D= 1.482

Fig. 10.9 Example of different complexity of a patch border expressed by the fractal dimension D, note that the increase of edges is equivalent to the increase of fractal dimension. (From van Hees, 1994, with permission)

k is a constant. Increasing L the total length TOT(L) is reducing, and vice versa if L is small. Transforming into a logarithmic form Eq. (10.1) will become log TOTðLÞ ¼ log k þ ð1  DÞ log L

ð10:41Þ

Regressing TOT(L) and log L, 1-D will be the angular coefficient of the regression. For Euclidean object D ¼ 1, the length is independent of measurement scale. For a fractal object like Koch’s snow ake, the fractal dimension D ¼ 1.26. This power law has been applied to study the tortuosity of the pathway of insect movements (Wiens et al., 1993). In fact, fractal dimension is a scale-independent measure of the tortuosity of a pathway (Wiens et al., 1995). D is calculated regressing ln (natural logarithm) of path length C(L) and the logarithm of length scale L, which is subtracted 1 to yield D. K is the intercept of the regression line. When the pathway is a straight line D ¼ 1 and when the pathway is so complex to fill a plane D ¼ 2. In general, we can assure than more a pathway is tortuous (high value of D), more the organism interacts in fine-grained way with the heterogeneity of the landscape. C(L) may be measured by using the grid method (Sugihara & May, 1990) that consists in superimposing a regular grid of side length L to the pathway or the edge of interest. At every L size, grid, the squares containing a piece of the pathway or edge are counted. Then the natural logarithm of total number of squares is regressed with the L. D is equal to slope of the regression minus 1 (Fig. 10.10).

10.3.3 The Fractal Dimension of Patches The complexity of a polygon is expressed by the relationship P  √AD (e.g., logP1/2DLog A), where P is the perimeter and A the area. For simple polygons as circles and rectangular P  √A and D ¼ 1. For irregular and complex polygons, the perimeter tends to fill the plane and P  A with D — > 2.

422 Fig. 10.10 The irregularity of a border can be estimated by calculating the fractal dimension applying the caliber method. In this case, the total length of the border decrease with the increase of the caliber size

10 Methods in Landscape Ecology

A B C

L/1 Fig. 10.11 Two patches with identical surface (Aa ¼ Ab ¼ 10) but with a different perimeter (Pa ¼ 20, Pb ¼ 14) have respectively a fractal dimension of A ¼ 1.50, B ¼ 1.11. The dimension has been calculated according Bogaert, 2000

L/2

P= 20 A=10

A

L/4 P= 14 A=10

L=1

B

This relationship can be used to calculate the complexity of coastlines of various islands, or the complexity of vegetation patches using the same scale of measurement assuming a self-similarity between islands or vegetation patches of different size. In this case, the scale of the ruler should be enough small in order to avoid that with the decreasing island perimeter–area the measured shapes become Euclidean (D — > 1). The fractal dimension is obtained regressing log(P) on log(A), where D ¼ 2 x regression slope. Bogaert (2000) (Fig. 10.11) has found by approximation the fractal dimension:

10.3

The Fractal Geometry Approach

423

D¼2

ln P 4

ln A

ð10:42Þ

Using this approach, Krummel et al. (1987) have demonstrated as fractal dimension changes according to the size of forest patches. Moving to small woodlot, produced by human disturbance to large less disturbed forests, the fractal dimension shows an increase. This means that at a larger scale where the natural processes are dominant, the landscape is more convoluted. On the contrary, at a small scale, the patterns are more regular and simplified and most of these patterns have been produced by human disturbance regime. In terms of fractal analysis, this means that moving across scale the invariance is respected in two distinct subset. A first subset is dominated by human disturbance regime, a second subset is dominated by natural processes. This approach is very interesting and can be applied to a broad range of phenomena in which shape is important component of the ecological processes. The Box Grid Dimension Patch shape can be measured assuming that patches and the number of necessary boxes to cover the object are into a power law relationship. Assuming L to be the length of the box, the number of boxes necessary to cover the patch will be N(L), and the number of boxes are related with their dimension by the equation: N ðLÞ ¼ kLD

ð10:43Þ

The exponent is negative because the number of boxes decreases with the increase of the box length L. Transforming the equation in the logarithm form Log N(L) ¼ log k- D log L D is calculated regressing L with N(L). It is possible instead to use the length of the box to use the area of the box A(L). AðLÞ ¼ N ðLÞxL2 AðLÞ ¼ kLD xL AðLÞ ¼ kL2D

ð10:44Þ

The fractal dimension is calculated regressing the area of the box that cover the patch investigated with the length of the box, D ¼ 2-m, where m is the slope of the regression line (Fig. 10.12). The Cluster (Mass) Dimension This method consists of the research of a favorable cover around a focal cell. Here, a sliding window is passed through the matrix of variable size according to the process or the organismic function that we intend to test (Fig. 10.13).

424

10 Methods in Landscape Ecology

L

L/2 L/4

Fig. 10.12 Box grid dimension calculated utilizing two figures with different shape and area

Considering that the size of favorable habitat O(L) increases with the increase of the sampling window as a power law O(L) ¼ kLD, where D is the amount of habitat expected for a window of size L. D can be calculated as the angular coefficient of the regression: log OðLÞ ¼ log k þ D log L

ð10:45Þ

Lacunarity Deterministic fractals with identical dimensions can have a different appearance, as in the case of Cantor dusts. Mandelbrot called the distribution of gap size as “lacunarity” (Mandelbrot, 1983). Lacunarity measures the distribution of gaps in a fractal figure. An object with a low lacunarity is invariant when translating; on the

10.3

The Fractal Geometry Approach

425

L=3

L=5

Fig. 10.13 Cluster dimension obtained using sliding windows of different dimension

0

1

2

3

Fig. 10.14 Cantor dust, a fractal object obtained dividing a line or a surface in three equal parts and deleting the middle part

other hand, an object with a heterogeneous gap size is not translationally invariant. But we have to consider that the invariance is scale dependent. An object invariant at a small scale may be heterogeneous at a broad scale and vice versa. Translational invariance is not synonymous with self-similarity. Lacunarity has the following advantages when compared with other indices of landscape structure (Plotnick et al., 1993): 1. The algorithm is relatively simple. 2. The gliding box algorithm samples the map in sufficient ways to quantify change in contagion and self-similarity with scale. 3. The results are not sensitive to a boundary map. This analysis can be used for very sparse data. 4. The fragmentation that represents one of the major human-induced disturbance effects can change the heterogeneity of a landscape-producing effect of species distribution. Lacunarity seems a reasonable method to measure this heterogeneity (see also Dale, 2000).

426

10 Methods in Landscape Ecology

The lacunarity method is based on the Cantor dust (Fig. 10.14), a fractal object obtained applying a generator to a unit interval that divides in three parts of equal intervals. The middle part is deleted. Then the two parts are again divided, each in three parts. In five generations, the length of segments are so short that is not possible to distinguish with the sixth generation. The fractal dimension of the triadic Cantor set is a fractal set with a fractal dimension D ¼ ln 2/ln3. In practice, to calculate lacunarity, we utilize the “gliding box” algorithm according to Allain and Cloitre (1991); see also Plotnick et al. (1993). A r x r box is moved from upper left corner to the right down corner of a landscape map by a step of a cell for each column and the number of occupied sites according to classes of box mass (number of occupied sites). The lacunarity for a box size is calculated as LðrÞ ¼ Z ð2Þ=ðZ ð1ÞÞ2

ð10:46Þ

where Z(1) ¼ ∑ SQ(S, r) and Z(2) ¼ ∑ S2Q(S, r) are the first and second moment of the frequency distribution Q(S, r). Q(S, r) ¼ n(S, r)/N(r), n(S, r) represents the number of boxes in which a box mass category has been found. The number of boxes of size r containing S occupied sites is indicated N(r). N(r) ¼ (M  r + 1)2 where M ¼ size of the map. The lacunarity analysis may be made using different maps with the same r, or different gliding box sizes for the same map. Simulations conducted by Plotnick et al. (1993) on maps with r ranging from 1 to 128 have demonstrated that the highest value of lacunarity is observed when r ¼ 1 and the grain size of the maps equal to the box size. Lacunarity can be used also to analyze transect data.

10.3.4 Semivariance and Fractal Analysis Russell et al. (1992) studied the sea bird dispersion and the distribution of food applying the fractal analysis. To assess the relationship between prey and predators, the authors have utilized a method based on geostatistics and regionalized variables RV theory. This regionalized variable is too complicated to be expressed by a simple mathematical model because has deterministic character in the nearby samples, but is stochastic in the sense that value at a given point because cannot be calculated from neighboring samples. The semivariance gðγ Þ ¼ 1=2N ðγ Þ

X

ðXj  Xj þ γ Þ2

is in relation with γ (that is the sampling interval) by the relationship:

ð10:47Þ

10.3

The Fractal Geometry Approach

427

2gðγ Þ ¼ γ ð42DÞ

ð10:48Þ

where g(γ) is the semivariance at g interval, the fractal dimension D ¼ ð4  mÞ=2 ðγ⟶0Þ

ð10:49Þ

where m is the slope (4-2D) of ln g(γ)-lnγ (Burrough, 1981).

10.3.5 Examples of Application of Fractal to Animal Behavior Fractal analysis is particularly efficient to describe variation across a wide range of scales. Generally, the patterns are produced by a variety of processes operating at many spatial scales and level of organization (Fleurant et al., 2008). If we adopt the organism-centered view of the landscape, it is essential to know the perception resolution or grain and the range of scale, the extension at which an organism perceives the landscape to be heterogeneous. Grain and extension can change during the development of the organism (e.g., fish size classes) and can change according to the seasons (e.g., migration, breeding). This can create problems on employing too-simple or too-sophisticated models. Fractals help in understanding the way ecological processes operate, although per se the information cannot be correlated with a specific process. Fractal analysis has also been used for computing home range of animals. Loehle (1990) has utilized the box-counting to assess the pattern of occupation of space by an object. In the case presented by Loehle, a radio-collared hawk visited the “home range” in an irregular manner: Some areas were more frequented, but others were never visited. If we encircle the whole area as the maximum distance in which animal has been observed, we lose many details. On the contrary, considering at a different spatial scale the birds movements, we can measure the complexity in the area covered from the roosting place to the entire landscape. The movements of animals are easily detected and measurable for many species. These movements are strongly affected by the body mass of the organism and by the resolution which the organism perceives the surroundings (Fig. 10.15). Considering that a landscape is a hierarchical array of patches, it is important to distinguish at what resolution the organism perceives its surrounding. In such a way, the organism ignores the patterns outside the specific range of resolution. If we use the movement of the organism as an indicator of landscape interaction, we can assume that species which move slowly perceive the environment at a finer scale than the species moving faster. But when two species have different sizes, it seems impossible to compare the behavior and the resource-use strategies because they are scaled differently.

428

10 Methods in Landscape Ecology

Fig. 10.15 Significant relationship between the fractal dimension of predators (least auklet Aethia pusilla) and prey across six transect. (From Russell et al., 1992, with permission)

In fact, movement pathway is strongly in uenced by landscape structure and the size of the organism. The body size–scale-dependent movements are difficult to be compared. Applying a fractal analysis, it is possible to affords a scale-independent measure of the movement because fractal dimension of a movement pathway is scale-independent and may be used to compare different taxa (Wiens et al., 1995). A clear example of such an approach is presented by With (1994), studying the movement patterns of three acridid grasshoppers (Orthoptera) in a grassland mosaic. Manipulation of species in a controlled micro-landscape has been carried out by the author, confirming the predictive potentiality of such approach to broad-scale experimentally intractable landscape. The larger species Xanthippus corallipes moved at a faster rate than the two other species Psoloessa delicatula and Opeia obscura, and perceived the micro-landscape in a different way, presenting different values of fractal dimension D. Xanthippus corallipes has more linear movements than the two others perceiving a less heterogeneous landscape. The other two species have a similar D value and this probably means that use resources in a similar way (Fig. 10.16). The divider method can be employed to calculate the fractal dimension of animal movements. It consists of measuring the total length of the pathway (summation of distances between the points) at different “ruler” lengths. For instance, Wiens et al. (1993) have found in three tenebrionid beetles living in prairie in northeastern Colorado at similar D dimension of pathway convolution differing significantly from 1 (linear movement) and from random walk, but these three species, although different in size and speed, have similar D dimension demonstrating a similar strategy across scale. In this case, 25 ruler lengths were selected. The minimum ruler length was calculated as the average distance between the points and the

10.3

The Fractal Geometry Approach

429

Fig. 10.16 Regression showing the relationship between the pathway length (Y axis) and the measurement scale (X axis) in beetles, grasshoppers, and ants. Beetles: □ ¼ Eleodes extricata, ○ ¼ Eleodes obsoleta, r ¼ Eleodes hispilabris. Grasshoppers: □ ¼ Opeia obscura nymph., ○ ¼ Opeia obscura adult, r ¼ Psoloessa delicatula, þ ¼ Xanthippus corallipes; Ants: □ ¼ Pogonomyrmex occidentalis. (From Wiens et al., 1995, with permission)

maximum as one-third of the total path length, considering that at least three points are required for a linear regression. The fractal analysis has been successfully applied by Alados et al. (1996) to study in the Capra pyrenaica the level of stress by pregnancy and by Sarcoptes scabies infection. Owing the increase in metabolic rate due to the infection stress, the infected animals show a reduction in the complexity of exploratory behavior. It is well known that the metabolism of biological tissues is modified under the effects of pathologies. In the same manner, the behavior suffers a reduction in variability. Head-lift behavior, which consists in the interruption of feeding behavior and in lifting the head as antipredatory vigilance, was analyzed by regressing after a log– log linearization the frequency of head-lifts where F(Δt) is the frequency of head-lifts at the time intervals of duration Δt. F ðΔt Þ ¼ kðΔt ÞD ¼ k ð1=Δt ÞD

ð10:50Þ

where k is constant and D is the fractal dimension. Animal movements can be compared to a straight line or a true random walk. Between these two extremes, the fractal dimension helps to understand the role of inherent and landscape factors. Webb et al. (2009) have demonstrated a different fractal dimension of the tortuosity of females of white-tailed deer (Odocoileus virginianus) when compared with males and on behavioral and contest factors related.

430

10 Methods in Landscape Ecology

Animals searching for resources explore and navigate crossing mosaics of favorable and unfavorable patches, adapting the movement patterns to the complexity of the encounter asset. It is the case studied by Roshier et al. (2008) of grey teal (Anas gracilis). This species is living in farmland and in desert across Australia. Grey teal in farmland was observed to move either at high and low tortuosity, while in the desert moved using low level of tortuosity demonstrating that broad-scale movement patterns re ect underlying resource distributions. Moreover, movements in some animals are complex regardless of the spatial scale over which movements occur. Bascompte and Vilà (1997) have investigated path shape in radio-collared wolves applying the fractal index proposed by Katz and George (1985): D¼

log ðnÞ  log ðnÞ þ log Lδ

ð10:51Þ

Where n is the number of steps, L is the sum of the length of each segment and d is the planar diameter (the greatest distance between two points in the curve), observing in females a change in fractal dimension along the year according to their cycles. Males had search paths more convoluted but more constant along the year. Despite some emerging criticism (e.g., Turchin, 1996) and corrections (Nams, 2006) fractal analysis applied to animal movement in landscape represents an important frontier to better understand the relationship between animals and landscape configuration (e.g., area-restricted searching behavior (Tremblay et al., 2007); movement responses to patch structure (With et al., 1999)). Fractal approach results very useful in landscape design when based on the simulation of animal movements (Milne, 1991a, 1991b). Fractal analysis can be applied to investigate complex behavior. For instance, the activity and inactivity cycle of Drosophila melanogaster is a typical example of scale-dependent trait. In fact, the Drosophila has a self-similar structure that is repeated in the same way regardless of the time scale. The application of a fractal analysis to the movement has demonstrated that the amount of movement in Drosophila depends on the scale of measurement (Cole, 1995) and the observation of fractal time variability may help to understand complex behavior in landscapes.

10.4

The Geographic Information Systems

10.4.1 Introduction Geographic Information Systems (GIS) is a technology for handling spatial data. Developed in recent years, it is now applied in many fields from the local to the global scale. The GIS is a configuration of computer hardware and software to capture, store, and process spatial information, both quantitative and qualitative, to

10.4

The Geographic Information Systems

431

data viewing/exploration

maps

data editing/updating

charts plots

data representation

data conflation/integration

tables

GIS tasks data storage spatial temporal

data analysis data input/digitising

statistical

modeling & simulation Fig. 10.17 Different tasks that can be assigned to a Geographical Information Systems (GIS). (Redrawn from Steiniger & Hay, 2009)

Computer cartography

Database management

GIS

Remote sensing

Computer-aided design

Fig. 10.18 Computer cartography, data base management, remote sensing and computer-aided design are the composing elements of a GIS

create, update, combine, and interpret maps. GIS is a revolution in map structure, content, and use (Fig. 10.17). The spatial information represented by the localization in a geographic space of attributes of an event can be easily handled and processed in a GIS, thanks to the combination of spatial statistic, mathematical procedures, and computer hardware. The combination of these components create a Geographic Information System or GIS (Burrough, 1981) in which computer cartography, data base management, remote sensing procedures, and computer-aided design represent the structuring components (Fig. 10.18). GIS are used by a growing number of people in different fields from geography to economy to social science and planning. There are GIS for many purposes; some are devoted to cartography and digital terrain models, to handle cartography and data

432

10 Methods in Landscape Ecology

base, to process remote sensing information, and others to analyze landscape spatial patterns. The GIS procedure may find a great number of applications in many fields from geography, economy, social science, cartography, urban planning, ecoacoustics, etc. Some are extremely friendly to be used; others need a dedicated operator. For instance, there are some free and open source desktop GIS: GRASS GIS (1982 ; Quantum GIS (2002) ; ILWIS Open (1984/85) ; uDIG (2004/2005) ; SAGA (2001/2); OpenJUMP 82002/3); MapWindow(1998), gvSIG(2003) . The incredible development and variability of these systems create difficulties to describe the several applications available, rooted in geography, computing, and application areas. In landscape ecology, GIS is a fundamental tool especially if it is used as platform to manipulate models and real data, transferring information from implicit to explicit analysis (Steiniger & Hay, 2009). The GIS appears indispensable for most of the landscape investigations like the following: • • • • •

Land-use change. Vegetation patterning. Animal distribution across the landscape. Linking remote sensing with topography. Modeling processes across the landscape.

Three types of information on landscape features considered by a GIS are as follows: Name and characteristic of the features, their locations, and the spatial relationship to one another.

10.4.2 The Representation of the Spatial Information Two systems are available to represent the spatial information as lake–forest– field maps: raster and vectorial. The raster format is the representation of a feature by using discrete units “pixel” or cells. The vectorial format utilizes the position of point, line, and area and their connectivity.

10.4.3 Map Layer Often indicated simply as a layer, it is a conventional map reporting a variety of information not necessarily geographical. In a map layer, information is represented by occupied cells described according to different attributes of the subject.

10.4

The Geographic Information Systems

433

A layer contains other explanatory information as title, resolution, orientation, and zone(s). Titles such as “vegetation,” “bird richness,” etc. are important when layers are manipulated and each layer enters in an algebraic formula. Resolution of a layer represents the relationship between ground and “on paper” distance. Orientation of a layer indicates the relationship between geographic and cartographic direction. Zones are a part of the map distinct for some attributes from other zones; for example, a forest patch, a field, urban area, etc. Each zone is indicated by a label that is a name, and the values that pose as a further specification of the zone. For example, a zone labelled “forest” with a value of 200: “forest 200 m from road.” Value can be expressed as ratio, interval, ordinal, and nominal. Nominal is the representation of a quality of zone, such as “dense shrubland.” Location is the elementary unit of a map. In raster format it is represented by a square cell or grid cell, and by pixels or picture elements in image processing. Coordinates are a pair of number expressed in geographic units; for example, the distance in meters from the Equator and Greenwich, or simply by a “on paper” numerical scale.

10.4.4 Procedures for Cartographic Handling and Modeling Most of the GIS available today has in common the capacity to manipulate the information at a single location and at the scale of the entire map. Data is the recorded facts that in landscape ecology may be vegetation cover, land use, etc. Every data in a GIS occupies a precise position located according to true geographical (e.g., UTM) or working coordinates (x, y).

10.4.5 Capturing Data Data can be captured in many ways: directly digitizing points or lines, or rasterizing images (existing maps such as topographic maps, land cover maps, etc.).The digitalization is a very precise but an expensive procedure. For example, the location with coordinates (xij, yij) in which i is the attribute (land cover type) and j is the layer, can be manipulated algebraically adding the same location in other layers: xij, yij + xim, yim (where m is another layer.) Other procedures can manipulate entire layers this map + that map ¼ new map.

434

10 Methods in Landscape Ecology

10.4.6 Some Cartographic Modeling Procedures According to Tomlin (1990), at least three operations are available in GIS procedures: Local operations: For each location it is possible to associate a new value that represents the transformation of the same value applying mathematical functions to each location’s value(s) on one or more existing map layers. Zonal operations: For each location computes a new value as a function of the existing value from a specified layer. Focal operations: For each location computes a new value according to the character of the neighboring locations of the same map layer or on other map layers.

10.4.7 Commands in GIS Many routines are available to transform data. For local operations, arithmetic menu is available: add: adds values of two or more existing maps, average: averages values of two or more existing maps, cover: covers values of one existing map with one or more existing maps, divide: divides values of one existing map by one or more existing maps, maximize: maximizes values of two or more existing maps on new maps, minimize: minimizes values of two or more existing maps on a new map, multiply: multiplies values of two or more existing maps, subtract: subtracts values of one existing map from one or more existing maps. For focal operations, a neighborhood menu is available with functions such as follows: clump: generates a map of contiguous like-valued cells, differentiate: generates slope map from surface data, interpolate: calculates intermediate values from two positions, orient: generates aspect map from surface data, radiate: generates viewshed map from specified viewer locations, scan: classifies neighborhood of specified locations as to neighboring values, score: compares and summarizes values of two maps on a point-by-point basis, smooth: generates map of surface from map of contours, spread: generates map of proximity to specified locations.

References

435

10.4.8 GIS and Remote Sensing Remote-sensing information produced by satellite or aerial photos has to be interpreted before it is to be used in a GIS. Generally, data from remote sensing are imported in a GIS after classification and georeferentiation. The procedures of land classification are independent to GIS technique, but when the data have been introduced in a GIS, it is necessary to know at least the spatial scale (the resolution) of the images for a georeferentiation.

10.4.9 Scaling in GIS In landscape ecology it is often useful to process spatial data at different scales; in fact, in a landscape, patterns and processes are visible and functioning along a broad range of spatial scales (Turner et al., 1989). Actually, a limited number of routines are available to carry out these procedures. Baker and Cai (1992) has presented a program operating in GRASS able to calculate more than 60 routines on landscape structure (e.g., distance, size, shape, fractal dimension, perimeters, diversity, texture, juxtaposition, edges), with different possibilities of sampling area of several size, changing 15 scales of analysis or using a moving window.

References Acuna, J. A., & Yortsos, Y. C. (1995). Application of fractal geometry to the study of networks of fractures and their pressure transient. Water Resources Research, 31, 527–540. Alados, C. L., Escos, J. M., & Emlen, J. M. (1996). Fractal structure of sequential behaviour patterns: An indicator of stress. Animal Behaviour, 51, 437–443. Allain, C., & Cloitre, M. (1991). Characterizing the lacunarity of random and deterministic fractal set. Physical Review A, 44, 3552–3558. Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138–146. Baker, W. L., & Cai, Y. (1992). The r.le programs for multiscale structure using the GRASS geographical information system. Landscape Ecology, 7, 291–302. Baldwin, D. J. B., Weaver, K., Schnekenburger, F., & Perera, A. H. (2004). Sensitivity of landscape pattern indices to input data characteristics on real landscapes: Implications for their use in natural disturbance. Landscape Ecology, 19, 255–271. Barak, P., Seybold, C. A., & McSweeney, K. (1996). Self-similitude and fractal dimension of sand grain. Soil Science Society of America Journal, 60, 72–76. Bartel, A. (2000). Analysis of landscape pattern: Towards a “top down” indicator for evaluation of land use. Ecological Modelling, 130, 87–94. Bascompte, J., & Vilà, C. (1997). Fractals and search paths in mammals. Landscape Ecology, 12(4), 213–221. Bogaert, J. (2000). Quantifying habitat fragmentation as a spatial process in a patch-corridor matrix landscape model. PhD Dissertation, Faculteit Wetenschappen, Department Biologie, University of Antwerpen.

436

10 Methods in Landscape Ecology

Bowen, G. W., & Burgess, R. L. (1981). A quantitative analysis of forest island pattern in selected Ohio landscapes. ORNL/TM-7759. Buechner, M. (1989). Are small-scale landscape features important factors for field studies of small mammal dispersal sinks? Landscape Ecology, 2, 191–199. Burrough, P. A. (1981). Principles of geographical information systems for land resources assessment. Oxford University Press. Chen, S. G., Ceulman, R., & Impen, I. (1994). A fractal-based Populus canopy structure model for the calculation of light interception. Forest Ecology and Management, 69, 97–110. Cole, B. J. (1995). Fractal time in animal behaviour: The movement activity of Drosophila. Animal Behaviour, 50(5), 1317–1324. Cushman, S. A. (2018). Calculation of configurational entropy in complex landscapes. Entropy, 20(4), 298. Dale, M. R. T. (2000). Lacunarity analysis of spatial pattern: A comparison. Landscape Ecology, 15, 467–478. Dorner, B., Lertzman, K., & Fall, J. (2002). Landscape pattern in topographically complex landscapes: Issues and techniques for analysis. Landscape Ecology, 17(8), 729–743. Donadio, C., Magdaleno, F., Mazzarella, A., & Kondolf, G. M. (2015). Fractal dimension of the hydrographic pattern of three large rivers in the Mediterranean morphoclimatic system: Geomorphologic interpretation of Russian (USA), Ebro (Spain) and Volturno (Italy) uvial geometry. Pure and Applied Geophysics, 172(7), 1975–1984. Elkie, P. C., Rempel, R. S., & Carr, A. P. (1999). Patch analyst User’s manual: A tool for quantifying landscape structure. Ontario Ministry of Natural Resources (Technical manual TM-002TM-002). Northwest Science & Technology. Ernoult, A., Bureau, F., & Poudevigne, I. (2003). Patterns of organization in changing landscape: Implications for the management of biodiversity. Landscape Ecology, 18, 239–251. Farina, A., Righini, R., Fuller, S., Li, P., & Pavan, G. (2021). Acoustic complexity indices reveal the acoustic communities of the old-growth Mediterranean forest of Sasso Fratino integral natural reserve (Central Italy). Ecological Indicators, 120, 106927. Farrell, J., & Barth, M. (1999). The global positioning system and inertial navigation. McGrawHill. Feder, J. (2013). Fractals. Springer Science & Business Media. Fleurant, C., Burley, J. B., Loures, L., Lehmann, W., & Mchugh, J. (2008). Inverse box-counting method: A fractal-based procedure to create biospheric landscape patterns. In Proceedings of the 1st WSEAS international conference on landscape architecture (LA’08): New aspects of landscape architecture (pp. 86–91). World Scientific and Engineering Academy and Society. Frontier, S. A. (1987). Fractals in marine ecology. In L. Legendre (Ed.), Development in numerical ecology (pp. 335–378). Springer-Verlag. Gao, P., Cushman, S. A., Liu, G., Ye, S., Shen, S., & Cheng, C. (2019). FracL: A tool for characterizing the fractality of landscape gradients from a new perspective. ISPRS International Journal of Geo-Information, 8(10), 466. https://doi.org/10.3390/ijgi810046 Gergel, S. E., & Turner, M. G. (2002). Learning landscape ecology. A practical guide to concepts and techniques. Springer. Groom, G., Mücher, C. A., Ihse, M., & Wrbka, T. (2006). Remote sensing in landscape ecology: Experiences and perspectives in a European context. Landscape Ecology, 21(3), 391–408. Gustafson, E. J., & Parker, G. R. (1992). Relationships between landcover proportion and indices of landscape spatial pattern. Landscape Ecology, 7, 101–110. Habel, J. C., Teucher, M., Ulrich, W., Bauer, M., & Rödder, D. (2016). Drones for butter y conservation: Larval habitat assessment with an unmanned aerial vehicle. Landscape Ecology, 31(10), 2385–2395. Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. In IEEE transactions on systems, man, and cybernetics SMC-3 (pp. 610–621). Hargis, C. D., Bissonette, J. A., & David, J. L. (1997). Understanding measures of landscape pattern. In J. A. Bissonette (Ed.), Wildlife and landscape ecology (pp. 231–261). Springer.

References

437

Hastings, H. M., & Sugihara, G. (1993). Fractals. A user’s guide for the natural sciences. Oxford University Press. Heip, C. (1974). A new index measuring evenness. Journal of Marine Biological Association, 54, 555–557. Hill, M. O. (1973). Diversity and evenness: A unifying notation and its consequences. Ecology, 54, 427–432. Hulshoff, R. M. (1995). Landscape indices describing a Dutch landscape. Landscape Ecology, 10, 101–111. Ichoku, C., Karnieli, A., & Verchovsky, I. (1996). Application of fractal techniques to the comparative evaluation of two methods of extracting channel networks from digital models. Water Resources Research, 32, 389–399. Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics. Oxford University Press. Jenerette, G. D., & Wu, J. (2001). Analysis and simulation of land-use change in the Central Arizona-Phoenix region, USA. Landscape Ecology, 16, 611–626. Johnson, A. R., Wiens, J. A., Milne, B. T., & Crist, T. O. (1992). Animal movements and population dynamics in heterogeneous landscapes. Landscape Ecology, 7, 63–75. Katz, M. J., & George, E. B. (1985). Fractals and the analysis of growth paths. Bulletin of Mathematical Biology, 47(2), 273–286. Krummel, J. R., Gardner, R. H., Sugihara, G., O’Neill, R. V., & Coleman, P. R. (1987). Landscape patterns in a disturbed environment. Oikos, 48, 321–324. Lathrop, R., & Peterson, D. L. (1992). Identifying structural self-similarity in mountainous landscapes. Landscape Ecology, 6, 233–238. Lausch, A., & Herzog, F. (2002). Applicability of landscape metrics for the monitoring of landscape change: Issues of scale, resolution and interpretability. Ecological Indicators, 2, 3–15. Leduc, A., Prairies, Y. T., & Bergeron, Y. (1994). Fractal dimension estimates of a fragmented landscape: Sources of variability. Landscape Ecology, 9, 279–286. Li, H., & Reynolds, J. F. (1995). On definition and quantification of heterogeneity. Oikos, 72, 280–284. Li, H., & Wu, J. (2004). Use and misuse of landscape indices. Landscape Ecology, 19, 389–399. Li, B. L., Loehle, C., & Malon, D. (1996). Microbial transport through heterogeneous porous media: Random walk, fractal, and percolation approaches. Ecological Modelling, 85, 285–302. Loehle, C. (1990). Home range: A fractal approach. Landscape Ecology, 5, 39–52. Loehle, C., & Li, B. L. (1996). Statistical properties of ecological and geologic fractals. Ecological Modelling, 85, 271–284. Mandelbrot, B. (1983). The fractal geometry of nature. Freeman. Mandelbrot, B. (1986). Self-affine fractal sets. In L. Pietronero & E. Tosatti (Eds.), Fractals in physics (pp. 3–28). North-Holland. McAlpine, C. A., & Eyre, T. J. (2002). Testing landscape metrics as indicators of habitat loss and fragmentation in continuous eucalypt forests (Queensland, Australia). Landscape Ecology, 17, 711–728. McGarigal, K., & Marks, J. B. (1995). Fragstats: Spatial analysis program for quantifying landscape structure (General technical reports PNW-GTR-351). US Department of Agriculture, Forest Service, Pacific Northwest Research Station. McIntyre, N. E., & Wiens, J. A. (2000). A novel use of the lacunarity index to discern landscape function. Landscape Ecology, 15, 313–321. Metzger, J. P., & Muller, E. (1996). Characterizing the complexity of landscape boundaries by remote sensing. Landscape Ecology, 11, 65–77. Milne, B. T. (1991a). Lessons from applying fractal models to landscape patterns. In M. G. Turner & R. H. Gardner (Eds.), Quantitative methods in landscape ecology (pp. 199–235). SpringerVerlag. Milne, B. T. (1991b). The utility of fractal geometry in landscape design. Landscape and Urban Planning, 21(1–2), 81–90.

438

10 Methods in Landscape Ecology

Mladenoff, D. J., & DeZonia, B. (2000). APACK 2.14 analysis software: User’s guide (draft version: 2-18-00). Forest Landscape Ecology Laboratory. Department of Forest Ecology and Management. University of Wisconsin-Madison. Monacchi, D., & Farina, A. (2019). A multiscale approach to investigate the biosemiotic complexity of two acoustic communities in primary forests with high ecosystem integrity recorded with 3D sound technologies. Biosemiotics, 12(2), 329–347. Musick, H. B., & Grover, H. D. (1991). Image textural measures as indices of landscape pattern. In M. G. Turner & R. H. Gardner (Eds.), Quantitative methods in landscape ecology (pp. 77–103). Springer-Verlag. Nams, V. O. (2006). Improving accuracy and precision in estimating fractal dimension of animal movement paths. Acta Biotheoretica, 54(1), 1–11. Newman, E. A., Kennedy, M. C., Falk, D. A., & McKenzie, D. (2019). Scaling and complexity in landscape ecology. Frontiers in Ecology and Evolution, 7, 293. O’Neill, R. V., Krummel, J. R., Gardner, R. H., Sugihara, G., Jackson, B., De Angelis, D. L., Milne, B. T., Turner, M. G., Zygmunt, B., Christensen, S. W., Dale, V. H., & Graham, R. L. (1988). Indices of landscape pattern. Landscape Ecology, 1, 153–162. Perfect, E., & Kay, B. D. (1995). Applications of fractals in soil and tillage research: A review. Soil & Tillage Research, 36, 1–20. Perfect, E., McLaughlin, N. B., Kay, B. D., & Topp, G. C. (1996). An improved fractal equation for the soil water retention curve. Water Resources Research, 32, 281–287. Perrera, A. H., Baldwin, D. J. B., & Schnekenburger, F. (1997). LEAP II: A landscape ecological analysis package for land use planners and managers (Forest research report n.146). Ontario Forest Research Institute, Ontario Ministry of Natural Resources. Perrier, E., Mullon, C., & Rieu, M. (1995). Computer construction of fractal structures: Simulation of their hydraulic and shrinkage properties. Water Resources Research, 31, 2927–2943. Pielou, E. C. (1975). Ecological diversity. Wiley. Pielou, E. C. (1977). Mathematical ecology. Wiley. Plotnick, R. E., Gardner, R. H., & O’Neil, R. V. (1993). Lacunarity indices as measures of landscape texture. Landscape Ecology, 8, 201–211. Rasiah, V. (1995). Fractal dimension of surface-connected macropore count-size distributions. Soil Science, 159, 105–108. Rex, K. D., & Malanson, G. P. (1990). The fractal shape of riparian forest patches. Landscape Ecology, 4, 249–258. Richardson, J. J., Moskal, L. M., & Bakker, J. D. (2014). Terrestrial laser scanning for vegetation sampling. Sensors, 14(11), 20304–20319. Riitters, K. H., O’Neill, R. V., Hunsaker, C. T., Wickham, J. D., Yankee, D. H., Timmins, S. P., Jones, K. B., & Jackson, B. L. (1995). A factor analysis of landscape pattern and structure metrics. Landscape Ecology, 10, 23–39. Riitters, K. H., O’Neill, R. V., Wickham, J. D., & Jones, K. B. (1996). A note on contagion indices for landscape analysis. Landscape Ecology, 11(4), 197–202. Romme, V. H. (1982). Fire and landscape diversity in subalpine forests of Yellowstone National Park. Ecological Monographs, 52, 199–221. Roshier, D. A., Doerr, V. A., & Doerr, E. D. (2008). Animal movement in dynamic landscapes: Interaction between behavioural strategies and resource distributions. Oecologia, 156(2), 465–477. Russell, R. W., Hunt, G. L., Jr., Coyle, K. O., & Cooney, R. T. (1992). Foraging in a fractal environment: Spatial patterns in marine predator-prey system. Landscape Ecology, 7, 195–209. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press. Sheldon, A. L. (1969). Equitability indices: Dependence on the species count. Ecology, 50, 466–467. Simpson, E. H. (1949). Measurement of diversity. Nature, 163, 688.

References

439

Steiniger, S., & Hay, G. J. (2009). Free and open source geographic information tools for landscape ecology. Ecological Informatics, 4(4), 183–195. Sugihara, G., & May, R. M. (1990). Applications of fractals in ecology. TREE, 5, 79–86. Tal, D., & Altschuld, J. (2021). Drone Technology in Architecture, engineering and construction: A strategic guide to unmanned aerial vehicle operation and implementation. Wiley. Tischendorf, L. (2001). Can landscape indices predict ecological processes consistently? Landscape Ecology, 16, 235–254. Tomlin, C. D. (1990). Geographic information systems and cartographic modeling. Prentice-Hall, 249 p. Cahiers de géographie du Québec, 35(96), 600–601. Trani, M. K., & Giles, R. H., Jr. (1999). An analysis of deforestation: Metrics used to describe pattern change. Forest Ecology and Management, 114, 459–470. Tremblay, Y., Roberts, A. J., & Costa, D. P. (2007). Fractal landscape method: An alternative approach to measuring area-restricted searching behavior. Journal of Experimental Biology, 210(6), 935–945. Trimble Navigation. (1994). GPS mapping systems, general reference. Trimble Navigation, Ltd.. Turchin, P. (1996). Fractal analyses of animal movement: A critique. Ecology, 77(7), 2086–2090. Turner, M. G., & Gardner, R. H. (Eds.). (1991). Quantitative methods in landscape ecology. Springer. Turner, M. G., O’Neill, R. V., Gardner, R. H., & Milne, B. (1989). Effect of changing spatial scale on the analysis of landscape pattern. Landscape Ecology, 3, 153–162. Turner, S. J., O’Neill, R. V., Conley, W., Conley, M. R., & Humpries, H. C. (1991). Pattern and scale: Statistics for landscape ecology. In M. G. Turner & R. H. Gardner (Eds.), Quantitative methods in landscape ecology (pp. 17–49). Springer-Verlag. van Dorp, D., & Opdam, P. F. M. (1987). Effects of patch size, isolation and regional abundance of forest bird communities. Landscape Ecology, 1, 59–73. van Hees, W. W. S. (1994). A fractal model of vegetation complexity in Alaska. Landscape Ecology, 9, 271–278. Webb, S. L., Riffell, S. K., Gee, K. L., & Demarais, S. (2009). Using fractal analyses to characterize movement paths of white-tailed deer and response to spatial scale. Journal of Mammalogy, 90(5), 1210–1217. Wiens, J., Crist, T. O., & Milne, B. T. (1993). On quantifying insect movements. Environmental Entomology, 22, 709715. Wiens, J. A., Crist, T. O., With, K. A., & Milne, B. T. (1995). Fractal patterns of insect movement in microlandscape mosaics. Ecology, 76, 663–666. With, K. A. (1994). Using fractal analysis to assess how species perceive landscape structure. Landscape Ecology, 9, 25–36. With, K. A., Cadaret, S. J., & Davis, C. (1999). Movement responses to patch structure in experimental fractal landscapes. Ecology, 80(4), 1340–1353. Young, C. H., & Jarvis, P. J. (2001). Measuring urban habitat fragmentation: An example from black country, UK. Landscape Ecology, 16, 64658.

Suggested Readings

Chapter 1 Allen, T. H. F., & Hoekstra, T. W. (1982). Toward a unified ecology. Columbia University Press. Allen, T. H. F., & Starr, T. B. (1982). Hierarchy, perspectives for ecological complexity. The University of Chicago Press. Appleton, J. (1996). The experience of landscape (Rev ed.). Wiley. Bourassa, S. C. (1991). The aesthetics of landscape. Belhaven Press. Brown, J. H. (1995). Macroecology. The University of Chicago Press. Farina, A. (1998). Principles and methods in landscape ecology. Chapman & Hall. Farina, A. (2000). Landscape ecology in action. Kluwer Academic Publishers. Farina, A. (2014). Soundscape ecology. Springer. Forman, R. T. T. (1995). Land mosaics. The ecology of landscapes and regions. Cambridge Academic Press. Forman, R. T. T., & Godron, M. (1986). Landscape ecology. Wiley. Gibson, J. J. (1986). The ecological approach to visual perception. Erlbaum Associates, Inc. Harris, L. D. (1984). The fragmented forest, island biogeography theory and the preservation of biotic diversity. University of Chicago Press. Hernandez-Stefanoni, J. L. (2005). Relationships between landscape patterns and species richness of trees, shrubs and vines in a tropical forest. Plant Ecology, 179(1), 53–65. Ingold, T. (2000). The perception of the environment. Routledge, Taylor and Francis Group. Kaplan, R., & Kaplan, S. (1989). The experience of nature. A psychological perspective. Cambridge University Press. MacArthur, R. H., & Wilson, E. O. (1967). The theory of island biogeography. Princeton University Press. MacArthur, R. H. (1972). Geographical ecology, patterns in the distribution of species. Princeton University Press. Naveh, Z., & Lieberman, A. S. (1994). Landscape ecology. Theory and application (2nd ed.). Springer. Vink, A. P. A. (1983). Landscape ecology and land use. Longman. With, K. A. (2019). Essentials of landscape ecology. Oxford University Press. Zonneveld, I. S. (1995). Landscape ecology. SPB Academic Publishing. Zonneveld, I. S., & Forman, R. T. (Eds.). (1990). Changing landscape: An ecological perspective. Springer.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Farina, Principles and Methods in Landscape Ecology, Landscape Series 31, https://doi.org/10.1007/978-3-030-96611-9

441

442

Suggested Readings

Chapter 2 Adam, B. (1995). Timewatch: The social analysis of time. Polity Press. Appleton, J. (1975). The experience of landscape. Wiley. Barbieri, M. (2003). The organic code. An introduction to semantic biology. Cambridge University Press. Bateson, G. (1972). Steps to an ecology of mind. Ballantine Books. Bourassa, S. C. (1991). The aesthetics of landscape. Belhaven. Carson, R. (2002). Silent spring. Houghton Mifflin Harcourt. de Saussure, F. (1916). Course in general linguistic. Peter Owen. Farina, A., & Gage, S. H. (Eds.). (2017). Ecoacoustics: The ecological role of sounds. Wiley. Hoffmeyer, J. (1996). Signs of meaning in the universe. Indiana University Press. Kaplan, R., & Kaplan, S. (1989). The experience of nature. A psychological perspective. Cambridge University Press. Krause, B. L. (2012). The Great Animal Orchestra: Finding the origins of music in the world’s wild places. Profile Books Limited. Laszlo, E. (1996). The whispering pond. Element Book. Odum, E. P. (1971). Fundamentals of ecology (3rd ed.). Saunders. Truax, B. (1999). Handbook for acoustic ecology CD-ROM edition. Cambridge Street Publishing.

Chapter 3 Adams, F. (2003). The information turn in philosophy. Minds and Machines, 13, 471–501. Allen, T. F. H., & Hoekstra, T. W. (1992). Toward a unified ecology. Columbia University Press. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence. From natural to artificial systems. Oxford University Press. Bossomaier, T. R. J., & Green, D. G. (Eds.). (2000). Complex systems. Cambridge University Press. Cilliers, P. (1998). Complexity & Postmodernism. Routledge. Cushing, J. M., Costantino, R. F., Dennis, B., Desharnais, R. A., & Henson, S. M. (2003). Chaos in ecology. Experimental nonlinear dynamics. Academic. Ducan, R. (Ed.). (1998). Cognitive ecology. The evolutionary ecology of information processing and decision making. The University of Chicago Press. Gilpin, M., & Hanski, I. (Eds.). (1991). Metapopulation dynamics: Empirical and theoretical investigations. Academic. Hanski, I. (1999). Metapopulation ecology. Oxford University Press. Johnson, H. A. (1970). Information theory in biology after 18 years. Science, 168, 1545–1550. Kauffman, S. (1993). The origin of order. Oxford University Press. Laland, K. N., & Brown, G. R. (2006). Niche construction, human behavior, and the adaptive-lag hypothesis. Evolutionary Anthropology, 15, 95–104. Levin, S. (1999). Fragile dominion. Complexity and the commons. Helix Books/Perseus Books. MacArthur, R. H., & Wilson, E. O. (1967). The theory of island biogeography. Princeton University Press. Manson, S. M. (2001). Simplifying complexity: A review of complexity theory. Geoforum, 32, 405–414. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition. The realization of the living. D. Reidel Publishing Company. Maurer, B. A. (1999). Untangling ecological complexity. The macroscope perspective. The University of Chicago Press. Merry, U. (1995). Coping with uncertainty. Insights from the new sciences of chaos, selforganization, and complexity. Praeger Publishers.

Suggested Readings

443

Morowitz, H. J. (1968). Energy flow in biology. Biological organization as a problem in thermal physics. Academic. Morowitz, H. J. (1970). Entropy for biologists. An introduction to thermodynamics. Academic Press. Morowitz, H. J. (2002). The emergence of everything. How the world became complex. Oxford University Press. Odling-Smee, J., Douglas, H. E., Palkovacs, E. P., Feldman, M. W., & Laland, K. N. (2003). Niche construction theory: A pratical guide for ecologists. The Quarterly Review of Biology, 88(1), 4–28. O’Neill, R. V., DeAngelis, D. L., Waide, J. B., & Allen, T. F. H. (1986). A hierarchical concept of ecosystems. Princeton University Press. Pierce, J. R. (1980). An introduction to information theory. Symbolism signals and noise. Dover Publications. Pulliam, R. (1988). Sources-sinks, and population regulation. American Naturalist, 132, 652–661. Sirot, E., Renaud, P.-C., & Pays, O. (2016). How competition and predation shape patterns of waterhole use by herbivores in arid ecosystems. Animal Behaviour, 118, 19–26. Stauffer, D. (1985). Introduction of percolation theory. Taylor & Francis. Stephen, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton University Press. Taylor, M. C. (2001). The moment of complexity. Emerging network culture. The University of Chicago Press. Ulanowicz, R. E. (1997). Ecology, the ascendent perspective. Columbia University Press. von Bertalanffy, L. (1969). General system theory. George Braziller. Wiener, N. (1950). The human use of human beings. Houghton Mifflin Company.

Chapter 4 Allen, T. F. H., & Starr, T. B. (1982). Hierarchy. Perspectives for ecological complexity. University of Chicago Press. Boyce, M. S., Mallory, C. D., Morehouse, A. T., Prokopenko, C. M., Scrafford, M. A., & Warbington, C. H. (2017). Defining landscapes and scales to model landscape–organism interactions. Current Landscape Ecology Reports, 2(4), 89–95. Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology, 7, 1943–1967. Peterson, D., & Parker, V. T. (Eds.). (1998). Ecological scale. Theory and applications. Columbia University Press. Quattrochi, D. A., Wentz, E., Lam, N. S. N., & Emerson, C. W. (Eds.). (2017). Integrating scale in remote sensing and GIS. CRC Press.

Chapter 5 Baartman, J. E., Temme, A. J., & Saco, P. M. (2018). The effect of landform variation on vegetation patterning and related sediment dynamics. Earth Surface Processes and Landforms, 43(10), 2121–2135. Butler, D. R. (1995). Zoogeomorphology. Animals as geomorphic agents. Cambridge University Press. Fischer, J., & Lindenmayer, D. B. (2007). Landscape modification and habitat fragmentation: A synthesis. Global Ecology and Biogeography, 16(3), 265–280.

444

Suggested Readings

George, T. L., & Dobkin, D. S. (Eds.). (2002). Effects of habitat fragmentation on birds in western landscapes: Contrast with paradigms from the Eastern United States (Studies in Avian Biology, No. 25). Cooper Ornithological Society. Printed by Allen Press. Goldammer, J. G., & Jenkins, M. J. (Eds.). (1990). Fire in ecosystem dynamics. Mediterranean and Northern perspectives. SPB Academic Publishing BV. Harris, L. D. (1984). The fragmented forest. Island biogeography theory and the preservation of biotic diversity. The University of Chicago Press. Hilty, J. A., Lidicker, W. Z., Jr., & Merenlender, A. M. (2012). Corridor ecology: The science and practice of linking landscapes for biodiversity conservation. Island Press. Huggett, R. J. (1985). Geoecology. An evolutionary approach. Routledge. Kozlowski, T. T., & Ahlgren, C. E. (Eds.). (1974). Fire and ecosystems. Academic. MacArthur, R. H., & Wilson, E. O. (1967). The theory of island biogeography. Princeton University Press. Miller, D. E. (1981). Energy at the surface of the earth. An introduction to the energetics of ecosystems. Academic. Muscolo, A., Bagnato, S., Sidari, M., & Mercurio, R. (2014). A review of the roles of forest canopy gaps. Journal of Forestry Research, 25(4), 725–736. O’Neill, P. (1985). Environmental chemistry. Chapman & Hall. Pickett, T. A., & White, P. (Eds.). (1985). The ecology of natural disturbance and patch dynamics. Academic. Taylor, P. D., Fahrig, L., Henein, K., & Merriam, G. (1993). Connectivity is a vital element of landscape structure. Oikos, 68, 571–573. Trabaud, L. (Ed.). (1987). The role of fire in ecological systems. SPB Academic Publishing.

Chapter 6 Butler, D. R. (1995). Zoogeomorphology. Animals as geomorphic agents. Cambridge University Press. Hansen, A. J., & di Castri, F. (Eds.). (1992). Landscape boundaries. Consequences for biotic diversity and ecological flows. Springer. Holland, M. M., Risser, P. G., & Naiman, R. J. (Eds.). (1991). Ecotone. The role of landscape boundaries in the management and restoration of changing environments. Chapman & Hall. Kolasa, J., & Pickett, S. T. A. (Eds.). (1991). Ecological heterogeneity. Springer. Margalef, R. (1968). Perspectives in ecological theory. The University of Chicago Press. Naiman, R. J., Holland, M. M., Decamps, H., & Risser, P. G. (1988). A new UNESCO program: Research and management of land: Inland water ecotones. Biology International, Special Issue, 17, 107–136. Pickett, S. T. A., & White, P. S. (Eds.). (1985). Natural disturbance and patch dynamics. Academic. Shorrocks, B., & Swingland, I. R. (Eds.). (1990). Living in a patchy environment. Oxford University Press. Turner, M. G. (Ed.). (1987). Landscape heterogeneity and disturbance. Springer.

Chapter 7 Binkley, D., Sisk, T., Chambers, C., Springer, J., & Block, W. (2007). The role of old-growth forests in frequent-fire landscapes. Ecology and Society, 12(2).

Suggested Readings

445

Blondel, J., & Aronson, J. (1999). Biology and wildlife of the Mediterranean region. Oxford University Press. Goldammer, J. G., & Jenkins, M. J. (Eds.). (1996). Fire in ecosystem dynamics. Mediterranean and Northern perspectives. SPB Academic Publishing BV. Grove, A. T., & Rackham, O. (2001). The nature of Mediterranean Europe. An ecological history. YaleUniversity Press. Gunderson, L. H., & Holling, C. S. (Eds.). (2002). Panarchy. Understanding transformations in human and natural systems. Island Press. Kalin, M. T. A., Zedler, P. H., & Fox, M. D. (Eds.). (1995). Ecology and biogeography of Mediterranean ecosystems in Chile, California, and Australia. Springer. Navarro, L. M., & Pereira, H. M. (2015). Towards a European policy for rewilding. In Rewilding European Landscapes (pp. 205–223). Springer. Plachter, H., & Hampicke, U. (Eds.). (2010). Large-scale livestock grazing: A management tool for nature conservation. Springer. Satterthwaite, D. (2005). The scale of urban change worldwide 1950–2000 and its underpinnings (No. 1). IIED. Trabaud, L. (Ed.). (1987). The role of fire in ecological systems. SPB Academic Publishing. Vlachogianni, T., Vogrin, M., & Scoullos, M. (2012). Biodiversity in the Mediterranean region. MIO-ECSDE. www.mio-ecsde.org.

Chapter 8 Anonimus. (1991). Community woodland design. Guidelines. HMSO. Anonimus. (1992). Lowland landscape design. Guidelines. HMSO. Austad, I., Hauge, L., & Helle, T. (1993). Maintenance and conservation of the cultural landscape in Sogn ogFjordane, Norway. Department of Landscape Ecology, Sogn og Fjordane College. Berger, J. J. (Ed.). (1990). Environmental restoration. Science and strategies for restoring the earth. Island Press. di Castri, F., & Balaji, V. (Eds.). (2002). Tourism, biodiveristy and information. Backhuys Publishers. Drew, C. A., Wiersma, Y. F., & Huettmann, F. (Eds.). (2010). Predictive species and habitat modeling in landscape ecology: Concepts and applications. Springer. Green, B., & Vos, W. (Eds.). (2001). Threatened landscapes. Conserving cultural environments. Spon Press. Halladay, D., & Gilmour, D. A. (Eds.). (1995). Conservation biodiversity outside protected areas. The role of traditional agro-ecosystems. IUCN. Harris, L. D. (1984). The fragmented forest. University of Chicago Press. Mazzoleni, S. (2004). In G. di Pasquale, M. Mulligan, P. di Martino, & F. Rego (Eds.), Recent dynamics of the Mediterranean vegetation and landscape. Wiley. van Droste, B., Plachter, H., & Rossler, M. (Eds.). (1995). Cultural landscapes of universal value. Gustav Fischer. Westman, W. E. (1985). Ecology, impact assessment and environmental planning. Wiley.

Chapter 9 Bailey, R. G. (1998). Ecoregions. The ecosystem geography of the oceans and continents. Springer. Blondel, J., & Aronson, J. (1999). Biology and wildlife of the Mediterranean region. Oxford University Press.

446

Suggested Readings

Chester, J. W. (1997). The great famine: Northern Europe in the early fourteenth century. Princeton University Press. Clayton, L. W. (2003). Identity and the natural environment: The psychological significance of nature. Mit Press. Driskell, J. E., & Salas, E. (Eds.). (2013). Stress and human performance. Psychology Press. Dulias, R. (2016). The impact of mining on the landscape: A study of the upper Silesian Coal Basin in Poland. Springer. Gould, P., & White, R. (1986). Mental maps. Allen & Unwin. Hallady, D., & Gilmour, D. A. (Eds.). (1995). Conservation biodiversity outside protected areas. The role of traditional agro-ecosystems. IUCN. Ingold, T. (2000). The perception of the environment. Routledge. Thomashow, M. (1995). Ecological identity. Becoming a reflective environmentalist. MIT Press. Ulanowicz, R. E. (1983). Ecology, the ascendant perspective. Columbia University Press.

Chapter 10 Burrough, P. A. (1986). Principles of geographic information systems for land resources assessment. Clarendon. Cracknell, A. P., & Hayes, L. W. B. (1993). Introduction to remote sensing. Taylor & Francis. Diggle, P. J. (1983). Statistical analysis of spatial point patterns. Academic Press. Farrell, J. A., & Barth, M. (1999). The global positioning system & inertial navigation. McGrawHill. Feder, J. (1988). Fractals. Plenum. Gergel, S., & Turner, M. (Eds.). (2002). Learning landscape ecology. A practical guide to concepts and techniques. Springer. Hastings, H. M., & Sugihara, G. (1993). Fractals. A user’s guide for the natural sciences. Oxford University Press. Johnson, P. J. (1969). Remote sensing in ecology. University of Georgia Press. Leick, A. (1990). GPS satellite surveying. Wiley. Hofmann-Wellenhof, B., Lichteneger, H., & Collins, J. (1993). Global positioning system, theory and practice (2nd ed.). Springer. Lillesand, T. M., & Kiefer, R. W. (1967). Remote sensing and image interpretation (2nd ed.). Wiley. Ludwig, J. A., & Reynolds, J. F. (1988). Statistical ecology. Wiley. Maguire, D. J., Goodchild, M. F., & Rhind, D. W. (Eds.). (1991). Geographical information systems. Longman Scientific & Technical. Mandelbrot, B. (1982). The fractal geometry of nature. Freeman. Newman, E. A., Kennedy, M. C., Falk, D. A., & McKenzie, D. (2019). Scaling and complexity in landscape ecology. Frontiers in Ecology and Evolution, 7, 293. Ripley, B. D. (1981). Spatial statistics. Wiley. Steiniger, S., & Hay, G. J. (2009). Free and open source geographic information tools for landscape ecology. Ecological Informatics, 4(4), 183–195. Tal, D., & Altschuld, J. (2021). Drone technology in architecture, engineering and construction: A strategic guide to unmanned aerial vehicle operation and implementation. Wiley. Tomlin, C. D. (1990). Geographic information systems and cartographic modelling. Prentice-Hall. Turner, M. G., & Gardner, R. H. (Eds.). (1991). Quantitative methods in landscape ecology. Springer.