Aquatic Ecosystems [1 ed.] 9781620814871, 9781613243992

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Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Aquatic Ecosystems, edited by Sheila A. Browne, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Aquatic Ecosystems, edited by Sheila A. Browne, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

MARINE BIOLOGY

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AQUATIC ECOSYSTEMS

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Aquatic Ecosystems, edited by Sheila A. Browne, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

MARINE BIOLOGY

AQUATIC ECOSYSTEMS

SHEILA A. BROWNE Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

EDITOR

Nova Science Publishers, Inc. New York

Aquatic Ecosystems, edited by Sheila A. Browne, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

Copyright © 2012 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. Library of Congress Cataloging-in-Publication Data Aquatic ecosystems / editor, Sheila A. Browne. p. cm. Includes index.

ISBN:  (eBook)

1. Aquatic ecology. 2. Aquatic biodiversity. I. Browne, Sheila A. QH541.5.W3A6788 2011 577.6--dc22 2011011442

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CONTENTS Preface

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Chapter 1

vii Assemblages of Zoosporic True Fungi, Heterotrophic Straminipiles and Plasmodiophorids in Freshwater Ecosystems Agostina V. Marano, Carmen L. A. Pires-Zottarelli Frank H.Gleason, Sigrid Neuhauser and Mónica M. Steciow

Chapter 2

Aquatic Ecosystem Health: A Review Surjya Kumar Saikia, Santanu Ray, Joyita Mukherjee and Madhumita Roy

Chapter 3

The Eutrophication of Aquatic Ecosystems: Causes, Effects and Rehabilitation Gabriela Elena Dumitran and Liana Ioana Vuţă

Chapter 4

Chapter 5

Peculiarities of Aquatic Ecosystems Functioning with Anthropogenic Impact: (Thermodynamic Aspect) A.M. Nikanorov and M. M. Trofimchouk Cyclic Recurrence of Intrareservoir Processes when Anthropogenic Impact Occurs on Freshwater Ecosystems A.M. Nikanorov and B.L. Sukhorukov

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vi Chapter 6

Contents Change of Biodiversity of Subarctic Freshwater Ecosystems: Case Study Russian North-West Region T.I. Moiseenko, A. N. Sharov , O.I. Vandysh, V. A. Yakovlev and C. N. Gashev

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Index

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PREFACE In this book, the authors present topical research in the study of aquatic ecosystems of which there are two main types: marine ecosystems and freshwater ecosystems. Topics discussed include a change of biodiversity in subarctic freshwater ecosystems located in the northwest region of Russia; assemblages of zoosporic true fungi, heterotrophic straminipiles and plasmadiophorids in freshwater ecosystems; aquatic ecosystem health and the causes, effects and rehabilitation of the eutrophication of aquatic ecosystems. Chapter 1 - Zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids are phylogenetically unrelated groups of microorganisms that belong to the phyla Blastocladiomycota, Chytridiomycota, Neocallimastigomycota, Hyphochytriomycota, Labyrinthulomycota, Oomycota and the Class Phytomyxea and are currently placed into three different supergroups in the tree of life: Opisthokonta, Chromalveolata and Rhizaria. Recently these organisms have been grouped together with the heterotrophic flagellates due to the production of zoospores which are similar in size and morphology. They are ubiquitous and primarily inhabitants of freshwater but they also inhabit brackish, marine and terrestrial habitats, and have similar ecological roles in food webs as saprobes, mutualists or parasites. Saprobes decompose a large variety of substrates derived from plant and animal tissues such as chitin, cellulose, keratin, lignin and sporopollenin; mutualists are found to occur as obligate anaerobes in the digestive system of herbivorous vertebrates; whereas parasites occur on algae, crustaceans, fungi, macrophytes, protists, fishes, amphibians and invertebrates. Although their importance in food webs has recently been highlighted by many authors, there is little information on these organisms in many ecosystems and thus, the diversity, distribution and abundance of these organisms still remains largely unknown. Ecosystems are

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Sheila A. Browne

being degraded by deforestation, pollution and other anthropogenic actions that affect their biodiversity, long before surveys can be undertaken by mycologists and protistologists. Hence, attempts to gather information about the diversity of zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids are urgently needed. In this contribution the author summarizes the state of knowledge of the different groups, including taxonomic, biogeographic and ecological approaches Chapter 2 - Ecosystem health is a symbol for a complex set of ecological realities, rather than a condition that can be measured or monitored directly. It is an idea comprising a class of phenomena. Ecosystem health describes the preferred state of sites modified by human activity. The science of ecology has been primarily to the non-cultural elements of ecosystem health, and has focused on such concepts as species distribution and abundance, the structure, stability, and productivity of ecosystems, and the ability of ecosystems to self organize and evolve. Ecosystem health clearly refers to an ecosystem state, where the identity of the system and the integration of its elements, substantially intact and undamaged. It is not expected to observe ecosystem health directly rather many indices are used to measure the ecosystem health. This chapter describes a number of principles that may contribute to the practical pursuit of ecosystem health as a goal in science based management of natural resources of aquatic ecosystem. Many health indices such as exergy, ascendancy and emergy and their applications to measure the ecosystem organization towards its health are also described Chapter 3 - This paper aims to outline the basic concepts of eutrophication of aquatic ecosystems, in particular with reference to lakes and reservoirs causes, stages of development, and effects on water quality and rehabilitation possibilities of damaged ecosystems. The objectives of this study are multiple and approach issues related to understanding the process of eutrophication, which essentially consist in super fertilization of surface waters. This is one of the most serious problems affecting surface water quality in today's society due to the effects generated in water bodies. Thus, large quantities of nutrients induce excessive growth of plants and hence the associated effects: algae bloom, high quantities of macrophyte, organoleptic changes and impact on aquatic life and even on human health. Overall, the paper tries to give answers to questions as: What is eutrophication? What are the causes and the effects of this phenomenon, or What are the possibilities for rehabilitation of eutrophic ecosystems. So the paper tries to provide relevant information to lake ecosystem managers on how

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Preface

ix

to avoid this phenomenon, or when not possible, the possibilities of diminishing its effects on lake water quality. Chapter 4 - The full – scale experiments at natural model ecosystems (mesocosms) have been made in Hydrochemical Institute and Southern Division of Institute of Water Problem of RAS for 20 years with the purpose to study an impact of pollutants on intrabasin processes. In the experiment, in particular, an intensity of photosynthetic production and organic matter destruction as generalized thermodynamic parameters of ecosystem state were investigated in the process of polluting of natural models with cadmium in concentration from 25 to 750μg/dm3. The three – dimensional phase pictures of ecosystems on the basis of dynamics of destruction values (R) and gross primary production (P), ratio of destruction to gross primary production (R/P) and rate of change in this ratio (Δ(R/P).t-1) are plotted. Owing to the use of three – dimensional phase space in the dynamics of ecosystem state the critical points – points of bifurcation in which, supposedly, a change in the regimes of ecosystem functioning and transition to a new state occurs are revealed. In all cases it was observed at zero or close to zero rates of change in R/P value and sharp change in the direction of phase trajectories. In general case according to Prigozhin theorem such regularities are characteristic of limit states of dissipative systems and correspond to transition between stability and instability, when production of excess entropy disappears. An analytical treatment of ecosystem behavior in three – dimensional phase space demonstrated that ecosystems after toxic impact strive to retain an initial balance of the processes of production and destruction regardless of absolute values of intensity of these processes and, to all appearance, when various structural characteristics of biota. Thus, it was revealed that a change in a biotic structure of ecosystem and intensity of production – destruction processes in response to change in environment conditions are directed to the retention of optimum balance of these processes under new conditions. It is also determined that the ecosystems that were disturbed from the stationary state under impact of outside factors (pollution and others) tend to reset in the state characterized not only by the optimum balance of destruction and primary production but the minimum rate of change in the ratio of these parameters. In this case a phenomenon of kind of “ecological hysteresis” manifested both in delay of an ecosystem respond to the outside impact and in a shift of an ecosystem as a minimum in one of three projections of phase space is observed. The author suggests that a dynamics of ecosystem state suffers anthropogenic impact and described by the values of primary production and

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Sheila A. Browne

destruction complies with the general laws of evolution of states of dissipative systems. Thus, a ratio of destruction to primary production can consider as a measure of thermodynamic order of ecosystem, and a rate of change in ratio of destruction to production is suggested to consider as a thermodynamic criterion of evolution of ecosystem state. Using the above mentioned criterion it is possible to reveal typical regimes of ecosystem functioning: stationary states, critical points (points of bifurcation) and transitions from one state into another, and also to determine the limits of aquatic ecosystem stability. The proposed approach can be taken as a basis of ecological standardization of anthropogenic impact upon natural ecosystems. Chapter 5 - The long-term data on the change in the state of natural and man-made freshwater ecosystems under the impact of natural and anthropogenic pollution are collected. The man-made aquatic ecosystems – mesocosms – in the process of the long-term experiments were subjected to pollution with the compounds of heavy metals. The volume of mesocosms in various experiments was from 2 to 5 m3. The mesocosms were polyethylene cylinders deepened into bottom sediments and fixed from above with floats or hand ropes stretched from a planked footway from which the measurements were made. The observations of these ecosystem responses according to various parameters under various experiment conditions were carried out. The compounds of Cu and Hg were chosen as pollutants. Concentrations of compounds in recalculation on pure metals in some experiments were changed, but within the limits from 5 to 100 µg/dm3 for Cu, and from 0,1 to 2 µg/dm3 for Hg, respectively. The main part of the experiments was made together with the measurement of spectra of brightness coefficients of radiation upwelling from the water. Transformed spectrometric data are presented in the form of trajectories in a space of optic images plotted on the basis of reducing a large number of model and experimental spectra. The trajectories of aquatic ecosystems in this space are quasi-closed curves. The time scale of the processes under various conditions differs by orders. The explanation of such cyclic processes is possible on the basis of the introduction notion of integrated indexes of aquatic ecosystem state and usage of the Le Chatelier-Brown principle. Cyclic change in trajectories in variables of “impact-response” allowed to formulate a concept of ecological hysteresis. If generalized coordinates of optic image are used as variables then by analogy with classic thermodynamic reasoning, it is possible to suppose that the parameters of optic images can be considered as the parameters of aquatic ecosystem state.

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Chapter 6 - On the basis of generalisation of extensive materials on surface waters flora and fauna condition in the Kola North (Russia) there is formed the conception of contemporary tendencies in changes of biodiversity under impact of water pollution by heavy metals, eutrophication, acidification and discharge of warm water by nuclear power station (NPS). There are marked both general traits in communities’ reorganisation and specific response to the given kinds of impact. There are suggested the indices for monitoring of biodiversity of Subarctic freshwater ecosystems.

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In: Aquatic Ecosystems Editor: Sheila A. Browne

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Chapter 1

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ASSEMBLAGES OF ZOOSPORIC TRUE FUNGI, HETEROTROPHIC STRAMINIPILES AND PLASMODIOPHORIDS IN FRESHWATER ECOSYSTEMS Agostina V. Marano1*, Carmen L. A. Pires-Zottarelli2, Frank H.Gleason3, Sigrid Neuhauser 4 and Mónica M. Steciow 1 1

Instituto de Botánica Spegazzini, Universidad Nacional de La Plata, calle 53 N 477, La Plata, 1900, Buenos Aires, Argentina 2 Instituto de Botânica, CP 3005, 01061-970 São Paulo, SP, Brasil 3 School of Biological Sciences A12, University of Sydney, NSW, 2006 Australia 4 Institute of Microbiology, Leopold Franzens University Innsbruck, Technikerstraße 25, 6020 Innsbruck, Austria

ABSTRACT Zoosporic true fungi, heterotrophic straminipiles and plasmadiophorids are phylogenetically unrelated groups of micro-organisms *

Corresponding author: [email protected]

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Agostina Marano, Carmen Pires-Zottarelli, Frank Gleason et al. that belong to the phyla Blastocladiomycota, Chytridiomycota, Neocallimastigomycota, Hyphochytriomycota, Labyrinthulomycota, Oomycota and the Class Phytomyxea and are currently placed into three different supergroups in the tree of life: Opisthokonta, Chromalveolata and Rhizaria. Recently these organisms have been grouped together with the heterotrophic flagellates due to the production of zoospores which are similar in size and morphology. They are ubiquitous and primarily inhabitants of freshwater but they also inhabit brackish, marine and terrestrial habitats, and have similar ecological roles in food webs as saprobes, mutualists or parasites. Saprobes decompose a large variety of substrates derived from plant and animal tissues such as chitin, cellulose, keratin, lignin and sporopollenin; mutualists are found to occur as obligate anaerobes in the digestive system of herbivorous vertebrates; whereas parasites occur on algae, crustaceans, fungi, macrophytes, protists, fishes, amphibians and invertebrates. Although their importance in food webs has recently been highlighted by many authors, there is little information on these organisms in many ecosystems and thus, the diversity, distribution and abundance of these organisms still remains largely unknown. Ecosystems are being degraded by deforestation, pollution and other anthropogenic actions that affect their biodiversity, long before surveys can be undertaken by mycologists and protistologists. Hence, attempts to gather information about the diversity of zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids are urgently needed. In this contribution we will summarize the state of knowledge of the different groups, including taxonomic, biogeographic and ecological approaches.

INTRODUCTION The “Fungi” in its original morphophysiological description comprises a diverse assemblage of true fungi and fungal-like organisms. To date, over 3,000 taxa have been reported from aquatic habitats with a greater overall diversity recorded from temperate, as compared to tropical regions. The greatest number of taxa belongs to the meiosporic and mitosporic Ascomycota, followed by the Chytridiomycota (Shearer et al., 2007). Zooporic true fungi, heterotrophic straminipiles and plasmodiophorids belong to the Blastocladiomycota, Chytridiomycota, Neocallimastigomycota, Hyphochytriomycota, Labyrinthulomycota, Oomycota and Phytomyxea (Kirk et al., 2008). What unifies this group of organisms is the presence of a motile spore (the zoospore), primarily osmotrophic nutrition and the absence of chlorophyll. Zoospores develop into morphologically heterogeneous

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structures which range from microscopic single cells (e.g. Phytomyxea, monocentric Chytridiomycota) to more or less complex filamentous systems (e.g. polycentric Chytridiomycota, Oomycota). In this chapter, we will summarize the current knowledge of zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids from freshwater habitats based on taxonomic, biogeographical and ecological approaches. We will primarily consider studies on the Blastocladiomycota, Chytridiomycota, Oomycota and Phytomyxea worldwide. Finally, the most promising areas for further research are discussed. The term zoosporic true fungi will be used in this chapter to refer to members of the Phyla Blastocladiomycota and Chytridiomycota, while heterotrophic straminipiles will be used here to refer to the Phyla Oomycota and Hyphochytriomycota and plasmodiophorids to the Class Phytomyxea.

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PHYLOGENY Zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids form a heterogeneous group of eukaryotic microorganisms which Sparrow (1960) called the “aquatic phycomycetes”. Because of the presence of zoospores, Sime-Ngando et al. (2011) placed them among the heterotrophic flagellates (HF). Recent studies have demonstrated that the “aquatic phycomycetes” are polyphyletic (Keeling et al., 2005) and their similarities may be due to convergent evolution (Barr, 1983). However, because these microorganisms are commonly found in the same habitats, and have historically been treated as fungi, they are often included together in the same studies. For many years all of these groups were considered to be part of the Mastigomycotina in the Kingdom Fungi (Whittaker, 1959; Ainsworth, 1973). More recently Cavalier-Smith (1981, 1986, 2001), Barr (1992), Hawksworth et al. (1995), Moore-Landecker (1996) and Kirk et al. (2001) placed the Oomycota, Hyphochytriomycota and Labyrinthulomycota in the Kingdom Chromista. Dick (2001) renamed the Chromista as Straminipila (Lat. stramen= straw, pilus= hair) due to the presence of one straminipilous flagellum, considering as their ancestor a heterotrophic flagellate protist. Based on the data from gene sequences which code for α and β-tubulin, actin and the elongation factor 1 (EF-1), Baldauf et al. (2000) considered the Chytridiomycota as part of the Kingdom Fungi and the Oomycota, Hyphochytriomycota and Labyrinthulomycota as part of the Kingdom

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Straminipila together with the phaeophyceaen algae and as a sister clade to the Ciliophora and Apicomplexa (Sporozoa). The Phytomyxea (plasmodiophorids) have recently been placed in the supergroup Rhizaria (Bass et al., 2009a). The organisms that were commonly referred to as “aquatic phyco mycetes” belong to the phyla Chytridiomycota, Blastocladiomycota, Neocallimastigomycota, Oomycota, Hyphochytriomycota, Labyrinthulomycota and to the Class Phytomyxea. The genera Olpidium and Rozella traditionally described in the Chytridiomycota are currently considered as two new and separate lineages with Olpidium among the Zygomycota and Rozella appearing as the earliest branch to diverge in the fungal kingdom (James et al., 2006a; Kirk et al., 2008). The genera Olpidium and Rozella still remain to be assigned to phyla. These organisms are distributed into three of the six supergroups in the tree of life: Opisthokonta, Chromalveolata (subgroup Straminipila) and Rhizaria (Baldauf, 2003; Adl et al., 2005). The Kingdom Fungi is now part of the supergroup Opisthokonta (Adl et al., 2005). Traditional views of fungal phylogeny indicate that fungi with flagellated cells (i.e. zoosporic true fungi) are the sister group to the remaining phyla of non-flagellated fungi (Zygomycota, Glomeromycota, Ascomycota and Basidiomycota). Recently, James et al. (2006a) have shown that the phylum Chytridiomycota is polyphyletic. Studies based on the rDNA sequences demonstrated that zoosporic true fungi are a basal group within the Kingdom Fungi (James et al., 2007). The presence of zoospores is an ancestral character (plesiomorphic) which relates them to a choanoflagellate ancestor (Adl et al., 2005). During the last few years, much reorganization have been undergoing within the Phylum Chytridiomycota which at present comprises more than 700 species (Kirk et al., 2008). The class Chytridiomycetes has been splitted out into two classes: Chytridiomycetes and Monoblepharidomycetes. The Monoblepharidomycetes (James et al., 2007; Hibbett et al., 2007) accomodated the members of the traditional order Monoblepharidales. Many orders of Chytridiomycetes have also been restructured and seven new orders have been established: Chytridiales, Chytridiomycetales, Cladochytriales, Lobulomycetales, Rhizophlyctiales, Rhizophydiales and Spizellomycetales (Letcher et al., 2006, 2008a,b; Mozley-Standridge, 2009; Simmons et al., 2009; Wakefield et al., 2010; Vélez et al., 2011). James et al. (2006b) recognised the Blastocladiales as distinct from the other orders in the Chytridiomycota and elevated this order to the level of phylum (Blastocladiomycota) and showed that members of this phylum are

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more closely related to the non-zoosporic fungi than to the Chytridiomycota. The Blastocladiomycota presently contains one order (Blastocladiales) and five families (Blastocladiaceae, Catenariaceae, Coelomomycetaceae, Sorochytriaceae and Physodermataceae) with 179 species (Kirk et al., 2008). The Neocallimastigomycota is a group of obligate anaerobic, mutualistic zoosporic true fungi which have been observed in the digestive system of herbivorous vertebrates (Liggenstoffer et al., 2010). They will not be discussed further in this paper. The heterotrophic straminipiles (Straminipila) and the plasmodiophorids (Phytomyxea) forms part of the “SAR” supergroup, which contains Straminipila, Alveolata and Rhizaria (Burki et al., 2007; 2008; 2010). Straminipiles are characterized by forming zoospores with one anteriorly or two laterally-inserted flagella (one of the two, the tinsel flagellum, bears tubular hairs and the second is a smooth whiplash type), by synthesing lysine via the α, ε-diaminopimelic acid pathway (DAP) and by having a cell wall composed of β-glucans, hydroxiprolins, cellulose, small quantities of chitin in some species and mitochondria with tubular cristae (Alexopoulos et al., 1996). This Kingdom Straminipila comprises three heterotrophic phyla: the Hyphochytriomycota, Labyrinthulomycota and Oomycota which differ in the number and insertion (apical or lateral) of the flagellum in the zoospore (Hawksworth et al., 1995) since some of them have lost the whiplash flagellum through evolution. The straminipiles are now considered part of the supergroup Chromalveolata (Adl et al., 2005). Members of the Hyphochytriomycota (hyphochytrids) are a small group of organisms with only one order, two families, six genera and 24 species (Kirk et al., 2008). Hyphochytrids are characterized by the production of zoospores with one anteriorly directed flagellum which bears mastigonemes. This phylum has recently been proposed as the sister group to the Oomycota (Van der Auwera et al., 1995; Hausner et al., 2000). The Labyrinthulomycota comprises three lineages: labyrinthulids, thraustochytrids, and aplanochytrids (Leander et al., 2004; Schärer et al., 2007). Since they are primarily marine organisms they are not included in this chapter. The lower level taxonomy of the Oomycota has been reorganized many times (Dick et al., 1984; Dick et al., 1989; Dick, 1990; 1995) and although the phylum is considered to be monophyletic (Gunderson et al., 1987, Förster et al., 1990) considerable rearreagements are still being performed at the levels of orders and families. Dick et al. (1984) subdivided the class Oomycetes (Peronosporomycetes) in the subclasses Saprolegniomycetidae

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and Peronosporomycetidae. Based on DNA studies Saprolegniomycetidae comprises the orders Saprolegniales, Leptomitales and Sclerosporales and Peronosporomycetidae the orders Pythiales and Peronosporales (Dick et al., 1989). The subclass Rhipidiomycetidae formerly proposed by Dick et al. (1989) and Dick (1995) accommodated the members of the Rhipidiales. At present this phylum contains one class, 13 orders, 25 families, 106 genera and 956 species (Kirk et al., 2008). Nevertheless, the use of molecular techniques for building up phylogenies might lead to further reorganization of most of the higher taxonomic ranks. The Phytomyxea (common name plasmodiophorids) are a monophyletic group which includes two orders: Plasmodiophorida and Phagomyxida with 12 genera and 41 species (Bass et al., 2009a Neuhauser et al., 2011). The taxonomic position of this group has been frequently debated. They were first considered to be protists and later to be members of the Mastigomycotina together with the zoosporic true fungi and heterotrophic straminipiles. Currently they are considered to be members of the supergroup Rhizaria (Cavalier-Smith, 1993; Bulman et al., 2001; Archibald & Keeling, 2004; Nikolaev et al., 2004; Adl et al., 2005; Bass et al., 2009a). Plasmodiophorids are obligate biotrophic parasites of green plants and straminipiles (e.g., oomycetes, diatoms, brown algae). These microorganisms have complex life cycles with two free swimming zoosporic stages, two different plasmodial stages within the host, a thin walled zoosporangium and a thick walled resting stage.

DISTRIBUTION AND BIOGEOGRAPHY The biogeographic distribution of fungi in freshwater habitats appears to follow three main patterns (modified from Wood-Eggenschwiler & Bärlocher, 1985): (i) cosmopolitan: several species with a worldwide distribution; (ii) limited: some species restricted to temperate and cold, while others to tropical and warm regions; and (iii) restricted: a few species distributed in only small geographical areas (e.g. endemism). It is still not known if the distribution of zoosporic true fungi and heterotrophic straminipiles follows a similar biogeographic pattern. Most zoosporic true fungi and fungus-like organisms are ubiquitous and are found throughout the world in freshwater, brackish and marine environments, as well as in terrestrial habitats. They are thought to be cosmopolitan (i.e. they can be found wherever their required habitat is

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available), although a few examples of limited or restricted distribution can be found in literature. Whether or not some taxa have a limited distribution is debatable. For example, Phragmosporangium uniseriatum had been only recorded in two soils of West Africa since 1970, although approximately 10000 soil samples from tropical areas have been analysed (Johnson et al., 2005). Therefore, P. uniseriatum has been regarded as a rare species with limited distribution until 2006 when it was re-isolated in Brazil (PiresZottarelli et al., 2007a). Most plant pathogenic Oomycetes appeared to have an originally limited phylogeography. The best known example is the potato late blight pathogen Phytophthora infestans which caused the potato famine in Ireland (Frey, 2008). This pathogen is thought to have spread out from its geographical origin in America to other regions worldwide. Surveys on zoosporic true fungi and heterotrophic straminipiles have been concentrated on temperate regions of North America and Europe, hence, a greater number of taxa have been reported from temperate regions as compared to tropical and subtropical regions. For example, the Saprolegniales of North and South America and Europe have been surveyed more intensively than those of Africa, Australia, and the Atlantic and Pacific Islands (Shearer et al., 2007). Therefore, the distribution of zoosporic true fungi and heterotrophic straminipiles as determined from studies to date could be due to the fact that most groups have been under-sampled in many regions of the world, particularly in southern temperate and tropical regions around the world, the Middle East, and Arctic/Antarctic regions. We predict that the major fraction of unknown fungal species will be found in tropical areas, where diversity is considered to be higher than in temperate regions because of more favorable environmental conditions and the high number of niches and microhabitats (Hyde & Hawkswort, 1997; Hawksworth, 2001). As an example, during the last ten years many new species have been discovered in Argentina (Steciow, 2001a,b,c; 2002; 2003a,b; Steciow & Elíades, 2002a,b,c; Steciow et al., 2005; Steciow & Marano, 2006, 2008; Steciow & Paul, 2007; Letcher et al., 2008a,b) and new records of existing species have been documented for Argentina and Brazil (Steciow & Elíades, 2001, 2002a; Steciow et al., 2001a,b; Rocha & PiresZottarelli, 2002; Milanez et al., 2003; Herrera et al., 2005; Rosa et al., 2006; Schoenlein-Crusius et al., 2006; Gomes & Pires-Zottarelli, 2006; PiresZottarelli et al., 2007a,b; Pires-Zottarelli & Gomes, 2007; Pires-Zottarelli & Rocha, 2007; Marano et al., 2006, 2007, 2008a; Nascimento & PiresZottarelli, 2009, 2010).

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Although a much higher diversity is expected in the tropics, these regions have been poorly studied to date. For example, the number of zoosporic species already reported in Argentina and Brazil is equal to the 8.7 % and 17.5 % respectively (Steciow et al., unpublished data) of the worldwide number provided by Kirk et al. (2008). There are only a few studies documenting their presence in other South American countries such as Bolivia, Chile, Colombia, Venezuela and Ecuador, most of them written by foreign mycologists (Emerson, 1941; Sparrow, 1960; Persiel, 1960; Dogma, 1974a,b; Fell & Master, 1975; Karling, 1981a,b, 1984, 1985; Orduz et al., 1992; Vivar Muñoz & Bernal, 1997; Zaror et al., 2004; Barrionuevo et al., 2008). The diversity and biogeography of eukaryotic microorganisms have been thoroughly and controversially debated in the recent years (see e.g. Foissner, 2008; Weisse, 2008). The main question that needs to be addressed is whether global distribution patterns of zoosporic true fungi and heterotrophic organisms are real or merely reflect the regions which have been intensively sampled (and thus the distribution of scientists studying them). This raises the following question: Are cases of endemism reliable reflecting the distribution of a particular taxa or are them reflecting merely those areas in which the taxa have been sampled? Thus, many cases of endemism need to be re-evaluated.

ECOLOGICAL ROLES Zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids have been previously described as part of “small unidentified heterotrophic flagellates” based on the analysis of the 18S rDNA (Lefèvre et al., 2007, 2008) because their zoospores lack distinctive morphological characteristics. Furthermore, these organisms have been frequently under-sampled in DNAbased environmental screening studies due to negative primer bias and the use of “general” non-targeted PCR-primers (Stoeck et al., 2007; Bass et al., 2007, 2009b). Recently, zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids have been grouped together with other flagellated groups (e.g. Choanoflagellates, Bicosoecids, Proteromonads, Opalines, heterotrophic Chrysophytes and Dinoflagellates) into the heterotrophic flagellates (HF). Heterotrophic flagellates are defined as microorganisms with flagellated cells in at least one part of their life cycle and without chlorophyll (Sime-Ngando et al., 2011).

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Role in Food Webs The diverse groups of microorganisms that form the HF pool commonly inhabit the same habitats but explore different ecological niches (Sparrow, 1968; Nascimento et al., 2010) and are essential components of the microbial food webs, transferring nutrients directly and indirectly between trophic levels. The inclusion of zoosporic true fungi in food web fluxes was first proposed by Sigee (2005). Zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids are saprobes, parasites and mutualists but also act as sources of nutrients for consumers in freshwater systems. Thus, recycling of inorganic and organic nutrients, energy transfer between trophic levels, trophic upgrading and regulation of population size of their hosts are their most important roles in food webs (Gleason et al., 2008a; Sime-Ngando et al., 2011).

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Saprobes Zoosporic true fungi and heterotrophic straminipiles possess extremely diverse enzymatic capabilities including the ability to degrade a great variety of natural polymers of plant and animal origin such as cellulose (algae and vegetable debris), keratin (snake skin, hairs and feathers), chitin (fish scales, insect and crustacean exoesqueletons), lignin (woody plant tissues) and sporopollenin (pollen grains) (Sparrow, 1960; Shearer et al., 2004). Thus, they transform particulate organic matter (POM: dead phytoplankton, leaves, pollen, insects and crustacean exoskeletons, cadavers, and other detritus) into dissolved organic (DOM) and inorganic matter (DIM). (Haskins & Weston, 1950; Cantino, 1955; Willoughby, 1962; Nolan, 1970; Gleason, 1976; Hassan & Catapane, 2000; Midgley et al., 2006; Gleason et al., 2008a; Digby et al., 2010). Some species convert inorganic ammonium, nitrate, nitrite, sulphate and phosphate salts into organic compounds (Digby et al., 2010) while others are able to use insoluble phosphorous which they solubilise before being absorbed (Midgley et al., 2006). Thus, they make carbon, organic nitrogen, phosphorous and sulphur compounds available to other trophic levels in food webs. Proteins and polysaccharids are major constituents of plant and animal tissues. Laboratory-based studies on pure cultures showed that several species of zoosporic true fungi and heterotrophic straminipiles produce a rich array of enzymes for degrading polysaccharides, hydrolyzing proteins and

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metabolizing amino acids. They have been shown to have the capacity to use the amino acids alanine, aspargine, aspartate, glutamate, leucine, phenyl alanine, proline and serine, and the carbohydrates cellobiose, fructuose, glucose, maltose and sucrose as sole sources of carbon in many studies (Gleason, 1968; Gleason et al., 1970a,b, 2008a, 2011; Deacon, 1979; Thompstone & Dix, 1985; Steciow, 1993). This capacity appeared to be correlated with their growth on natural complex substrates such as hemp seeds (Gleason et al., 1970a). Different species (and even different isolates of the same species) are capable of using different carbon sources (Gleason, 1968; Gleason et al., 1970a,b; Gleason, 1976; Digby et al., 2010; Gleason et al., 2011), suggesting differential utilization of nutrient sources provided by a complex substrate in the environment. The reasons for nutritional variation in related species have not been yet clarified. Nevertheless, research on pure cultures only indicates the potential nutritional capacities of a species and does not consider the complexity of interactions between species and the environment. For example, most of the zoosporic true fungi tested by Gleason et al. (2011) did not grow in crystalline cellulose although they are commonly found growing on pollen grains and onion skin which contain cellulose as well as other carbohydrates in mixed culture with soil. It is worthwhile to note that some species of facultative anaerobes such as Aqualinderella, Mindeniella, Rhipidium and Blastocladia grow on fruits because they can ferment simple sugars in stagnant freshwater habitats (Gleason, 1968; Natvig, 1982; Gleason & Gordon, 1989).

(i) Degradation of Cellulose Debris: Relative Importance in Decomposition of Leaves The decomposition process of particulate organic matter (POM) might be divided into three phases: (a) leaching of soluble compounds into water; (b) colonization by microorganisms such as fungi and bacteria; and (c) fragmentation by mechanic activity (invertebrate feeding) (Petersen & Cummins, 1974). Thus, only a small part of the POM that enters aquatic ecosystems is rapidly used by detritivorous, since a previous colonization by fungi and bacteria is necessary (Bärlocher & Kendrick, 1973, 1974; Bärlocher, 1992). The colonization phase is characterized by microbial growth which changes the physical and chemical composition of leaves. Decomposition of gross particulate organic matter (GPOM) is carried out mainly by fungi while decomposition of fine particulate organic matter (FPOM) by bacteria (Nikolcheva & Bärlocher, 2004). Fungal growth generates an increase in the nitrogen content of the leaves (Kaushik & Hynes,

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1971), making them more palatable and nutritious for invertebrates. Thus, fungi act as trophic intermediaries between fallen leaves and the invertebrates that feed on them (Bärlocher, 1992) since they provide a source of amino acids that are not available in non-colonized leaves (Cummins & Klug, 1979). Fungal decomposers of leaves in aquatic environments include representatives of the Ascomycota (especially the Hyphomycetes), zoosporic true fungi and heterotrophic straminipiles and to a lesser extent some representatives of Zygomycota and Basidiomycota (Dix & Webster, 1995; Schoenlein-Crusius et al., 2006; Gessner et al., 2007). Zoosporic true fungi and heterotrophic straminipiles are ubiquitous and are very abundant on decomposed plant debris (e.g. leaves) (Willoughby, 1974). Also, the colonization of a great variety of cellulosic substrates (Willoughby & Redhead, 1973) and floating organic matter (e.g. leaves) by Pythium spp. and many other species is well-known (Rossi et al., 1983). Even so, research on zoosporic true fungi and heterotrophic straminipiles on decomposed leaves in freshwater, lags far behind the research on other groups of true fungi. Only a few studies documented the presence of Chytridiomycota and Oomycota during leaf breakdown in freshwater (Bärlocher & Kendrick, 1974; Schoenlein-Crusius & Milanez, 1989, 1998; Schoenlein-Crusius et al., 1990, 1992, 1999; Nikolcheva et al., 2003; Nikolcheva & Bärlocher, 2004; Moreira, 2006; Gulis et al., 2008; Nechwatal et al., 2008; Seena et al., 2008; Marano et al., 2011). On the contrary, in estuarine ecosystems, the importance of the oomycete Halophytophthora spp. in the decomposition of Rhizophora spp. leaves has been largely recognised (e.g. Newell et al., 1987; Newell & Fell, 1994, 1997; Raghukumar et al., 1995; Ravikumar et al., 1996; Ananda et al., 2008). Furthermore, some authors have observed a great diversity of zoosporic true fungi and heterotrophic straminipiles during decomposition of leaves in freshwater environments. Schoenlein-Crusius et al. (1990) first observed a greater number of zoosporic true fungi and heterotrophic straminipiles than of aquatic hyphomycetes on submerged leaves. In agreement with these observations, results based on molecular data have also shown a great contribution of these groups to leaf breakdown. Moreover, results based on molecular methods showed that Chytridiomycota appear to be more abundant on decomposed leaves than other fungal groups (Nikolcheva & Barlocher, 2004, Seena et al., 2008). For example, Seena et al. (2008) recorded 15 sequences belonging to Nowakowskiella spp., one to Nowakowskiella hemisphaerosphora Shanor, and two unidentified species of Chytridiales.

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Recently, Marano et al. (2011) found that Nowakowskiella elegans, Phytophthora sp., and Pythium sp. were dominant in relation to their frequency and abundance as compared to taxa of mitosporic fungi. However, a fuller understanding on the role of these organisms in leaf decomposition awaits further investigation. The majority of the studies have used methods that specifically encourage sporulation (i.e., moist chamber technique) focusing on mitosporic fungal communities (e.g. Ingold, 1942; Shearer & Webster, 1991; Shearer, 1993; Laitung & Chauvet, 2005) and not considering other groups of fungi and heterotrophic straminipiles. Moreover, fungal biomass is usually estimated by quantifying the ergosterol content (Gessner et al., 2003) and therefore total fungal biomass is underestimated since ergosterol is absent in Chytridiomycota and Oomycota.

(ii) Degradation of Chitin and Keratin Substrates Although zoosporic true fungi and heterotrophic straminipiles are most commonly found growing on cellulosic substrates, some of them can only grow on chitinous and/or keratinous materials (Sparrow, 1960, 1968; Karling, 1977). Chitin is an important and abundant biopolymer in aquatic ecosystems (Reisert & Fuller, 1962) which is found in arthropod exoskeletons (insects and crustaceans), fish scales and fungal cell walls. Microbial chitinases catalyze the hydrolysis of chitin, an unbranched polymer of 1,4-N-acetylglucosamine. Thus, chitinases play an important physiological and ecological role in ecosystems by releasing carbon and nitrogen sources (Cohen-Kupiec & Chet, 1998). Keratin, with its refractory chemical nature and its frequent physical toughness, supports great diversity of zoosporic true fungi and heterotrophic straminipiles which develop typically on keratinized tissues of animals (Sparrow, 1960; Cooke & Rayner, 1984), which are frequently found in aquatic ecosystems. The specificity for substrates usually varies between species, although nutritional preferences that are so apparent in nature may not be so well documented under laboratory conditions (Sparrow, 1968). As an example, Dick (1970) investigated the colonization of small insect exuviae in a lake in Canada. Saprolegnia diclina and Aphanomyces laevis were by far the most important primary colonizers although evidence for substrate preference was observed. Higher numbers of Saprolegnia were found on Trichoptera exuviae, whereas Achlya and, to a lesser extent, Leptolegnia, were more frequent on Anisoptera exuviae. Saprolegnia diclina and A. laevis were also

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the most frequent colonizers on exuviae of chironomid flies especially in shallow waters.

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Parasites Zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids occur as parasites on algae, crustaceans, fungi, macrophytes, protists (Sparrow, 1960, 1968; Karling, 1977; Edgerton et al., 2004; DiéguezUribeondo et al., 2007, 2009), fishes (Fregeneda-Grandes et al., 2007; van West, 2006; Phillips et al., 2008; Diéguez-Uribeondo et al., 2009), amphibians (Blaustein et al., 1994; Kiesecker et al., 2002; Rachowicz et al., 2006), invertebrates (Karling, 1944, 1946; Sparrow, 1960, 1968). Some of these organisms are known to have a narrow host range, whilst some have a broad range (Kagami et al., 2007) and this specificity appears to occur during encystment of the zoospores (Doggett & Porter, 1995). Some species of zoosporic true fungi can naturally regulate microphytoplankton blooms (Kagami et al., 2007; Gleason et al., 2008a), populations of crustaceans (Chapman, 1985; Johnson et al., 2006), insects and nematodes (Martin, 1984; Chapman, 1985; Jafee, 1986; Silva & Campos, 1991). Species of Catenaria, Coelomomyces and Olpidium are common parasites of small invertebrates in freshwater (Whisler, 1985; Powell, 1993; Dick, 2003; Barron, 2004). Coelomomyces contains species that are obligate parasites of insects such as Aedes aegypti (López-Lastra & García, 1997), A. vexans and Anopheles quadrimaculatus, which are are vectors of human diseases (Sparrow, 1960; Alexopoulos et al., 1996; Barr, 2001). Recently Gleason et al. (2010) reviewed the information available on blastocladean parasites (Blastocladiomycota) of invertebrates. Batrachochytrium dendrobatidis (Bd) causes the disease chytridiomycosis and appears to be responsible for the devastation and extintion of amphibia worldwide (Berger et al., 1998; Longcore et al., 1999; Bosch et al., 2001; Bradley et al., 2002; Carnaval et al., 2005; Rachowicz et al., 2006; Bosch et al., 2007). It has been suggested that Bd originated in Africa and subsequently spread to other parts of the world by trade in African clawed frogs (Weldon et al., 2004). Some genera such as Olpidium, Synchytrium and Physoderma include species that are pathogens of vascular plants many of them of economic importance. For example, Olpidium brassicae causes the putrescence in roots of many vegetables and acts as a vector for virus infestations such as LBVV

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on lettuce (Tomlinson & Faithfull, 1980) and TNV on pepper, lettuce, cucumber and tomato (Paludan, 1985; Rochon et al., 2004). Synchytrium endobioticum is responsable for the potatoe wart disease and Physoderma maydis causes the brown spot in Zea mays (Barr, 1990; 2001). The Oomycota are by far the best known because the group contains many species that responsible for significant economic losses in aquaculture and agriculture worldwide. Zoosporic plant pathogens cause significant crop losses worldwide and are the object of a substantial amount of epidemiological research (e.g. Phytophthora spp.). Some species could also opportunistically infect mammals, including humans (Phillips et al., 2008) such as P. insidiosum which is pathogen in subtropical and tropical areas (Wester & Weber, 2007) and causes potentially fatal infections (Mendoza, 2005). The most important orders in terms of economic relevance are the Peronosporales, Pythiales and Saprolegniales. Peronosporales are not considered in detail in this chapter because most of them are pathogens of crop plants (Rietmüller et al., 2002) and thus a primary terrestrial group. Many species of Phytophthora are among the most damaging pathogens in agriculture (e.g. P. infestans, the late blight pathogen) and silviculture (e.g. P. ramorum, P. alni). Phytophthora alni, for example, is known to be transmitted via water currents and causes fatal root rot of alders along rivers throughout Europe (Érsek & Nagy, 2008) and thus, severely influences alder riparian ecosystems (Adams et al., 2009). Among the Pythiales, Pythium is one of the best studied genera which can live saprotrophically in freshwater and natural and cultivated soils but when conditions are appropriate, they can be destructive parasites by causing damping-off of a great variety of crops. There are several species that are important pathogens in hydroponic systems, affecting a wide range of hosts around the world (Stanghellini & Kronland, 1986; Sutton et al., 2006). Saprolegniales parasites of freshwater crayfish and fish (e.g., salmon, trout, and catfish) are known to cause severe damage in aquacultures and wild populations (Edgerton et al., 2004; Phillips et al., 2008; van West, 2006). However, only very few parasitic members of the Saprolegniales have been more intensely studied. Willoughby (2003) listed several important parasitic species of freshwater fishes. An important example is Aphanomyces astaci which was introduced to Europe around a century ago on imported Pacifastacus leniusculus (Cerenius et al., 1988; Edgerton et al., 2004) and is now responsible for declining of populations of European native crayfishes such as Astacus astacus. This parasite produces abundant chitinase which is necessary for infection of crayfishes.

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Species of Saprolegnia have been implicated in a number of diseases of fish and their eggs causing “saprolegniosis” and ulcerative dermal necrosis (UDN). Aphanomyces invadans (syn A. piscida) causes epizotoic ulcertive syndrome (EUS), an emerging disease of farmed fish in warmer countries (Lilley et al., 1998). It is not absolutely clear whether members of Saprolegniales can infect previously uninjured fishes or if they are biotrophic parasites. For example, it is commonly observed that handling or bruising freshwater fishes leads to infection and high mortality rates. The role of Saprolegnia parasitica in a serious skin disease of mature salmonid fish (e.g. salmon and brown trout) is controversial. At present, it is accepted that S. parasitica might appear as primary parasite causing lesions in the skin that heal rapidly and develop into progressive necrosis. The fungus penetrates deeply into the musculature and might cause sudden death of the fish by circulation failure. Extracellular enzymatic activities such as hemolitic activity, casein hydrolysis and desoxyribonuclease production appeared to be particularly important in parasitic species of the genera Achlya, Saprolegnia and Aphanomyces (Alberts et al., 1989). The parasitic condition of some oomycetes could be beneficial to humans in some instances. Many heterotrophic straminipiles have potential use as biocontrol agents. Examples are Nematophthora which infects nematode eggs (Dick, 2001) and Lagenidium giganteum and Leptolegnia chapmanii both parasites of mosquito larvae (López Lastra et al., 1999; Kerwin, 2007). Although many aspects of the biology of host-pathogen interactions that involve oomycetes remain to be investigated, even less is known about their significance in ecosystem functioning. Some studies have highlighted the consequences of epidemics caused by eukaryotic pathogens on the abundance of primary producers in aquatic environments (e.g. Holfeld, 1998; Tilman, 1999). The inclusion of parasites in food web models leads to an increase in species richness, connectance (i.e., the percentage of possible links realized in a food web), and nestedness (the degree of structure and order in an ecosystem); and it might have an important impact on ecosystem stability (Lafferty et al., 2006; Wood et al., 2007). Plasmodiophorids are biotrophic parasites of vascular plants, brown algae, diatoms and straminipiles commonly found in many freshwater, soil and marine environments. In addition, many of them could also act as vectors of several plant viruses (Adams, 1991; Campbell, 1996; Rochon et al., 2004). They spend most of their lives endobiotically inside their hosts. However,

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they usually propagate with free swimming zoospores, the only stage of their life cycle that is not directly dependent upon a living host. Although most of the described species depend on hosts that are true aquatic organisms (oomycetes, diatoms, brown algae, freshwater aquatic plants, plants living in wet soils, and seagrasses) plasmodiophorids are still mainly known for causing serious plant diseases. Plasmodiophora brassicae is the causal agent of the economically important club root disease of brassicas (Dixon, 2009), and Spongospora subterranea causes powdery scab of potatoes (Webster & Weber, 2007) and transmits potato viruses (Merz & Fallon, 2009). Very recently, Neuhauser et al. (2011) proposed two ecological roles for plasmodiophorids besides their status as plant pathogens: (i) transfer of energy derived from primary producers or from saprobes and other parasites to the pool of HF; and (ii) alteration of the growth and chemical composition of their host which can indirectly modify the carbon flows in aquatic ecosystems.

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Mutualists Except for the Neocallimastigomycota which is group of zoosporic true fungi that are obligately anaerobic in the digestive system of herbivorous vertebrates (Liggenstoffer et al., 2010) and which are not going to be considered in further detail in this chapter, there is only one case describing a potential mutualistic relationship in literature. Rhizidium phycophilum was isolated on pollen grains from soil samples in Australia and described as a new species. Although this fungus appeared to grow as a saprotroph on pollen grains, thalli could only develop on nutrient medium in the presence of a coccoid green alga originating from the same soil sample. No nutrient medium has been found on which this fungus could grow as a single culture (Picard et al., 2009) and no parasitic relationship was demonstrated (Picard, unpublished data). The interaction between R. phycophilum and the algae is not yet fully understood and requires more comprehensive studies. This kind of interespecific relationship has not been yet reported for aquatic habitats.

Zoospores as Food Sources Zoospores may constitute an alternative resource for consumers (Niquil et al., 2011) and are probably involved in many different food chains

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(Gleason et al., 2008a). Zoospores are important for quick energy transfer between trophic levels while resting spores can serve as long-term storage of nutritional energy in the environment (sequential transfer of nutritional energy) (Neuhauser et al., 2011). Zoospores of true fungi have been shown to be consumed by the metazoan zooplankters (Kagami et al., 2004) and represent an interesting source of different energy-rich compounds (glycogen, proteins, sterols and fatty acids such as PUFAs) for the grazers (Suberkropp & Cantino, 1973; Weete et al., 1989; Gleason et al., 2008a). Consumers can obtain from zoospores some essential compounds that cannot be produced de novo (Bec et al., 2003, 2006) which are important somatic factors for their growth (Desvilettes & Bec, 2009). Thus, zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids upgrade the food sources available to grazers, a phenomenon which is called “trophic upgrading”. As an example, zoospores of the chytrid Zygorhizidium which parasites the inedible alga Asterionella (inedible because of its relatively large size) are eaten by the cladoceran zooplankter Daphnia and provide important nutrients for the cladoceran (Kagami et al., 2007) enhancing their growth. The parasitic fungi support a direct link between dead or living large algae (‘inedible algae’) and filter-feeding zooplankton (Kagami et al., 2004). This undescribed link which refers to the utilization of nutrients through HFs as high quality nutrient sources was termed “mycoloop” (Kagami et al., 2004; Kagami et al., 2007). In addition, changes in the chemical composition of the hosts produced by infection of parasitic zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids indirectly alter its edibility and consequently the organisms which feed on them (Niquil et al., 2011). A well documented example for such changes is Plasmodiophora brassicae, a plasmodiophorid parasite of brassicas. Upon infection an increase in glucose, sucrose, and starch and an altered compositon of amino acids, sterols and fatty acids occurs in the hosts (Ludwig-Müller et al., 2009; Neuhauser et al. 2011) making them more palatable for consumers.

Diversity The diversity of these organisms in many ecosystems is largely unknown. Up to date, approximately 1,988 species of zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids have been described (Kirk et al., 2008). Currently there are over 800 known species of Oomycota

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(Rossman & Palm, 2007) and approximately 1000 species of Chytridiomycota (Kirk et al., 2008). Considering that the estimated percentage of known species of Oomycota and Chytridiomycota is quite small (Mueller & Schmit, 2007), human activities and environmental change may lead to species losses before they have even been discovered. Therefore, taxonomic surveys need to be carried out globally, covering as many types of habitats as possible, since information is lacking for most countries and ecosystems. Overexploitation, water pollution and acidification, flow modification, destruction or degradation of habitats, invasion by exotic species (Dudgeon et al., 2006) and global warming, are all affecting fungal diversity in aquatic habitats worldwide.

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Diversity Associated with Environmental and Biological Factors The distribution of zoosporic true fungi and heterotrophic straminipiles as of many other microorganisms in aquatic habitats might be mostly associated with two kind of factors: (i) environmental: the presence and availability of substrates in the environment and physicochemical conditions of water, and (ii) biological: intra- and interspecific relationships, e.g. competition for nutrient sources, parasitism and grazing.

(i) Environmental Factors Temporal fluctuations (i.e. diurnal or seasonal) and spatial distribution (e.g. ecotones, microhabitat conditions, gradients along a stream) might affect the distribution and survival of zoosporic true fungi and heterotrophic straminipiles in aquatic ecosystems. The presence, distribution and availability of substrates are affected by those conditions as well. Also their distribution depends on the scale of analysis (macroscale vs. microscale). Thus, the relationship of these organisms with the environment is highly complex and it is usually difficult to interpret. Zoosporic true fungi and heterotrophic straminipiles are considered to be ruderals (Andrews, 1992) and thus, when nutrients are available their population sizes might rapidly increase and some species are able to complete their life cycles in 48 hs (Ward, 1939). When nutrient concentrations or availability of substrates (or hosts) are high, fluctuations of population sizes are observed as temporal increases in their abundance (Lozupone & Klein, 2002).

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There are only a few studies that analysed the relationship between the distribution of zoosporic true fungi and heterotrophic straminipiles and environmental factors in freshwater habitats (Marano et al., 2008b; 2011; Nascimento et al., 2010). Most of the studies that analysed the response of these organisms to temperature, osmotic potential, pH, oxygen concentrations and nutrient concentrations are laboratory-based (Gleason, 1968; Booth, 1971a; Booth & Barrett, 1976; Dick, 1976; Gleason, 1976; Natvig, 1982; Powell, 1993; Gleason et al., 2004, 2005, 2007, 2008b; Midgley et al., 2006; Kagami et al., 2007). Laboratory-based studies do not exactly predict what happens to fungi growing in natural habitats due to the interrelation of environmental and biotic factors. Biotic factors (e.g., competition, facilitation or parasitism) can restrict or enhance what is found to occur in the laboratory. Therefore, future studies that aimed at analysing the dynamics of zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids in their natural habitats are needed. Temperature, osmotic potential, pH and concentrations of dissolved oxygen and nutrients appeared to be the most important environmental factors that affect the occurrence and distribution of these organisms in natural habitats (Sparrow, 1968; Booth, 1971a; Mer et al., 1980; Misra, 1982; Khulbe & Bargava, 1983; Dubey et al., 1994; Johnson et al., 2002; Marano et al., 2008b, 2011; Nascimento et al., 2011). Temperature appeared to be inversely correlated to the distribution of zoosporic true fungi and heterotrophic straminipiles especially in the tropics, since they appeared to be scarcely represented when temperature increases (Mer et al., 1980; Gupta & Mehrotra, 1989; El-Hissy et al., 1992; Nascimento et al., 2011). Laboratory-based studies have shown that high temperatures might limit the growth of Chytridiomycota and Blastocladiomycota. Some aquatic species could not develop at temperatures higher than 23-25 °C (Longcore, 1993; Boyle et al., 2003; Johnson et al., 2003). Moderate temperatures (16-21 °C) appeared to be more favourable for their growth than higher or lower temperatures (El-Hissy et al., 1992; Mer et al., 1980). As an example, the colonization of baits (pieces of leaves of Phragmites australis) by Pythium phragmitis in the laboratory was significantly increased at 20 and 25 °C, as compared to 15 °C (Nechwatal et al., 2008). The maximum growth temperature for zoosporic true fungi isolated from soil is found within the range of 30-40 °C (Gleason et al., 2005) and no species was able to grow at temperatures higher than 45 °C (Booth, 1971b; Gleason et al., 2005).

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Zoospores in aquatic systems are subjected to wide diurnal and seasonal changes in pH conditions and because of that their survival and establishment on substrates might be highly influenced by these changes. Some authors (Lund, 1934; Dick, 1976; Johnson et al., 2002; Gleason et al., 2009) have suggested that the pH is another important factor that affects the distribution, growth, release of zoospores and production of sexual (oogonia, oospores and antheridia) and asexual structures (zoosporangia). For example, pH appeared to be a limiting factor for zoospore survival in species of Phytophthora (Kong et al., 2009). Tolerance to pH in zoosporic true fungi and heterotrophic straminipiles seem to be highly variable between species. Although some of these organisms are able to tolerate a wide range of pH (2.9-11.3) (Sparrow, 1968; Sparrow & Lange, 1977; Mullen et al., 2000; Letcher & Powell, 2001; Commandeur et al., 2005; Gleason et al., 2009), neutral or close to neutral conditions appeared to be most favourable (Dayal & Tandon, 1963; Suzuki, 1960; Khulbe, 1980). The relationship between the concentration of dissolved oxygen and the distribution of zoosporic fungi and heterotrophic straminipiles in freshwater habitats does not follow a unique pattern (Rattan et al., 1980). Members in the order Chytridiales, Monoblepharidales, Rhizophydiales and Spizellomycetales are mostly obligate aerobes and their growth rates are inhibited by low concentrations of dissolved oxygen (Gleason, 1976; Barr, 2001; Gleason et al., 2007). Some members of the Blastocladiales like Allomyces macrogynous and Blastocladiella emersonii are aerobes while others like Blastocladiella ramosa are facultative anaerobes with a fermentative metabolism (Natvig, 1982). A great number of species are not able to grow anaerobically, although many of them may be able to survive under such conditions for short periods of time (Gleason et al., 2007). Only a few facultative anaerobic species of heterotrophic straminipiles in four genera, Aqualinderella, Mindeniella, Rhipidium, and Sapromyces have been isolated from stagnant fresh waters (e.g. ponds) (Natvig, 1982) and tolerate low concentrations of dissolved oxygen (Gleason, 1968; Emerson & Natvig, 1981; Whisler, 1987). As an example, Apodachlya sp., Phytophthora cinnamomi and Pythium aphanidermatum are strictely aerobic whereas Aqualinderella fermentans is facultative anaerobic with a requirement for a high concentration of carbon dioxide (Natvig, 1982). Saprolegniales (Oomycota) are generally found in habitats where oxygen concentrations are not limiting. For example, El-Hissy et al. (1992) recorded a greater number of species in sites where the concentrations of dissolved oxygen were higher. Roberts (1963), Alabi (1971) and Misra (1982) observed that organic inputs

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were positively correlated with the distribution and abundance of oomycetes. Therefore, Suzuki & Tasuno (1965) concluded that since some Saprolegniales are found in stagnant waters with great amounts of organic matter they might be adapted to low concentrations of dissolved oxygen. Many human activities, e.g. the discharge of effluents from different sources (domestic and industrial) to freshwater ecosystems, the use of pesticides and fertilizers in agriculture or the deforestation of native riparian vegetation might affect the survival, frequency and abundance distribution of zoosporic true fungi and heterotrophic straminipiles. Harvey (1952), Cooke & Barstch (1959), Steciow (1988; 1993a,b; 1997 a, 1998) and Steciow & Arambarri (1991) isolated many species of zoosporic true fungi and heterotrophic straminipiles in aquatic habitats subjected to domestic and industrial effluents. How and in which ways do these substances affect the abundance and diversity of zoosporic true fungi and heterotrophic straminipiles in freshwater ecosystems? The answer to this question is not yet clarified and should be subject for future studies. Alifatic and polycyclic aromatic compounds are commonly found in nature as constituents of fossil fuels (Cerniglia et al., 1978, 1979) or as products or residues of some industrial activities. When these compounds enter a freshwater ecosystem indigenous microorganisms might react in three different ways: (i) by dying due to toxicity; (ii) by tolerating them for variable periods of time or; (ii) by using them as a source of carbon and energy which it is conditioned by oxygen, nitrogen and phosphorous concentrations, amount of organic matter available and temperature conditions, among other abiotic factors (Atlas & Bartha, 1973). The tolerance to aliphatic and aromatic compounds or their utilization by zoosporic true fungi and heterotrophic straminipiles has not been extensively studied under laboratory conditions. Leptomitus lacteus (Leptomitales, Oomycota) is called the“sewage fungus” because it occurs predominantly in waters heavily polluted by sewage and other municipal effluents. In addition, this species appears to occur in habitats with C14-C28 hydrocarbons (Steciow & Elíades, 2002). Saprolegnia parasitica and some Chytridiomycetes have been referred as able to degradate hydrocarbons (Cerniglia, 1978). In addition, some species of Achlya (A. americana, A. polyandra, A. prolifera) and Dictyuchus monosporus were tested for growth using aliphatic (heptane, gasoline, gas oil and kerosene) and aromatic compounds (benzene, xylene and toluene) as sole carbon sources. All of these species appeared to grow sparingly in gas oil and kerosene but presented a very slight growth in the aromatic compounds. D.

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monosporus, appeared not grow in gasoline and heptane (Steciow & Arambarri, 1991; Steciow, 1993b). Human activities might also increase the concentration of some heavy metals in freshwater. Heavy metals appeared to have a negative impact on the physiology of zoosporic true fungi and heterotrophic straminipiles. Parris & Baud (2004) demonstrated a negative interaction between copper and the amphibian pathogen Batrachochytrium dendrobatidis and hypothesized that copper may have decreased the growth of the fungus. Poléo et al. (2004) found that aluminum and zinc decreased the number of parasites in Atlantic salmon Salmo salar. On the other hand, de Souza et al. (2008) demonstrated the potential of isolates of Saprolegnia subterranea and Pythium torulosum for removing ions like copper, manganese and cadmium from diluted solutions in the laboratory. These studies have demonstrated the great adaptability of some species to pollution and show their potential application in the removal of toxic substances from soils. However, since a few studies have been yet carried out, further and more detail analyses are required before any generalization can be made. Eutrophication also appeared to affect the survival of zoosporic true fungi and heterotrophic straminipiles. Czeczuga & Muszyñska (2004) found that high concentrations of nitrate and sulphate limited the growth of certain species. For example, nitrate decrease zoospore production or kill zoospores in Saprolegnia spp. This impact is probably larger in freshwater environments in close vicinity to agricultural sites where often high amounts of N-fertilizers are used.

Temporal Fluctuations: Diurnal All lakes are subjected to diurnal fluctuations of temperature and in temperate regions to stratification. Therefore, changes in other abiotic factors (e.g. concentration of dissolved oxygen) that are dependent on temperature are expected to experience diurnal fluctuations. The pH is another factor that fluctuates during the day because of respiration of algae and aquatic plants. All of these changes might affect the distribution and survival of propagules (zoospores) in the water column. As an example, Suzuki & Hatakeyama (1960) and Suzuki (1961a,b) studied the vertical distribution of zoospores of Saprolegniaceae in Japanese lakes and found positive correlation between the distribution and the oxygen concentrations and continuous circulation of water. Unfortunately no further attempts have been made to estimate the

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number of zoospores during diurnal fluctuations of abiotic factors at different depths.

Temporal Fluctuations: Seasonal Many species have a seasonal periodicity associated with environmental conditions and nutrient availability (Hughes, 1962; Dick 1976; Klick & Tiffany, 1985). However, as stated by Wielgoss et al. (2010) “the identification of single factors responsible for changes in natural ecosystems is difficult, because seasonality is rather the net result of several complex interactions in the ecosystem than being based on distinct correlations with single environmental factors”. Even so, some general patterns of periodicity could be identified. In temperate regions many studies showed a higher number of taxa in fall and/or spring (e.g., Perrott, 1960; Roberts, 1963; Sparrow, 1968; Dick, 1976; Czeczuga, 1991a, b, 1996; Czeczuga & Proba, 1987) while others, in contrast, found a greater number of taxa in winter and/or summer (Waterhouse, 1942; Dayal & Tandon, 1962). Decreasing number of isolations of oomycete species have been frequently reported when water temperatures rises and water levels, pH values and oxygen concentrations are reduced during summer (Hallett & Dick, 1981; Misra, 1982; Ali-Shtayeh et al., 1986; Abdelzaher et al., 1995; Marano et al., 2008b; Wielgoss et al., 2009). Recent studies using clone libraries also showed that the number of species and diversity were significantly lower in summer samples, as compared with spring and autumn samples (Wielgoss et al., 2009). In tropical regions, seasonality is marked by dry and rainy seasons. Zoosporic true fungi and heterotrophic straminipiles are strongly dependent on the presence of water for zoospore dispersal. Thus, their occurrence and distribution appeared to be influenced by these seasonal conditions (Srivastava, 1967, Alabi, 1971a,b; Dick, 1976; Nascimento et al., 2011). Rainfall during rainy seasons might influence their distribution in aquatic systems (i) by decreasing zoospore density (“dilution”); and (ii) by introducing a new source of nutrients into the aquatic system through surface water runoff from terrestrial habitats. Results found in literature are often contradictory. Nascimento et al. (2011) found that the abundance (and not the species richness) of zoosporic true fungi and heterotrophic straminipiles was significantly greater in the dry season as compared to the rainy season. On the contrary, Paliwal & Sati (2009) recorded a greater diversity during the rainy season as compared to the dry season.

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Whether these contradictory results are due to different availabilities of nutrients or chemical substances triggering the germination of zoospores or simply due to sampling bias and/or patterns of spatial distribution is yet unknown. In addition, flooding events (both seasonal: e.g. after snow melt in alpine regions; or temporal e.g. after heavy rain), might greatly favour zoospore dispersion. Therefore flooding might affect the presence, distribution, capacity of colonization of substrates or infection of hosts by zoospores in aquatic habitats. In plant parasites, for example, infection appeared to be significantly induced when plants are completely submerged or parts of the plant are in contact with water during flooding (Nechawatal et al., 2008). Roots might experience temporal hypoxia or even anoxia during flooding, and thus because of this stress they might become more susceptible to infection by parasitic zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids.

Spatial Distribution In forested streams where light is a limiting factor for primary production, energy inputs are mainly allochtonous (i.e. leaf litter) and the metabolic processes are typically heterotrophic. There is a gradient of morphological, physical and consequently biological characteristics in a stream from headwaters down to lower reaches (River Continuum Concept: Vannote et al., 1980). Changes in the quantity of leaf litter inputs and in the velocity of water flow are more evident in headwater-forested streams. Along this gradient allochthonous inputs become less important and the aquatic communities tend to concentrate more on autochthonous processing of nutrients transported downstream. Those changes might influence the presence and amount of substrates available for colonization by zoosporic true fungi and heterotrophic straminipiles and thus, their distribution along the stream. Similar considerations might be extrapolated to lakes. The amount of available substrates is generally higher at coastal areas near the shore where the influence of the riverine vegetation and drainage are still important as compared to open waters toward the centre of the lake. Dick (1970) investigated the colonization of insect exuviae by Saprolegniales in mud from the margin, the littoral zone and sites beyond one meter from the shore line. He observed not only an abrupt fall off in the colonization of exuviae from sites beyond one meter from the shore but also a different species composition. The patterns of distribution of zoosporic true fungi in lakes appeared to be similar (Willoughby, 1961).

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As previously mentioned, studies in freshwater ecosystems are highly dependent on the scale of analysis, since they offer a diversity of microhabitats for aquatic fungi that should be considered when analysing their spatial distribution at microscales. An ecotone is a transitional area between two adjacent but different habitats mainly distinguished on a physiognomic basis (Clements, 1905). The ecotone between terrestrial and aquatic habitats (coastal areas) might contain species that are common to both communities but also include a number of highly adaptable species that tend to colonize such transitional areas. As a consequence an “edge effect” along the boundary line can be observed (especially in abrupt ecotones), with the ecotone displaying a greater diversity of species (Hansen et al., 1992; Risser, 1995). A transitional gradient of environmental factors and possibly a continuum of assemblages between habitats (ecocline sensu Whittaker, 1960) are observed when analysing ecotones at small-scales. Thus, it is highly important that data on species richness and their distribution of abundances as well as environmental factors along the gradient are obtained when studying transitional areas. Yet, there has been no attempt to fit data on zoosporic true fungi and heterotrophic straminipiles assemblages to the boundary models of ecotones-ecoclines.

(ii) Biological Factors Several studies have focused on the interaction between fungi and bacteria in biofilms (leaves) (e.g. Møller et al., 1999; Gulis & Suberkropp, 2003a). Nevertheless, there are no studies that aimed at analysing interspecific relationships (competition, facilitation, grazing, mutualism and parasitism) between species of zoosporic true fungi and heterotrophic straminipiles and other microorganisms such as bacteria isolated from freshwater. However, a few studies have speculated about competitive interactions between different species or between zoosporic true fungi and heterotrophic straminipiles and other microorganisms. Here we will only consider competention and facilitation among interespecific relationships since mutualism, parasitism and grazing have been discussed previously in this chapter (see Role in food webs). Competition might take place by means of chemical warfare or rapid colonization of substrates (i.e. finding and occupying the substrates first). A successful competitor has the ability to become dominant by adopting a particular ecological strategy when exploiting identical resources under the same conditions (i.e., the same ecological niche) as the other/s competitors. Different ecological strategies (competitive, ruderal and stress-tolerant) may

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be adopted under different environmental conditions, against different competitors, or during different stages in the life cycle of a fungus (Boddy & Wimpenny, 1992; Dix & Webster, 1995). Zoosporic true fungi and heterotrophic straminipiles are considered to be ruderals (Newton, 1971; Barlocher & Kendrick, 1974; Pugh, 1980; Manerkar et al., 2008; Barlocher, 2009; Marano et al., 2011). Usually ruderals are characterized by a short growth phase with high reproductive potential that enable them to quickly colonize ephemeral substrates and complete their life cycle in a short period of time (Dix & Webster, 1995). Zoospores have finite endogenous energy reserves and they do not survive for long or at high densities in stable, low nutrient, highly competitive substrates. They would have to either encyst or attach to an appropriate substrate quickly (Gleason & Lilje, 2009). Zoospore chemotaxis is implicated in finding, recognizing, accumulating on and then attaching to usable substrates. The dynamics of initial colonization of a substrate is primarily mediated by the time taken for fungal zoospores to locate, attach and encyst on it. Species which produce larger numbers of zoospores would be expected to colonize a larger number of substrates. However, zoospores of different species might reach the substrate simultaneously and must compete for space on the surface of the bait along with other microorganisms (e.g. bacteria). For example, Newell & Fell (1997) has speculated that rapid colonization of fallen leaves by halophytophtoras is a means of coping with competition from microbial films on the leaves (including protozoa and labyrinthulids that are potential predators upon halophytophthoras). Halophytophthoran capacity for rapid attachment and entry into new-fallen leaves (Newell et al., 1987) may have evolved as a mechanism for successful competition with bacterial assemblages. This subject has been largely under-explored in freshwater ecosystems. Under suitable environmental conditions, the asexual life cycle is completed rapidly resulting in the release of a large number of zoospores into water (Sparrow, 1960). Thus, rapid changes in population sizes may result in a sudden increase in the population (Sparrow, 1960; Gleason & Macarthur, 2008) which was referred as “chytrid epidemics” by Sparrow (1960). Some zoosporic true fungi might escape competition by inhabiting sites which are sub-optimal and hostile for other decomposers (Lee, 2000). Tolerance to stressful conditions may select for species that respond rapidly to changing environmental conditions in stressful habitats, leading to the dominance of species with ruderal attributes (Letcher et al., 2004). The increase in their abundance or density under stress might be related to the

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reduction in the number of competitors. The survival of zoosporic true fungi in extreme or stressful conditions has been recently reviewed by Gleason et al. (2010). Willoughby (1983a,b) studied the interaction between Rhizophlyctis rosea and bacteria on cellulosic baits and suggested that chemical warfare between zoosporic fungi and other microorganisms might take place and appeared to be an important determinant of population levels. The growth of two-member cultures of Phlyctochytrium sp. (Chytridiomycota) and Thraustochytrium striatum (Labyrinthulomycota) was tested in the laboratory at various salinities. In mixed cultures, even with T. striatum initially dominant in number, Phlyctochytrium sp. dominantes over time in low to intermediate salinities. Thus, these two organisms appeared to show competitive exclusion based on salinity when inhabiting the same environment (Amon & Yei, 1982). Natural microbiota from the skin of amphibians appeared to prevent the disease caused by Batrachochytrium dendobatidis. Competition by means of chemical warfare has been observed between bacteria and B. dendrobatidis. Brucker et al. (2008a,b) and Harris et al. (2009) observed that Lysobacter gummosus and Janthinobacterium lividum both isolated from the skin of the red-backed salamander produced 2,4-diacetylphloroglucinol and violacein respectively, two anti-fungal metabolites which inhibited the growth of B. dendrobatidis in the laboratory. We predict that facilitation mechanisms by zoosporic true fungi and heterotrophic straminipiles might operate in the same way as in the case of other aquatic fungi (e.g. mitosporic fungi). For example, colonization of newly fallen leaves by these organisms might alter the food quality and palatability of leaves for detritivorous in the same way mitosporic fungi do. Thus, detritivorous would selectively feed on previously colonized leaves since it has been demostrated that a diet rich in fungi stimulate detritivorous´ growth and fecundity (Graça, 1993). Preliminary results on facilitation of growth of zoosporic true fungi mediated by other microorganisms on natural substrates have been reported. Although Spizellomyces sp. (AVM1) was isolated from soil on filter paper, this isolate could not digest filter paper in pure culture (Gleason et al., 2011). Couch (1939) also found a number of species that could grow on filter paper in mixed cultures but not in pure culture on this substrate. This subject needs clarification and further analysis.

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CONCLUSION There are many gaps in our knowledge on zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids from freshwater ecosystems. Particularly, the ecology of these groups remains relatively neglected and studies currently available provide us with an incomplete view of their roles in food webs. Many aspects such as the selectivity for particular substrates, the effect of environmental and biological factors on species composition, abundance and diversity of assemblages are not yet been elucidated. Thus, ecological studies constitute a potentially significant new area of research for limnologists. Most biodiversity studies probably reflect only the distribution of specialists and not the complete distribution of taxa. There is also a great need for diversity studies to be implemented on a global scale. Today there are only a few mycologists studying zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids worldwide, but especially in many tropical and subtropical countries there are no groups currently engaged in active research. There are extensive areas on the surface of this planet which have not yet been studied and which are rapidly being degraded by agriculture, deforestation, pollution and other anthropogenic actions that affect their biodiversity (Tsui et al., 1998) even before surveys can be undertaken by mycologists and protistologists. Loss in biodiversity is accelerating at a rapid pace and time is running out. Some of the species which are lost before they are even studied might play a crucial role in the functioning of certain ecosystems. A few ecological projects are already under way, but it is also important that an increasing number of studies be initiated. During the past decade, PCR-based molecular methods and DNA sequencing have been commonly used to identify zoosporic true fungi and heterotrophic straminipiles, providing detailed insights into the taxonomy of most groups. Molecular methods offer also exciting prospects for elucidation of the processes that structure the assemblages of zoosporic true fungi, heterotrophic straminipiles and plasmodiophorids in freshwater. Future efforts in this area will advance our general perspective on the role of these microorganisms in the environment. Considerable attention, further research and training of professionals in techniques of collection, isolation and identification of taxa, including molecular and ultrastructural analysis are needed, particularly in regions where these organisms have been largely under-sampled.

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REFERENCES Abdelzaher, HMA; Shoulkany, MA; Yasser, MM. Effect of benomyl and metalaxyl on reproduction of the plant parasite (Pythium deliense) and the mycoparasite (P. oligandrum). Archives of Phytopathology and Plant Protection, 2004, 37, 307-317. Adams, MJ. Transmission of plant viruses by fungi. Annals of Applied Biology, 1991, 118, 479-492. Adams, GC; Catal, M; Trummer, L. Distribution and Severity of Alder Phytophthora in Alaska. Proceedings of the Sudden Oak Death Fourth Science Symposium, 2009, 29-49. Adl, SM; Simpson, AGB; Farmer, MA; Andersen, RA; Anderson, OR; Barta, JR; Bowser, SS; Brugerolle. G; Fensome. RA; Fredericq, S; James TY; Karpov, S; Kugrens, P; Krug, J; Lane, CE; Lewis, LA; Lodge J; Lynn, DH; Mann, DG; Mccourt, RM; Mendoza, L; Moestrup, Ø; MozleyStandridge, SE; Nerad, TA; Shearer, CA; Smirnov, AV. The new higher level classification of Eukaryotes with emphasis on the taxonomy of protists. Journal of Eukaryotic Microbiology, 2005, 52, 399-451. Ainsworth, GC. Introduction and keys to higher taxa. IVB, chap.1. In: Ainsworth, GC; Sparrow, FK; Sussman, AS (eds.). The Fungi: an advanced treatise. New York: Academic Press Inc.; 1973; 1-7. Alabi, RO. Factors affecting seasonal occurrence of Saprolegniaceae in Nigeria. Transactions of the British Mycological Society, 1971a, 56, 289299. Alabi, RO. Seasonal periodicity of Saprolegniaceae at Ibadan, Nigeria. Transactions of the British Mycological Society, 1971b, 56, 289-299. Alberts, VA; Khan, SS; Lim, DV; Te Strake, D. 1989. Extracellular enzyme activity of some Saprolegniales from a Florida estuary. Mycologia, 1989, 81, 460-463. Alexopoulos, CJ; Mims, CW; Blackwell, MB. Introductory Mycology. 4th edition. New York: John Wiley, Sons, Inc.; 1996. Ali-Shtayeh, MS; Lim-Ho, CL; Dick, MW. An improved method and medium for quantitative estimates of populations of Pythium species from soil. Transactions of British Mycological Society, 1986, 86, 39-47. Amon, JP; Yei, SP. The effect of salinity on the growth of two marine fungi in mixed culture. Mycologia, 1982, 74, 117-122. Ananda, K.; Sridhar, KR; Raviraja, NS; Bärlocher, F. Breakdown of fresh and dried Rhizophora mucronata leaves in a mangrove of Southwest India. Wetlands. Ecological Management, 2008, 16: 1-9.

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Andrews, JH. Fungal life-history strategies. In: Carroll, CG; Wicklow, DT (eds.). The Fungal Community. New York: Marcel Dekker; 1992; 119145. Archibald, JM; Keeling, PJ. Actin and ubiquitin protein sequences support a cercozoan/foraminiferan ancestry for the plasmodiophorid plant pathogens. Journal of Eukaryotic Microbiology, 2004, 51, 113-118. Atlas, RM; Bartha, R. Microbial ecology: Fundamentals and Applications. 4th edition. New York: The Benjamin/Cummings Publishing Compañy; 1998. Baldauf, SL; Roger, AJ; Wenk-Siefert, I; Doolittle, WF. A kingdom-level phylogeny of eukaryotes based on combined protein data. Science, 2000, 290, 972-977. Baldauf, SL .The deep roots of Eukaryotes. Science, 2003, 300, 1703-170. Bärlocher, F. The ecology of aquatic hyphomycetes. Berlin: Springer-Verlag; 1992. Bärlocher, F. Reproduction and dispersal in aquatic hyphomycetes. Mycoscience, 2009, 50, 3-8. Bärlocher, F; Kendrick, B. Dynamics of the fungal population on leaves in a stream. Journal of Ecology, 1974, 62, 761-791. Barr, DJS. The zoosporic groupings of plant pathogens. Entity or non-entity? In: Buckzacki, ST (ed.). Zoosporic Plant Pathogens. A modern perspective. London: Academic Press; 1983; 43-83. Barr, DJS. Phylum Chytridiomycota. In: Margulis, L; Corliss, JO; Melkonian, M; Chapman, DJ (eds). Handbook of Protoctista. Boston: Jones & Bartlett Publishers; 1990; 454-466. Barr, DJS. Evolution and kingdoms of organisms from the perspective of a mycologist. Mycologia, 1992, 84, 1-11. Barr, DJS. Chytridiomycota. In: McLaughlin, DJE; McLaughlin, G; Lemke, PA (eds.). The Mycota, Vol.VII. Part A. Systematics and Evolution. New York: Springer-Verlag; 2001; 93-112. Barrionuevo, JS; Aguayo, R; Lavilla, EO. First record of chytridiomycosis in Bolivia (Rhinella quechua; Anura: Bufonidae). Diseases of Aquatic Organisms, 2008, 82, 161-163. Barron, GL. Fungal parasites and predators of rotifers, nematodes, and other invertebrates. In: Mueller, GM; Bills, GF; Foster, MS (eds.). Biodiversity of fungi: Inventory and monitoring methods. Amsterdam: Elsevier Academic Press; 2004; 435-450.

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Steciow, MM. The occurrence of Achlya recurva (Saprolegniales, Oomycetes) in hydrocarbon–polluted soil from Argentina. Revista Iberoamericana de Micología, 1997a, 14, 135-137. Steciow, MM. Análisis cuali-cuantitativo de los hongos zoospóricos de Laguna Vitel y tributarios (Chascomús, Argentina). Boletín Micológico, 1997b, 12, 49-53. Steciow, MM. Abundancia y frecuencia relativa de los Oomycetes en Río Santiago y afluentes (Buenos Aires, Argentina). Gayana Botánica, 1997c, 54, 39-52. Steciow, MM. Hongos acuáticos (Chytridiomycota, Oomycota) de la Laguna Vitel y tributarios (Buenos Aires, Argentina). Darwiniana, 1998, 36, 101-106. Steciow, MM. A new freshwater species of Achlya from Tierra del Fuego Province, Argentina. New Zealand Journal of Botany, 2001a, 39, 277283. Steciow, MM. Achlya fuegiana, a new species from Tierra del Fuego Province (Argentina). Mycologia, 2001b, 93, 1195-1199. Steciow, M M. Saprolegnia longicaulis (Saprolegniales, Straminipila), a new species from an Argentine stream. New Zealand Journal of Botany, 2001c, 39, 483-488. Steciow, MM. Saprolegnia milnae (Saprolegniales, Straminipila), a new species from an Argentine river (Tierra del Fuego province, Argentina). New Zealand Journal of Botany, 2002, 40, 473-479. Steciow, MM. A new species of Brevilegnia (Saprolegniales, Straminipila) from Buenos Aires Province, Argentina. Mycologia, 2003a, 95, 934-942. Steciow, MM. Saprolegnia oliviae sp. nov. isolated from an Argentine river (Tierra del Fuego Province, Argentina). FEMS Microbiology Letters, 2003b, 219, 253-259. Steciow, MM; Arambarri, AM. Utilización de hidrocarburos por Oomycetes. I. Achlya polyandra. Boletín Micológico, 1991, 6, 33–35. Steciow, MM; Eliades, LA. Primer registro de Allomyces neomoniliformis (Chytridiomycota) y Dictyuchus missouriensis (Oomycota) aislados de un suelo agrícola (Buenos Aires, Argentina). Darwiniana, 2001, 39, 1518. Steciow, MM; Elíades, LA. Thraustotheca terrestris a new species from Argentine agricultural soil. Nova Hedwigia, 2002a, 75, 227-235. Steciow, MM; Elíades, L A. A. robusta sp. nov., a new species of Achlya (Saprolegniales, Straminipila) from a polluted Argentine channel. Microbiology Research, 2002b, 157, 177-182.

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Steciow, MM; Elíades, LA. A new species of Saprolegnia (Saprolegniales, Straminipila), from a polluted Argentine channel. New Zealand Journal of Botany, 2002c, 40, 679-685. Steciow, MM; Elíades, LA. Mohos acuáticos con metabolismo fermentativo en aguas contaminadas del Partido de Ensenada (Buenos Aires, Argentina). Boletín de la Sociedad Argentina de Botánica, 2002d, 37, 511. Steciow, MM; Elíades, LA; Arambarri, AM. Nuevas citas de Blastocladiales (Chytridiomycota) en ambientes contaminados de Ensenada (Buenos Aires, Argentina). Darwiniana, 2001a, 39, 15-21. Steciow, MM; Elíades, LA; Arambarri, AM. El género Gonapodya (Monoblepharidales, Chytridiomycota) en ambientes contaminados de Ensenada (Buenos Aires, Argentina). Boletín de la Sociedad Argentina de Botánica, 2001b, 36, 203-208. Steciow, MM; Lopez Lastra, CC; Dick, M. Scoliolegnia hypogyna (Saprolegniales, Oomycetes), a new species from Misiones Province, Argentina. Mycotaxon, 2005, 91, 381-391. Steciow, MM; Marano, AV. Blastocladia bonaerensis (Chytridiomycetes, Blastocladiales), a new species from Argentine polluted channel. Mycotaxon, 2006, 97, 359-365. Steciow, M M; Marano, AV. Achlya anomala (Saprolegniales, Straminipila), a new species from an argentine stream. Botanica Lithuanica, 2008, 14, 49-56. Steciow, MM; Paul, B. Saprolegnia bulbosa, a new species from an Argentine stream (Buenos Aires Province, Argentina), its taxonomy, ITS region of rDNA, and comparison with related species. FEMS Microbiology Letters, 2007, 268, 225-230. Stoeck, T; Zuendorf, A; Breiner, HW; Behnke, A. A Molecular approach to identify active microbes in environmental eukaryote clone libraries. Microbial Ecology, 2007, 53, 328-339. Subberkropp, KF; Cantino, EC. Utilization of endogenous reserves by swimming zoospores of Blastocladiella emersonii. Archiv für Mickrobiologie, 1973, 89, 205-221. Sutton, JC; Sopher, CR; Owen-Going, TN; Liu, W; Grodzinsk, B; Hall, JC; Benchimol, RL. Etiology and epidemiology of Pythium root rot in hydroponic crops: Current knowledge and perspectives. Summa Phytopathologica, 2006, 32, 307-321. Suzuki, S. Seasonal variation of aquatic Phycomycetes in lake Nakanuma. Japanese Journal of Ecology, 1960, 10, 215-218.

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Suzuki, S. The vertical distribution of the zoospores of aquatic fungi during the circulation and stagnation period. Botanical Magazine,1961a, 74, 254-258. Suzuki, S. The seasonal changes of aquatic fungi in the lake bottom of lake Nakanuma. Botanical Magazine, 1961b, 74, 30-33. Suzuki, S; Hatakeyama, T. Ecological studies on the aquatic fungi in the Shiga Lake group. Japan Journal of Limnology, 1960, 21, 64-72. Susuki, S; Tasuno, T. A study concerning the pollution of rivers and their purification, with microorganisms as indicators. Part I. The current analysis of the river pollution. Journal of Hygienic Chemistry, 1965, 11, 6-33. Tilman D. 1999. The Ecological Consequences of Changes in Biodiversity: A Search for General Principles Ecology, 80: 1455-1474. Tomlinson, J A; Faithfull, E M. Effects of fungicides and surfactants on the zoospores of Olpidium brassicae. Annals of Applied Biology, 1979, 93, 13-19. Thompstone, A; Dix, NJ. Cellulase activity in the Saprolegniaceae. Transactions of the British Mycological Society, 1985, 85, 361-366. Tsui, KM; Fryar, SC; Hodgkiss, U; Hyde, KD; Poonyth, AD; Taylor, LE. The effect of human disturbance on fungal diversity in the tropics. Fungal Diversity, 1998, 1, 19-26. Van der Auwera, G; de Baere, R; Van de Peer, Y; de Rijk, P; Van den Broeck, I; de Wachter, R. The phylogeny of the Hyphochytriomycota as deduced from ribosomal RNA sequences of Hyphochytrium catenoides. Molecular Biology and Evolution, 1995, 12, 671-678. Van West, P. Saprolegnia parasitica, an oomycete pathogen with a fishy appetite; new challenges for an old problem. Mycologist, 2006, 20, 99104. Vannote, RL; Minshall, GW; Cummins, KW; Sedell, JR; Cushing, E. The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences, 1980, 37, 130-137. Velez, CG; Letcher, PM; Schultz, S; Powell, MJ; Churchill, PF. Molecular phylogenetic and zoospore ultrastructural analyses of Chytridium olla establish the limits of a monophyletic Chytridiales. Mycologia, 2011, 103, 118-130. Vivar Muñoz, V; Bernal, FL. Control de saprolégniales por acido acético, cloruro de sodio y verde malaquita en huevos de trucha arcoiris. Boletín Micológico, 1998, 13, 29-34.

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Willoughby, LG. Decomposition of litter in freshwaters. In: Dickinson, CH; Pugh, GTF. (eds). Biology of Plant Litter Decomposition. London: Academic Press, 1974, 659-681. Willoughby, LG. Diseases of freshwater fishes. In: Tsui, CKM; Hyde KD (eds.). Freshwater Mycology. China: Fungal Diversity Press, 2003, 111126. Willoughby, LG. The bacterial antagonist of Karlingia rosea: further observation from Spain and Cyprus. Nova Hedwigia, 1983a, 38, 113127. Willoughby, LG. The bacterial antagonist of Karlingia rosea: observations from mainland Spain. Transactions of the British Mycological Society, 1983b, 81, 435-438. Willoughby, LG; Redhead, K. Observations on the utilization of soluble nitrogen by aquatic fungi in nature. Transactions of the British Mycological Society, 1973, 60, 598-601. Witkamp, M; van der Drift, J. Breakdown of forest litter in relation to environmental factors. Plant Soil, 1961, 15, 595-311. Wood, CL; Byers, JE; Cottingham, KL; Altman, I; Donahue, MJ; Blakeslee, AMH. Parasites alter community structure. Proceedings of the National Academy of Sciences, 2007, 104, 9335-9339. Wood-Eggenschwiler, S; Bärlocher, F. Geographical distribution of Ingoldian fungi. Verhandlungen der Internationalen Vereinigung für Theoretische und Angewandte Limnologie, 1985, 22, 2780-278. Zaror, L; Collado, L; Bohle, H; Landskron, E; Montaña, J; Avendaño, F. Saprolegnia parasitica in salmon and trout from southern Chile. Archivos de Medicina Veterinaria, 2004, 36, 71-78.

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Chapter 2

AQUATIC ECOSYSTEM HEALTH: A REVIEW Surjya Kumar Saikia, Santanu Ray*, Joyita Mukherjee and Madhumita Roy

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Ecological Modelling Laboratory, Department of Zoology, Visva-Bharati University, Santiniketan, India

ABSTRACT Ecosystem health is a symbol for a complex set of ecological realities, rather than a condition that can be measured or monitored directly. It is an idea comprising a class of phenomena. Ecosystem health describes the preferred state of sites modified by human activity. The science of ecology has been primarily to the non-cultural elements of ecosystem health, and has focused on such concepts as species distribution and abundance, the structure, stability, and productivity of ecosystems, and the ability of ecosystems to self organize and evolve. Ecosystem health clearly refers to an ecosystem state, where the identity of the system and the integration of its elements, substantially intact and undamaged. It is not expected to observe ecosystem health directly rather many indices are used to measure the ecosystem health. This chapter describes a number of principles that may contribute to the practical pursuit of ecosystem health as a goal in science based management of natural resources of aquatic ecosystem. Many health *

Corresponding author: email: [email protected]

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Surjya Kumar Saikia, Santanu Ray, Joyita Mukherjee et al. indices such as exergy, ascendancy and emergy and their applications to measure the ecosystem organization towards its health are also described.

BACKGROUND

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The concept of ecosystem health can be traced back to the early 1940s. In 1941, Leopold first defined land health by identifying indicators of land sickness. Meanwhile, he used land sickness to describe land dysfunction. In succession, Leopold stated: a thing is right when it tends to preserve the integrity, stability, and beauty of the biotic community. It is wrong when it tends otherwise. Barrett and Rosenberg (1981), Odum (1985), Rapport (1985) considered the definition of ecosystem health is related to stress ecology. An ecosystem will continue to degrade under the pressure of increasing demands unless humans apply preventative and restorative strategies to achieve the health and integrity of regional ecosystems. On the basis of Leopold’s original indicators, Rapport et al. (1989) brought forward what he called ecosystem distress syndrome (EDS).

CONCEPT AND DEFINITION ‘Ecosystem health”, a concept recently popularised as the way forward in evaluating nature. The concept was first enunciated by the pioneering ecologist Aldo Leopold (1939) as a means to elucidating the condition of ecosystems. Rapport (1989) proposed the concept of ecosystem health for the first time. Though ‘health’ and ‘integrity’ looks synonymous, Karr and Chu (1999), reflect a common position that concepts of ecosystem health and integrity are fundamentally different. Steedman (1994) used the word ecological integrity to refer to the condition of ecosystems with little or no influence from human actions. Karr and Chu (1999), define ecosystem health as the preferred state of ecosystems modified by human activity (e.g. lake environments, farm land, urban environments, airports, managed forests). In contrast, ecological integrity is defined as an unimpaired condition in which ecosystems show little or no influence from human actions. Ecosystems with a high degree of integrity are natural, pristine, and often labelled as the base line or benchmark condition. Natural ecosystems would continue to function in essentially the same way if humans were removed. Karr et al. (1986) stated

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that a biological system can be considered healthy when its inherent potential is realized, its condition is stable, its capacity for self repair when perturbed is preserved, and minimal external support for management is needed. Schaefferetal (1988) and Haskell et al. (1992) suggested using a human and animal health assessment model as a guide for ecosystem health assessment. Rapport (1992) notes that defining health as the absence of disease focuses on disabilities, while defining health as the potential to recover after perturbation focuses on capability. On the basis of combinatorial, Haskell et al. (1992), Woodley et al. (1993) and Sampson et al. (1994) stated that an ecosystem is healthy (or has integrity) if it is stable and sustainable; that is to say, if it is active and maintains its organization and autonomy over time and is resilient to stress while providing for human needs. Today the concept is being hailed as the way forward in evaluating nature and its management for conservation and resource use purposes. In general, a healthy ecosystem in economic terms provides the necessary “factors of production” needed for human health and basic life-supports required for development (Folke, 1999). Costanza (1992) thought that ecosystem health could be defined in terms of system organization, resilience and vigour, as well as the absence of sign of ecosystem distress. He summarized the wide variety of proposed conceptive definitions of ecosystem health based on ecosystem distress syndrome (EDS): Health as homeostasis; as absence of disease; as diversity or complexity; as stability or resilience; as vigour or scope for growth; and as balance between system components. From this conception it is clear that ecosystem health evaluation should contain system resilience, balance capability, organization (diversity) and vigour (metabolism). Costanza (1998) further defined it as “Systems are healthy if they can absorb stress and use it creatively, rather than simply resisting it and maintaining their former configurations”. Thus, a healthy ecosystem is defined as being stable and sustainable, maintaining its organization and autonomy over time and its resilience to stress (Costanza, 1992, 1998). To be healthy and sustainable, the system must maintain its metabolic activity level, maintain its internal structure and organization, and be resilient to outside stresses. Input and product of healthy ecosystem is shown in Figure 1.

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Figure1. Input and product of healthy ecosystem.

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WHAT CAN WE EXPECT FROM THIS CONCEPT OF ECO- SYSTEM HEALTH? 1) We can expect to convey a context for the interpretation of observations. Ecosystem health clearly refers to an ecosystem state where the identity of the system, and the integration of its elements, is substantially intact and un-damaged. It does not refer to a damaged eco-system where the components are no longer integrated and interdependent. 2) We can expect the concept of ecosystem health to suggest simple metaphors and analogies for complex realities. Some aspects of ecosystem health can be understood in terms of organism health. Using this perspective, indicators or surrogate measures of ecosystem health are analogous to body temperature, blood pressure, or blood chemistry (Haskell et al. 1992). 3) We can use the concept of ecosystem health to communicate useful generalizations about our goals for resource management. The people who coined terms like "biological integrity", and who incorporated them into legislation and international agreements, chose those words to convey a generalized idea of environmental health and well-being. The concept of ecosystem health can also be useful to convey ideas, rather than facts.

ECOSYSTEM HEALTH AND ECOLOGICAL POLICY Traditional approaches to implementing ecological policy typically follow the “command and control” (“promulgate and policy”) paradigm (Carnegie Commission on Science, Technology, and Government, 1990). With the command and control approach, a narrow (e.g., water, air, chemical, or effluent), technically based standard is promulgated as a surrogate for a larger, often nebulous ecological or public health policy goal. Command and control approaches to implementing ecological policy tend to be reductionist, thereby limiting the kinds of policy problems that can be addressed effectively. Attempts to correct one environmental problem sometimes create or exacerbate others. The command and control approach fits reasonably well for comparatively narrow policy problems (e.g., water quality and air quality), but does not mesh well with complex policy problems such as the

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consequences of land-use changes, maintenance of biological diversity, or the impacts of the introduction of exotic species. Another criticism of the command and control approach is its tendency to polarize the public and rouse strong opposition to the proposed policy or regulation. The very nature of the command and control approach engenders centralized decisionmaking, top-down policy-making, and public resistance. Lack of public support is understandable, even predictable, because ecological issues and socioeconomic issues are intertwined. There are winners and losers in policy choices, so the prospect of authentic win-win solutions is illusory. Even so, many perceive that command and control approaches in implementing ecological policy create excessive societal strife. Other critics assert that command and control approaches do not effectively use new scientific and technical information. Current understanding of the functioning of ecosystems, for example, has moved away from the assumption that the natural or climax condition of an ecosystem is fairly predictable (e.g., the old “balance of nature” idea, De Leo and Levin 1997). Current thinking is that the state of ecosystems is less circumscribed (e.g., “chaotic” events are often decisive). Although rarely explicitly stated, much of the command and control approach to implementing ecological policy has been predicated, in part, on the “balance of nature” world view. As efficient and alternative measure, Ecosystem health is the most popular of the emerging modifications of command and control (Gaudet et al. 1997, Belaoussoff and Kevan 1998; Rapport et al. 1998). Many of the popular alternatives and modifications to command and control (e.g., ecosystem management and ecosystem sustainability) have notions of ecosystem health at their core (Lackey 1998). Ecosystem health treats the ecosystem as the unit of policy concern, not the individual animal or plant (Schaeffer et al., 1988). Concerns about individual animals or plants— the typical focus of “animal rights” and “animal welfare” policy—are usually not the level at which ecological policy is debated.

ON THE PRETEXT OF ECOSYSTEM HEALTH UNDERSTANDING ECOSYSTEM CONCEPT Through ecosystem health first it has to be determined which system is assessed. It is not that ecosystem we go through fundamental to generalisation. For maintaining of good health it can follow different means of self regulation and control different from classical ecosystems. Direct

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Exchange of goods and services to human society are to be associated with such ecosystems. For assessment of ecosystem health it is needed to examine the performance of such ecosystems, the coherent, clear and quantifiable definitions of iterative ecosystem must be crafted so that the concept can be delineated operational. For delineating the concept of ecosystem health, the models of iterative ecosystems are described below.

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1. Model of Succession-to-Climax “Process-functionalist” approach – one systems perspective is developed in ecology (Allen & Hoekstra, 1992), which deals, organisms, species and their physical environments as “integral bio-geo-chemical energetic systems” in the ecosystem (Lotka, 1925). Thus, in this approach it states that the units of selection in ecosystems are cycles of energy and material flow that have different auto-catalytic properties (Odum, 1969; Depew & Weber, 1996). Lotka (1925) formulated the “maximum power principle” by considering this perspective of the underlying mechanisms of ecosystems. In essence, this hypothesis argues that when organisms are more efficient in their energy utilisation, and species are favoured, if they enter into cooperative interactions with other species, allowing the ecosystem as a whole to maximise the through flow of “useful” energy, they tend to be selected by these energetic systems (ecosystems). Consequently, the whole system is likely to increase its energy flows and material cycle rates (metabolism) through the system with the appropriate selection of organisms and species, which it is argued will increase total system biomass and the overall health of the ecosystem. On the basis of the theory of ecological succession, a theory which ultimately leads to a stable climax phase (Odum, 1969), the behaviour and development of an ecosystem of this process-functionalist approach has been modelled. Accordingly, the aptly named succession-to-climax model presupposes that a highly ordered successional sequence of biotic communities dominated by small fast growing species referred to as rstrategists, dynamically converge in a linear manner towards a sustained and often predictable climax assemblage of K-strategist species, species that are large but slow growing (Clements, 1916; Tansley, 1920; Odum, 1969). Assumption of a relatively steady growth, with stabilising forces along a trajectory to maintain productivity and towards a sustained single steady state or equilibrium of the system is proposed from it. Hence, the property of

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global stability is presumed implicitly in presuming the existence of a single stable equilibrium point and climax phase (Figure 2a and 2b). It is also common that the occurrence of ecological disturbances is inevitable, which have negative effect on the development of an ecosystem attaining its steady state.

Figure 2. (a) Diagrammatic representation in discrete time of a dynamic linear model, describing the development and behaviour of an ecosystem which is used in succession-to-climax model. (b) So-called ball-and-cup diagram (see DeAngelis & Waterhouse, 1987) illustrating that with a linear model, the system (ball) converges towards a single equilibrium.

It is also common that the occurrence of ecological disturbances is inevitable, which have negative effect on the development of an ecosystem attaining its steady state. However, the succession-to-climax model presumes that a disturbance will only take an ecosystem back to a previous successional phase, whereby the development towards the climax phase continues to persist predictably again after the disturbance. Thus, the model predicts that an ecosystem will always move towards its climax phase regardless of how far it is displaced from this phase after a disturbance event. As such, the health of an ecosystem according to the succession-to-climax model can be modelled as if it were a homeostatic system, because homeostasis assumes fundamentally that ecosystems exhibit a unique equilibrium, which can self-perpetuate

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generation after generation by negative feedback (Odum, 1969; Oechel et al., 1994; Kay & Regier, 2000). When homeostatic system is defined in terms of ecosystem health it has intuitive appeal as it interprets health in a similar manner to the health of humans, in that optimal ecosystem health can simply be defined as a system absent of “disease”, whereby “disease” is considered a disturbance to the system (Schaeffer et al., 1988; Anderson, 1991). Hence, the ecosystem “attempts” to eliminate the disease and return to its “preferred” healthy “state” for retaining its health, the “disease” is absent in climax phase. With the help of two variables: potential and connectedness the optimal health of the system found at the climax phase can be explained. The accumulated biomass or ecological capital developed through successional dynamics is represented by the potential of the system, and is therefore greatest at the climax phase (Carpenter et al., 1999). The strength of internal connections between species that mediate and regulate the influences between internal processes and the external environment is determined by the connectedness of the system. Differently, the degree of internal control that a system can exert over external variability is underpinned by the connectedness (Ulanowicz, 1986; Holling & Gunderson, 2002), which again is at its greatest levels at the climax phase. Thus, it is not surprising that the climax phase is most likely to be made up predominantly of K-strategist species, which are species that have smaller specific metabolic rates compared to rstrategists species, which are typically found in the earlier successional phases of ecosystem development. Accordingly, these climax species seem to have been selected as they utilise energy more efficiently and thus, require less maintenance. So, it can be concluded that K-strategist species are “better” adapted than are r-strategist species. Thus, as theorised the changes in the successional communities are made in an effort to improve the whole system’s adaptation to utilise resources more effectively. By considering the climax phase of ecosystem development as the definitive goal function of an ecosystem, Hannon (1992; 1999) has proposed a means to measuring ecosystem health termed gross ecosystem product (GEP), which is based directly on the familiar measure of economic flows gross domestic product (GDP). These explanations of ecosystem health in the succession-to-climax model are illustrated in Figure 3.

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Figure 3. Hypothetical depiction of ecosystem development according to the succession-to-climax model. Boxes A, B and C indicate successional phases, which develop in a linear manner towards box D, the climax phase.

In spite of the theoretical eloquence and clarity of the succession-toclimax model in portraying the process-functionalist approach, it has been severely criticised as being too narrow in scope, rigid and simplistic, and unable to account for and explain the scientific findings of variability in successional pathways, the constant dynamic changes in community composition and the non-equilibrium states observed with ecosystems (Hunter et al., 1988). After all, the development pathway or trajectory to a climax phase is likely to require a much longer time horizon than the “natural” frequency of disturbance events (Kimmins, 1996). Moreover, we know that when interactions between species in an ecosystem are explicitly modelled, their behaviour is non-linear, which will not produce a linear system. Indeed, it is well known that even simple non-linear difference equations of single species models may produce bizarre, extremely nonlinear dynamics (Gleick, 1987). Thus, it seems that the succession-to-climax model is flawed, not only because a complete ecological recovery of an ecosystem to a climax phase that maintains itself again and again can never be realised (Jorgensen, 1997), but because the non-linearity between species is almost certainly not going to lead to a linear system of the whole.

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2. Multiple Equilibria and Resilience in Non-Linear Dynamics

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Multiple equilibria exist, rather than a single equilibrium, as determined in the linear succession-to-climax model is implied by the non-linear nature of ecosystem development (Figure 4a and 4b). Accordingly, given that nonlinear models have multiple equilibria, ecosystem states are only locally stable, and not globally stable as assumed in a linear model. Importantly, this phenomenon of multiple equilibria is not just a mathematical artefact, as the presence of multiple ecosystem states and transitions among these states has been observed empirically in a range of ecosystems (Walker et al., 1981 and 1997).

Figure 4. (a) Graphical representation in discrete time of a dynamic non-linear model. The system has multiple equilibria demarked as x1 and x3, while x2 is an unstable state. (b) Ball-and-cup diagram of the same non-linear model. The cup represents a particular state of the system and the ball represents the current position of the system within the state.

Holling (1973) formally introduced the concept of ecological resilience. Ludwig et al., 1997 diametrically related to notions of local stability and elasticity of the system state. Ecological resilience is effectively about the pressure-stress-response capability of the system state, that is, the magnitude of disturbance a particular state can absorb without transitioning to an alternative system state (Figure 5) (Holling, 1973; 1986; Holling et al., 1995).

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Source: Gunderson et al., 2002. Figure 5. Stability profile illustrating ecological resilience. Importantly, the width of the system state “cup” dictates the magnitude of ecological resilience.

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3. The Population Community Interaction or DiversityStability Model The health of an ecosystem is reflected by its stability (or ecological resilience) is long held belief (Batabyal, 1998; Ferriera & Towns, 2001) and connectance (number of connections) of an ecosystem begets the stability of that ecosystem. Thus, greater the species diversity (a proxy measure for connectedness) present in a system, the more stable the system network is likely to be and the more likely the various ecosystem functions will be maintained (Folke et al., 1996). This diversity-stability hypothesis is the most legitimate ecological argument for preserving diversity within ecosystems. Importantly, in focusing on diversity, this hypothesis is grounded in the “population-community” approach to ecology, which focuses on organisms and species, and thus views ecosystems as “networks of interacting populations” (O’Neill et al., 1986; Allen & Hoekstra, 1992). Pioneering research investigating the diversity-stability hypothesis by MacArthur (1955) seemed to affirm that ecological communities that were highly connected are more stable than simpler ones. MacArthur (1955) suggested that there was a direct correlation between the logarithm of the number of food links in a food web and the degree of stability. Elton (1958) added further weight to the argument when it was pointed out that the apparent extreme stability of tropical rain forests may well be because these systems are the archetypal

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diverse and connected ecosystem. However, MacArthur (1972) suggests that stable systems have in fact intermediate levels of connectance. This credible proposition was also taken up by O’Neill et al. (1986) who concluded that, because a system can become unstable either by being over or underconnected, the addition of a new component can have an effect opposite to what might be intuitively expected. Thus, an increase in diversity can stabilise the system, either by adding connected parts to an underconnected system, or removing connected parts to an over-connected system. This diversity-stability relationship may be formulated as follows: if the system can offer a better survival (i.e. increasing stability in relation to the changing forces functions by decreasing the diversity), the system will not hesitate to react accordingly. Thus, the more diverse an ecosystem, does not give the best answer to stability and survival (Olmsted, 1988). The above model was opposed by May (1973), who found in his study of randomly assembled model food webs exactly the opposite: “too rich a web connectance leads to instability”. It is because as the number of species increases, the probability increases that one of them will be associated with a real positive eigen value, which will hence act towards an unstable mode of oscillation within the system. In the computer simulation, Pimm (1991) found that ecosystems with few species were easy to invade and destabilise. Indeed, ecological communities of up to twelve species were easily entered by intruding and destabilising species. This conclusion was soon supported with empirical evidence by Baskin (1994), who concluded from his findings that the “biggest gains in stability, for example come with the first ten species in a system; beyond ten, additional species did not seem to add much stability, perhaps because the essential functional niches had already been filled”. Interestingly, Baskin (1994) further noted that similar conclusions can be made for productivity, in that “more diverse systems are more productive – at least up to a point”. From most empirical evidence it is found that patterns present in ecosystems are for the most part entirely independent of the species the ecosystem contains (Naemm et al., 1994; Holling et al., 1995; Lockwood & Pimm, 2001). In fact, ecosystem functioning can normally be preserved even as the component species normally considered responsible for that particular function are lost, as other species readily fill the vacated niche (Tracy & Brussard, 1994). Similarly, studies of various ecosystems have also shown that the population dynamics of individual species are more sensitive to stress and perturbations within ecosystems than are ecosystem processes (Schindler, 1990; Vitousek, 1990). Then the question comes how important

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any species are in an ecosystem. In theory, we could experimentally delete species one at a time, measure the ecosystem impacts on resilience and function, and generate a frequency distribution of species importance, relative to abundance. Although some ecologist have tried to solve this insurmountable challenge the research remains inadequate as only a fraction of the species in an ecological community have been deleted (Berlow et al., 1999). The importance of a species might change in different places or at different times is an additional problem (Power et al., 1996). So, in one place and at one time a species may be highly valuable ecologically but in another place or at another time may not be important ecologically. It remains uncertain as what is the underlying relationship to deciphering ecological resilience. Keeping in consideration, Costanza (1992) proposed that, a system state should be considered healthy “if it is stable and therefore sustainable; that is, if it is able to maintain its metabolic vigour, its internal organisation, structure and autonomy and is resilient to perturbations and stresses over a time and space frame relevant to the system”. According to Costanga, modelling ecosystem health it must integrate measures of function, structure and stability, by incorporating potential, connectedness and ecological resilience variables together a straightforward though ad hoc multiplicative index of ecosystem health is formulated (Costanza, 1992; Mageau et al., 1995).

m ax E H I ≡ C × P × R where EHI is the ecosystem health index, C is the connectedness of the ecosystem, P is the potential of the ecosystem, R is the ecological resilience of the ecosystem Costanza’s (1992) Overall Health Index (HI) was HI=V*O*R where V = system vigor; O = system organization index (0-1); and R = system resilience (to stress) (0-1) Regardless, of Costanza’s efforts to develop a suitable ecosystem health index, it is apparent that the various models representing both the “processfunctionalist” and “population-community” approaches are insufficient and

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have considerable reductionistic tendencies that fail to capture the dynamics of non-linear systems and explore under the mist of ecosystem “complexity.”

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4. Self-Organisation, Emergence and Thermodynamics Von Bertalanffy (1968) recently conceptualised a general theory of nonlinear systems, it has emerged from outside the scientific field of ecology, which seems to have finally unveiled the development and behaviour of ecosystems into just a few fundamental principles. This new theory of development and behaviour of dynamic non-linear systems is at its core based on the principle of self-organisation. That is, dynamic non-linear systems such as ecosystems tend to lead to a process of lower to higher levels of organisation (through self-organisation), while being kept within limits (Schuster & Sigmund, 1980). The emergence of higher formed network structures is led by selforganisation (Figure 6). The nexus of this self-organising process is an “attractor”, whereby attractor comes from the state space description of the behaviour of the ecosystem. Thus, a state within a system behaves as if it were “attracted” toward a domain. The dynamics of a self-organising system are largely a function of internal causality and as such the system is dominated by non-Newtonian positive and negative feedback loops (Mandal et al. 2009a). These feedback loops allow the system to maintain itself about an attractor despite changes in the external environment, because the feedback loops of the system tend to maintain the system’s present state. Therefore, the environment may change substantially and exhibit new emergent properties, without the system exhibiting major change. It is this capacity to organise and maintain itself about an attractor that is the fundamental hallmark of a self-organising system. As such, a self-organising system implies a goal-like function (similar in character to the climax phase in the succession-to-climax model), whereby internal causal mechanisms direct the ecosystem towards the state attractor. It is important to re-enforce that self-organisation is a process developed internally within the system, and is not the product of an “external engine” as thought in case of vitalism where life forces directs an ecosystem along some trajectory (Mandal et al., 2009a). Emergent properties arise from local interactions among system components, and in turn they influence the local interactions. All evolving ecosystems possess emergent properties and appear to behave like much-like super-organisms (Kay & Regier, 2000). But this super-organic behaviour is

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the result of a continuing two-way feedback between local interactions and global properties.

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Source: Schuster & Sigmund, 1980. Figure 6. The diagram illustrates the process of self-organisation leading to emergence, where emergence is developed from an initial group of localised interacting agents, which leads to the formation of a higher network structure with global properties.

Prigogine (1980) proposed that thermodynamically, ecosystems as selforganising systems are dissipative systems on the purview of the second law of thermodynamics. That is, according to the second law, energy with a high amount of information and organisation always dissipates in a materially closed system. Thus, while the quantity of energy is always conserved as implied in the first law of thermodynamics, the quality as implied in the second law does not, which means that all energy transformations will involve energy of higher quality being degraded to energy of lower quality. In the purview of second law of thermodynamics an ecosystem of selforganisation must face decay and degradation. But, it is wondering how do ecosystems have the ability to build up and maintain increasingly newly emergent complex structures, when the components of a system have an inherent predisposition towards disorder, decay and degradation? It would appear there is a paradox of life; that is, the emergence of complex structures

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in the face of the second law of thermodynamics. This contradiction was resolved by Schrodinger (1944) by pointing out that whereas the second law of thermodynamics describes isolated or closed systems; all ecosystems have to be described as open systems, which exchange energy with surrounding systems and their environment. Thus, an ecosystem is not strictly a system, but rather a system of systems. That is, there is a hierarchical nature of systems, whereby each system is nested within a system and is made up of systems (Allen & Starr, 1982). Accordingly, because an ecosystem is an open system it can maintain a non-equilibrium state and avoid thermodynamic equilibrium by importing high quality energy from other surrounding systems and its external environment, whilst exporting low quality energy. This exchange of entropy (i.e. a measure of energy disorder, whereby high quality energy has low entropy) by the ecosystem allows the ecosystem’s total entropy to decrease, while inevitably increasing the entropy in the surrounding system’s environment. Schrodinger (1944) concluded these findings by expressing that “life feeds on low entropy”. In view of Schrodinger’s findings, it can be understood that selforganising dissipative processes emerge within open systems whenever a sufficient throughflow of high quality energy with surrounding is available to support them. The details of these dissipative processes depend on the materials available to operate them, the energy and information present to catalyse the processes, and the surrounding environment. The interplay of these factors defines the context for the set of processes which may emerge (Figure 7) (Jorgensen, 1997; Kay & Regier, 2000). Once a dissipative process emerges the open system has a high propensity to move away from thermodynamic equilibrium, and when the system does move it will reach a critical distance from equilibrium, whereby the open system responds with the spontaneous emergence of new organised behaviour that uses the throughflow of high quality energy to manifest and organise itself as a complex ecological structure. These structures provide a new context, nested within which new processes can emerge, which in turn beget new structures, or simply organisation. And with more high quality energy obtained by the system, an ecosystem ultimately emerges. Thus, an ecosystem according to this new perspective of dynamic non-linear systems can be described as a nested constellation of self-organising dissipative processes and structures organised about a particular set of sources of high quality energy, materials and information, embedded in the environment (Kay & Regier, 2000, Mandal et al., 2007).

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Figure 7. The diagram illustrates the process of self-organisation leading to emergence, considering ecosystem as open system. It also shows nested ecosystems describing ecosystem is a system of systems. The surrounding of respective ecosystem attains high entropy and ecosystems reorganise to form new, complex ecosystems.

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5. The Use of Emergy, Exergy and Ascendancy in Ecosystem Model to Assess the Health The operational measure and characterisation of ecosystem health have been captured by the notion of emergy (Odum, 1996), which expresses the amount of energy it costs to build an ecosystem and is measured utilising energy flows, much like Hannon’s (1992; 1999) novel GEP measure. An alternative thermodynamic objective function is that ecosystems attempt to maximise their storage of energy. This goal function can be measured by the notion of exergy, which is a measure of the amount of high quality energy (or information) stored in an ecosystem structure (Jorgensen, 1997, Ray, 2006). The biological and ecological meaning of this objective can be related to Darwin’s “survival of the fittest”, that is, survival means growth, which is equal to increased high quality energy of the system relative to the environment (Jorgensen, 1997) or in other words resilience. The organisation that is able to produce the highest exergy under prevailing conditions will be selected (Ray et al. 2001a). Emergy focuses on how much energy it costs to create the ecosystem structure, but the exergy considers accounting for the ability of the ecosystem to do work. Importantly, the same ecosystem can have quite different emergy

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and exergy values, because for example, some environments will require a greater amount of energy to produce similar structures of biomass. Jorgensen (1997) suggests that exergy is a more appropriate objective function of ecosystem development because while maximum degradation of energy is a consequence of the development of ecosystems from the early to the mature state, it is not necessarily an appropriate objective function for mature ecosystems, as ecosystems cannot degrade more energy than that corresponding to the incoming solar radiation. Interestingly, exergy as a measure of ecosystem health is similar to the “entropy theory of value” conceived by thermodynamic scientist and economist Georgescu-Roegen (1971), However, Jorgensen (1997) warns that while exergy capture and storage is a fundamental “objective” of ecosystem development it does not suggest that other factors should be neglected in demarcating ecosystem health. This sentiment is wise as while a measure of exergy has sound theoretical grounding, it is not only extraordinarily difficult to quantify and measure, it neglects both emergent network properties associated with dynamic non-linear systems and the need to incorporate a variable for determining the resilience properties of the system. Jorgensen (1992) proposed the idea to use exergy as an indicator of ecosystem health, later Ray et al., (2001a) and Mandal et al. (2007) used this index to measure the system performance and also the complexity. The Structural exergy remains at a constant level or increases when the allochtonous compounds are metabolized by the ecosystem, or when the ecosystem can adapt itself to the input of toxicant through structural changes. When the substance are too conservative, too toxic or/and are in too high concentrations, structural exergy decreases, demonstrating the inability of ecosystem to adapt to this influence and irreversibility of changes in ecosystem. Different structural, functional and system level parameters are used as structural exergy and exergy for estimation of ecosystem state and its changes under various external influences was demonstrated for real natural and experimental ecosystems. These parameters were shown to reflect the state of ecosystem and can indicate the degree of ecosystem adaptation, decreasing when important for ecosystem functioning components were eliminated. Jørgensen (1997) also proposed to use eco-exergy, specific ecoexergy = eco-exergy/biomass and ecological buffer capacities as Ecological indicators for ecosystem development and health. The calculation of exergy and its extension to ecological system are described by Mandal et al. (2009). A network perspective of non-equilibrium systems has also been established in the traditions of the diversity-stability model, which does

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capture emergent network properties through modelling ecosystem behaviour by information and network theories. One promising though again somewhat impractical measure of these theories is the “ascendancy” index (Ulanowicz, 1980; 1986; Wulff et al., 1989). The ascendancy index and measures like it go several steps beyond species diversity indices used in ecology, because they estimate not only how many different species there are in a system but, more importantly, how those species are organised collectively in the ecosystem. Thus, a rise in the index of ascendancy represents an increase in system size and organisation, which translates a measure of growth and development, and thus survival (Ray et al. 2001b). However, network approaches, such as the ascendancy index are almost entirely used to investigate systems near steady state. One can conclude that to truly represent and model the principles of self-organisation and emergent global structures, both thermodynamic (energetic) and network measures of ecosystem health (i.e. ascendancy and exergy) must be integrated, as in fact they are complementary perspectives and measures on how an ecosystem develops and behaves (Nielsen & Ulanowicz, 2000). But, the development of this pluralistic viewpoint of so-called “thermodynamic networks” requires more research in hierarchy theory and as such is still very much in its infancy (Jorgensen, 1997; Kay et al., 2001).

6. Order, Chaos and Complexity The fundamental dynamics of non-linear systems has revealed that away from equilibrium the nature of these systems are surprisingly rich and complex, whereby non-equilibrium conditions are a source of organisation and therefore order. In general, a system near thermodynamic equilibrium, being stable, can accommodate fluctuations from the mean state. When forced to move away from equilibrium by externally applied gradients, a critical point may be reached where the fluctuations can no longer be accommodated and instead are amplified to produce a new macroscopic order, described as “complexity”, the “edge of chaos” (Mandal et al. 2007). The process involves an instability being triggered by fluctuations that exceed some threshold, and the system then reorganises itself to accommodate the instability (Mandal et al. 2007, 2008).

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Source: Mandal et al. 2007 and 2009a. Figure 8. a) Ascendency values of the aquatic ecosystem in different conditions. In X axis from 1 to 3 when the system moves toward equilibrium point, values at 4 when it is in equilibrium state, values from 5 to 6 at the junction where oscillation starts (maximum value shown at 6), values at 7, 8 and 9 during limit cycle condition, doubling period and chaos respectively. b) Exergy values of the same aquatic ecosystem in different conditions, values from 1 to 3 when the system moves toward equilibrium point, values at 4 when it is in equilibrium state, values from 5 to 6 at the junction where oscillation starts (maximum value shown at 6), values at 7, 8 and 9 during limit cycle condition, doubling period and chaos respectively.

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Ulanowicz (1997) expressed that there is a minimum and maximum level in between which self-organisation can occur and emergence is maintained. Too much or too little of each of these opposing forces (i.e. internal and external forces) create an imbalance which will upset the efficiency of the system. Thus, the point where the disorganising forces of the environment and the organising forces of the ecosystem are balanced, an optimum operating point is established (Kay, 1984; Kay, 1991). Ulanowicz (1997) also proposed that if a far-from-equilibrium system such as a highly interconnected mature ecosystem becomes isolated, and severed from its energy sources, then it will decay towards thermodynamic equilibrium by irreversible processes. Thus, there is a range within which self-organisation and emergence occurs, and as such complex self-organising systems do not strive for a maximum, but rather an optimum (Figure 8). To grasp this macro-state of complexity consider a set of species as components of an ecosystem. The specific macro-state of the system will be determined by the degree of connections that bind the species of the system together. Now presume that all possible states imaginable can be arranged along an axis. Effectively, this axis (Figure 9) defines state space (Kauffman, 1993), that is, state space is the set of all possible system states that can be constructed from the given set of species available. At the ends of this axis, lie the two extremes: the null set of states, or states of order, which have no or few connections; and the complete set, or states of chaos, which have a high number of connections (or a complete set of connections). The adjacency structure of state space then distinguishes all possible n-systems.

Figure 9. The axis of state space. Importantly, only states within the “states of complexity” are able to produce emergent self-organising systems.

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Importantly, in states of order, coherence and stability of information is maximised, but experimental arrangements of that structure are minimised. In chaotic states the opposite applies, such that it affects a massive search of the possibilities within state space, but is unable to lock onto any that are “useful”. Complexity and the states that lie within it, on the other hand, could be considered an inference of the effect that a balance between stasis (states of order) and change (states of chaos) is the ultimate principle underlying all time evolutionary and self-organising processes. Thus, while equilibrium is the expression of “balance” in a linear “economic” world, complexity is the expression of “balance” in a dynamic non-linear “real” world (Potts, 2000). Importantly, in non-linear relationships that exist in ecosystems, chaotic states are not observed. It is because, unlike undisturbed ecosystem with natural integrity, nature attempts to avoid chaos, so that it can continue to self-organise and evolve (Mandal et al., 2008). Naturally then, the health of an ecosystem should be determined above all by the ability an ecosystem has in maintaining its “integrity” for continued self-organisation (Kay, 1991; Muller, 1998; Kay & Regier, 2000). Importantly, it is this definition of ecosystem health that is sometimes referred to as “ecosystem integrity” and thus, fundamentally different conceptually to ecosystem health (Karr, 1996).

7. Model of Adaptive Cycle Holling (1986) first developed a means to model specifically ecosystem development, with the principles of emergence, complexity, evolution and the integrity of self-organisation being intuitively incorporated. The representative model coined the adaptive cycle is structured by a sequence of four phases that occur within a system state. The first two phases of the model are similar in conception to the predictable and certain nature found with the succession-to-climax model. The first phase named the exploitation or r-phase begins with the ecosystem exploiting those ecosystem processes that are responsible for rapid colonisation of disturbed ecosystems during which developmental r-strategist species capture easily accessible resources. The second phase, described as the conservation phase occurs when the slow resource accumulation builds and stores increasingly complex structures, whereby K-strategist species predominate, hence this phase is also described as the K-phase of the cycle. Ecological capital consisting of biomass and physical structure increases during the long periods of the slow dynamic sequence from exploitation to conservation (r to K), while at the same time

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the system state becomes more and more tightly bound with existing species. Thus, the ecosystem’s connectedness increases to a point, eventually becoming too rigid and over-connected (Holling, 1986; 2001). At a certain point, the tightly-bound accumulation of ecological capital becomes too fragile through the ecosystem being “overconnected”. The actual change from the K-phase to the third phase, “release” or the Ω-phase is triggered by agents of disturbance. The disturbance suddenly releases the resources accumulated and sequestered as ecological capital and the tight organisation is lost. Importantly, the Ω-phase is sometimes referred to as “creative destruction”, which is a term originated by the economist Schumpeter (1954; 1964), to explain alterations in the economy between periods of renewal and periods more conducive with the predictability modelled in economies following a strictly neo-classical trajectory (r to K). Finally, the process of change resultant from the Ω-phase creates opportunity for the fourth phase, reorganisation (α-phase), where released ecological capital is mobilised to become available for the next r-phase.

Source: Holling, 1986; Holling & Gunderson, 2002. Figure 10. Representation of Holling’s adaptive cycle illustrating the four phases of ecosystem development (r, K, Ω, α) and the dynamics between these phases. The arrows indicate the speed of the development in the adaptive cycle, where short arrows indicate slow change and long arrows indicate fast change. The above adaptive cycle diagram is plotted against two ecosystem variables: 1. Y axis, is the potential of the system, which represents the ecological capital the system accumulates as well as unexpressed random genetic mutations (ecosystem innovations); 2. X axis, is the degree of connectedness.

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In contrast, to the r to K stage, the Ω to α stage contains considerable uncertainty. At that stage, the previously accumulated mutations and capital can become re-assorted into novel combinations, some of which nucleate new opportunity and where new species can evolve. Importantly, once the system reaches the α-phase, if the system still retains sufficient amounts of its previous components it can reorganise to remain within the same state as before. However, if the reorganisation process in the α-phase does not retain sufficient amounts of previous components it may make a transition into an alternative system state x. Thus, it is as if two separate “objectives” are functioning, not simultaneously but instead in sequence (Holling & Gunderson, 2002). The r to K stage maximises production and accumulation; then the Ω to α stage maximises invention and reassortment. Figure 10 illustrates a stylised representation of the four phases of the adaptive cycle illustrated within the two dimensions, potential and connectedness. Figure 11 adds the third dimension, ecological resilience to the adaptive cycle. This orientation of the figure illustrates to us that as the phases of the adaptive cycle develop; the ecological resilience of the system expands and contracts.

Source: Holling & Gunderson, 2002. Figure 11. Ecological resilience is another dimension of the adaptive cycle and is added to the two dimensional diagram shown in Figure 10.

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Ecosystem health demarcated by the integrity of self-organisation and modelled by the adaptive cycle model puts species in effect on an ecologically “equal” pedestal. However, this notion of species equality should not be confused with the diversity-stability model, as the adaptive cycle model does not propound that diverse systems are ecologically “better” either.

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8. Catastrophes, Bifurcations and Uncertainty It is known that despite ecosystems being able to maintain their present system states that change in a system state is inevitable. Indeed, beyond a critical threshold value, which is poised at the edge of chaos (Kauffman, 1993), the organisational capacity of a system is overwhelmed and the behaviour of the system becomes highly unstable, whereby the system state inevitably leaves its present domain of self-organisation and attraction. At this point the system may make a transition or “flip” from one attractor to another. Importantly, these shifts are not a gradual, smooth and continuous passage between system states, but rather rapid, catastrophic and step-wise (Perrings & Pearce, 1994). Indeed, the notion of self-organised criticality seems to explain why some processes lead to a minor event, while at other times the same processes lead to major catastrophes (Bak, 1996). The actual change to another basin of attraction, that is, a new state of an ecosystem, is most often modelled by either catastrophe theory or bifurcation theory. Catastrophe theory was originally developed by the mathematician Thom (1975) and explains state transitions in a way that a system trajectory along a smooth surface will at certain points have combinations of impossibility, which correspond to “folds” in the surface mapping. Thus, a system approaching one of these “folds” must make a jump, in so doing the system faces what Thom identifies as a catastrophe. Nonetheless, despite the usefulness of catastrophe theory it has scope limitations, and consequently bifurcation theory is considered the most applicable theory for modelling state transitions. The general approach of bifurcation theory is to construct bifurcations, that is, critical points whereby the trajectory of a system is divided into new possible pathways, so as to explain the state dynamics of a system (Figure 12). Generally, there will be successive bifurcations as the system moves further from equilibrium, each associated with a distinct system configuration.

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Figure 12 .A typical diagram illustrating bifurcation theory (b1, b2 and b3 represent bifurcation points).

There is however, an element of irreducible uncertainty about what new trajectory (or state) after the bifurcation will be selected prior to the actual selection occurring. This uncertainty intrinsically limits the capacity to predict categorically how a situation will unfold, say after changes in the management of an ecosystem (Costanza & Cornwell, 1992; Ludwig et al., 1993; Kay & Regier, 2000; Limburg et al., 2002). Thus, while the “time evolution” of an ecosystem is governed by somewhat deterministic and predictable laws between bifurcations (Levins, 1999), the behaviour at a bifurcation has elements of historical happenstance, which is largely unpredictable and cannot be reduced to probabilistic estimates, no matter how much information we have and how sophisticated our simulations might be. In other words, in the vicinity of a bifurcation, fluctuations with a chancelike character play a dominant role in determining the future state of the system. The reason for this chance-like character is because beyond a bifurcation a system may adopt more than one new state. Thus, changes in the system cannot be tied categorically to any specific environmental changes. In consequence of bifurcation theory, it seems that the Laplacian aspiration to be able to quantitatively predict, with certainty, how the future will unfold, is irreconcilable with modelling of complex self-organising systems (Kay & Regier, 2000). What is certain, however, is that before the

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state attractor is ever reached, the state conditions, determined by the external factors and the internal ecosystem components, will have changed and a new attractor is then effective. And before this new state attractor can ever be reached, new external and internal conditions will again emerge, and so the process goes on (Jorgensen, 1997).

9. PSER Model

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Designing a conceptual framework of ecosystem health assessment scientist designed numerous conceptual frameworks. In all of these frameworks, the Pressure-State-Response (PSR) model (OEDC, 1993) reached the most extensive agreement and was widely used for its clarity causal relationship. The PSER model (Ding et al., 2008; Figure 13) explains “Effect” index as the ability of self-recovery within an ecosystem. It contains resilience and nature response. The pressure index was subdivided into nature pressure and artificial pressure, the state index was subdivided into vigour and organizational structure. So, the PSR model (after Ding et al, 2008) has a better pertinence in indicator selection and accords with the conception of ecosystem health well.

After Ding et al. 2008. Figure 13. Pressure-State-Effect-Response (PSER) Model.

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10. DPSEEA Model

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The simplest framework it is presented is the DPSEEA framework (pronounced “deep sea”) that was developed as part of the HEADLAMP (Health and Environment Analysis for Decision Making) Project, a collaboration of the World Health Organization, the United Nations Environment Programme and the United Nations Environmental Protection Agency in the early and mid-1990s. DPSEEA stands for Driving forces, Pressures, State, Exposure, Effects and Actions. This model is often presented graphically (Figure 14), organized vertically in a linear fashion, from Driving forces (top) to Effects (bottom),with actions feeding in at all levels of the process.

Source: Carneiro et al., 2006, adapted from Corvalan et al., 2000. Figure 14. The DPSEEA Model.

The adaptation by Carniero (2006) provides a useful reminder of the feedback, cycles and interactions that characterize health and environment relationships.

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This model is useful in at least two ways. First, there is a clear derivation of the model from the Pressure State-Response framework that is ubiquitous in environmental management fields. This greatly facilitates communication of environment and health relationships to that particular audience. It brings environment and health into the professional comfort zone of practitioners of, for example, environmental impact assessment and environmental monitoring. Second, it is organized in a hierarchical manner while at the same time emphasizing action. This makes the point that intervention in environmental contexts to improve human health can be targeted at a variety of scales, and that choice of scale is important. The DPSEEA model, however, is simplistic and requires much clarification about the sequence of—and feedbacks within—any particular set of relationships.

11. Butterfly and Prism Models

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The “Butterfly” model of health for an ecosystem context (VanLeeuwen, 1998; VanLeeuwen et al., 1999) and the “Prism” framework of health and sustainability (Parkes 2003a, Parkes et al., 2003) seek to depict the interactions within environment-and-health systems (Figure 15).

Source: VanLeeuwen, 1998. Figure 15. Butterfly Model.

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Further-more, both models are couched in a discussion of the evolution of our understanding of the meaning of health and its determinants, the influence of ecological thinking, and the need for management of human health at the interface of biophysical and socio-economic environments. A primary advantage of these models is that they place human beings more explicitly within the system rather than external to it. Humans are the focal points and participants in the relationships that influence their health, and not simply the object of consequence for outcomes of biophysical or socioeconomic processes. This type of thinking has implications for our approach to managing such system.

Source: Parkes et al., 2003. Figure 16. Prism framework model of health and sustainability depicts.

In the Prism Framework (Figure 16), Parkes et al. (2003) emphasize “the need for integrated approaches to research and policy, methods that can engage with the synergies between the social and physical environment, and the incorporation of ecosystem principles into research and practice.” Thus,

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the management of social-ecological systems for human health will be multilayered, requiring the synthesis of knowledge across a broad spectrum of scientific, professional actors and private sector, governance and lay stakeholders.

12. MA Framework Model

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The final model reviewed here is the conceptual framework of the 2005 Millennium Ecosystem Assessment (Millennium Ecosystem Assessment Board, 2005).

Figure 17. MA Model. Changes in the indirect drivers of change can lead to changes in drivers that directly influence ecosystems. The resulting changes in ecosystems are reflected in changing ecosystem services, which in turn affect human well-being. These relationships operate concurrently at a number of scales, including the local, regional and global scales.

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The relationships depicted in Figure 17 complement the models of Parkes and VanLeeuwen by explicitly linking human well-being with a suite of significant “ecosystem services.” This anthropocentric approach makes a clear argument for environmental policy in human terms. It incorporates a broad definition of human wellbeing, which includes not only health, but also the basic material for a good life (good social relations, security, and freedom of choice and action) and notes that strategies and actions are needed at almost all points in the framework. The Butterfly model, the Prism Framework and the MA framework help us to understand that health is an expression of the condition of the overall system of interacting ecological and human relationships. By going beyond the linear depictions that characterize health or illness as a consequence of exposure to defined environmental hazards, these frameworks allow us to more easily conceive of human health as a lens through which to view socialecological systems that are complex and evolutionary.

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ECOLOGICAL RISK, RISK ASSESSMENT AND MANAGEMENT Ecological Health Assessment (EHA) is done to understand the whether the ecosystem is in risk. An EHA may end up healthy or unhealthy ecosystem. Unhealthy ecosystems, after assessment may be found exposed to ecological risk. Risk, literally, can be defined as the probability that a substance or situation will produce harm under specified conditions. It is a combination of two factors, first, the probability that an adverse event will occur (such as a specific disease or type of injury) and second, the consequences of the adverse event. Risk is time related, ranging from immediate consequences of various actions or lack of action to consequences over a lifetime for an individual and much longer periods for the whole society, ecosystem or the planet. Ecological risk ultimately impacts on public health and on the environment, and arises from exposure to stressors.

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Ecological Risk Assessment (ERA) Once an ecosystem is diagnosed as unhealthy and exposed to risk, assessment in the part of characteristics and mode of operandi of risk is the immediate need. Risk assessment is the systematic, scientific characterization of potential adverse effects of human or ecological exposures to hazardous agents or activities. Risk assessment is performed by considering the types of hazards, the extent of exposure to the hazards, and information about the relationship between exposures and responses, including variation in susceptibility. Adverse effects or responses could result from exposures to chemicals, microorganisms, radiation, or natural events. However, satisfactory ERA is impossible practically for loss – benefit mismatch among stakeholders of society and environment. People who face specific risks are different from the people who benefit from the products or activities that generate the risks, leading to conflict and litigation over proposed riskreduction actions over loss-benefit mismatch. That is why it is impossible to eliminate all environmental effects of human activities and that decisions must be made on the basis of incomplete data and incomplete scientific knowledge and to minimize human-ecosystem conflict (Suter, 1993). ERA helps to reach a stage of compromise between acceptable risks levels and the costs of reducing these risks. According to the U.S. EPA (1998), ERA has three main phases that are followed by risk management (Figure 18): 1) Problem formulation: In this phase it is necessary to select the assessment endpoints (defined as a formal expression of the environmental value to be protected, Suter, 1989), develop the conceptual model and prepare an analysis plan. 2) Analysis: In this phase two principal activities should be carried out which are exposure and ecological effects characterization. The first activity describes the sources of stressors, their environmental distribution and contact with ecological receptors, whereas ecological effects characterization deals with the evaluation of stressor response relationships, e.g. dose response curves for a contaminant. 3) Risk characterization: During this phase the ecological risks as well as the confidence degree are evaluated. However, risk assessment results have to be combined with socio-economic and legal aspects before decisions be taken.

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Figure 18. Strategies of Ecosystem Risk Assessment (ERA). Careful ERA leads to Ecosystem Risk Management (ERM).

Problem Formulation This initial phase provides the foundation for the entire process. During this phase three essential steps are required: Endpoints assessment, conceptual model and analysis plan (U.S. EPA, 1998).

Endpoints Assessment An assessment endpoint is a formal expression of the environmental values to be protected (Suter, 1993). In order to define them it is necessary first to identify valued attributes of the environment at risk and to define

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these attributes in operational terms. Generally speaking, endpoints should have biological as well as social relevance, a clear operational definition and proven susceptibility to the contaminant as well as should be easily measurable. However, sometimes assessment endpoints and measured endpoints are not the same. Measurement endpoints are generally values from toxicology, e.g. 96-h LC50, or functions as dose-response, whereas assessment endpoints generally refer to characteristics of ecosystems defined over long temporal scales, e.g. fish population in a lake. Therefore, it is necessary to define in any ERA, if assessment and measured endpoints are different, a method to extrapolate from one to the other. There are two statistical types of measurement endpoints (Suter, 1993): a/ those that prescribe a level of effect by fitting a function relating the measured effects to measurement of exposure (responsedose). Normally only a particular level is used, e.g. LC50, LD50 (median lethal dose), EC50 (median effective concentration), and LC01 (lethal threshold concentration); b/ those that are based on hypothesis testing. In this case, responses at the exposure concentrations are compared with a control (unexposed) to test the null hypothesis that they are the same as the control responses, examples include: NOEC (no observed effect concentration) and LOEC (lowest observed effect concentration).

Conceptual Model A conceptual model is a written and/or visual representation of predicted relationships between ecological entities and the stressors to which they may be exposed (U.S. EPA, 1998). In order to develop a conceptual model it is necessary to have information on existing stressors, potential exposure and predicted effects on the assessment endpoints. Conceptual models have two main components: Risk hypothesis and diagrams (U.S. EPA, 1998). Risk hypothesis are assumptions about potential risk to assessment endpoints, whereas conceptual model diagrams represent visually the risk hypotheses. Conceptual models may miss sometimes important relationships and therefore may misrepresent risks. For this reason ERA processes is normally an iterative process in which the different steps are improved as the information increases. Analysis Plan This stage should identify the problem, establish study boundaries, and determine necessary data quality, quantity and applicability to the problem

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being evaluated. Is in this part of the process that, risk hypothesis has to be evaluated to determine how they are going to be assessed. The analysis plan should include needed data and gaps with recommendations for new data collection (if needed), hypothesis prioritisation as a function of the level of risk and confidence level expected from management point of view.

Risk Characterization

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The integration of information concerning sources, exposure and effects is the last step in ERA. This integration should allow obtaining an estimate of the level of effects that will result from the exposure. According to U.S. EPA (1998), it is composed of two steps: Risk estimation and risk description.

Risk Estimation When integrating exposure and effects assessment, it is necessary to express the effects using a fourthdimensional state space (Suter,1993), whose dimensions (two for exposure and two for effects) are: 1) the concentration of the substance to which organisms are exposed; 2) the duration of the exposure; 3) the proportion of the community responding; and 4) the severity of the effect. Whereas in traditional human health risk assessment the important parameter is the duration of the exposure (dose rate x time), normally, in ERA it is more important the duration of the effects to assess how long an ecosystem is degraded. In addition, ecosystem exposed for long periods may adapt to a certain pollutant and biological effects may end before exposure disappear or may continue to occur after the exposure ceases, because there are time lags or other physical processes –e.g. re-suspension by storms -in the induction of the effects.

Ecological Risk Management (ERM) Risk management is the process of identifying, evaluating, selecting, and implementing actions to reduce risk to human health and to ecosystems. The goal of risk management is scientifically sound, cost-effective, integrated actions that reduce or prevent risks while taking into account social, cultural, ethical, political, and legal considerations.

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Even though risk management can be carried out without risk analysis, the effectiveness of it depends necessarily on the successful development of the ERA process. The ideal input from an ERA process to risk managers would be setting criteria for the allowable concentrations of a chemical in the ecosystem components and providing probability functions for unacceptable effects on the endpoint. Management has to follow three strategic steps preceded by ERA, viz. Development of regulatory options ÆEvaluation of public health, economic, social, political consequences of regulatory options Æ Agency decisions and actions. However, whatever management measures are to be approved, it should be in collaboration with stakeholders and using iterations if new information emerges that might change the need for or nature of risk management.

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Transition: Patterns and Processes of Biotic Impoverishment. Cambridge: Cambridge University Press. Schrodinger, E. (1944). What is Life? Cambridge, Cambridge University Press. Rapport, D. J., Costanza, R. & McMichael, A. J. (1998). Assessing ecosystem health. Trends in Ecology and Evolution, 13, 397402. Schumpeter, J. A. (1954). History of Economic Analysis. New York, Oxford University Press. Schumpeter, J. A. (1964). Business Cycles: A Theoretical, Historical and Statistical Analysis of the Capitalist Process. New York: McGraw-Hill. Schuster, P. & Sigmund, K. (1980). Self-organisation of biological macromolecules and evolutionary stable strategies. In Haken, H. (Ed.). Dynamics of Synergetic Systems. London: Springer-Verlag. Science Advisory Board. 1990. Reducing Risk: Setting Priorities and Strategies for Environmental Protection . U.S. Environmental Protection Agency, Publication SAB-EC90-021. Southerland, M. (1999). Considering Ecological Processes in Environmental Impact Analyses. U.S. Environmental Protection Agency Publication EPA 315-R-99001.N.p. Steedman, R. J. (1994). Ecosystem health as a management goal. Journal of the North American Benthological Society, 13, 605-610. Suter II, G. W. (Ed.) (1993). Ecological Risk Assessment. Boca Raton, FL. Lewis Publishers. Tansley, A. G. (1920). The classification of vegetation and the concept of development. Journal of Ecology, 8, 118-149. Thom, R. (1975). Structural Stability and Morphogenesis. Reading, Benjamin. Tracy, C. R. & Brussard, P. F. (1994). Preserving biodiversity: Species in landscapes. Ecological Applications, 42, 205-207. Ulanowicz, R. E. (1980). An hypothesis on the development of natural communities. Journal of Theoretical Biology, 85, 223-245. Ulanowicz, R. E. (1986). Growth and Development: Ecosystems Phenomenology. New York, Springer-Verlag. Ulanowicz, R. E. (1997). Ecology: The Ascendant Perspective. New York, Columbia University Press. US. EPA, 1998. Guidelines for Ecological Risk Assessment. EPA/630/R95/002F. Washington DC:U.S. Environmental Protection Agency, Risk Assessment Forum.

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VanLeeuwen, J. (1998). Describing,Applying and Testing Models and Indicators of Human Health in Agroecosystems: Finding the Balance. Thesis submitted for the Degree of Doctor of Philosophy at University of Guelph, Ontario, Canada. Ottawa: National Library of Canada. VanLeeuwen, J., Waltner-Toews. D., Abernathy, T. & Smit, B. (1999). Evolving models of human health toward and ecosystem context. Ecosystem Health, 5, 204–219. Vitousek, P. M. (1990). Biological invasions and ecosystem processes: Towards an integration of population biology and ecosystem studies. Oikos, 57, 7-13. Von Bertalanffy, L. (1968). General System Theory: Foundations, Development, Applications. New York, George Braziller. Walker, B. H., Ludwig, D., Holling, C. S. & Peterman, R. M. (1981). Journal of Ecology, 69, 473-498. Walker, B. H., Langridge, J. L. & McFarlane, F. (1997). Resilience of an Australian savannah grassland to selective and non-selective perturbations. Australian Journal of Ecology, 22, 125-135. Woodley, S., Kay, J. & Francis, G. (1993). Ecological integrity and the management of ecosystems. St. Lucie Press. Wulff, F., Field, J. G. & Mann, K. H. (1989). Network Analysis of Marine Ecosystems: Methods and Applications. Heidelberg: Springer-Verlag Zuorui, S., Wenjun, S., & Jianjun L. Research Progress of Theory and Technology of Ecosystem Health. In L. Wenhua & W. Rusong (Eds). Ecological security and ecological construction (pp.201-206). Beijing: China Meteorological Press.

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Chapter 3

THE EUTROPHICATION OF AQUATIC ECOSYSTEMS: CAUSES, EFFECTS AND REHABILITATION

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Gabriela Elena Dumitran and Liana Ioana Vuţă Hydraulic, Hydraulics Machinery and Environmental Engineering Department, Faculty of Power Engineering, University “Politehnica” of Bucharest; Romania

ABSTRACT This paper aims to outline the basic concepts of eutrophication of aquatic ecosystems, in particular with reference to lakes and reservoirs causes, stages of development, and effects on water quality and rehabilitation possibilities of damaged ecosystems. The objectives of this study are multiple and approach issues related to understanding the process of eutrophication, which essentially consist in super fertilization of surface waters. This is one of the most serious problems affecting surface water quality in today's society due to the effects generated in water bodies. Thus, large quantities of nutrients induce excessive growth of plants and hence the associated effects: algae bloom, high quantities of macrophyte, organoleptic changes and impact on aquatic life and even on human health.

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Gabriela Elena Dumitran and Liana Ioana Vuţă Overall, the paper tries to give answers to questions as: What is eutrophication? What are the causes and the effects of this phenomenon, or What are the possibilities for rehabilitation of eutrophic ecosystems. So the paper tries to provide relevant information to lake ecosystem managers on how to avoid this phenomenon, or when not possible, the possibilities of diminishing its effects on lake water quality.

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1. INTRODUCTION Nowadays pollution has become a socio-economic problem which required drastic action to limit pollutant discharges including into aquatic ecosystems. In this respect, one of the main directions of European policy is to ensure proper water quality for all consumers. This explains the currently existing interest in reducing the phenomenon of eutrophication, which has known a large spreading in Europe and all over the world and affects more and more aquatic ecosystems. The literature reports 45% eutrophic lakes on world scale: 54% in Asia, 53% in Europe, 48% in North America, 41% in South America and 28% in Africa. In other words, eutrophication phenomenon is one of the most serious problems affecting surface water quality (Mainstone and Parr, 2002). Adding nutrients to a certain limit into aquatic ecosystems does not represent a problem; it can enrich the aquatic flora and thus permitting growth and diversification of fauna. But exceeding this limit and provoking surface water super fertilization causes explosive growth of plants with serious effects onto water quality. This process is called eutrophication and represents one of the conventional pollution problems: it refers to the faith of organic and inorganic substances in water, integrated into physical and biochemical processes involving biocenoses aquatic ecosystem (Popa, 1998). Thus, eutrophication is the enrichment of water in nutrients, mainly nitrogen and phosphorus, causing algae flourishing, excessive growth of aquatic macrophytes, high turbidity of water, and oxygen deficiency in bottom layers of the aquatic ecosystems and, in some cases, an unpleasant smell and taste of water (Figure 1). Very slow process; stretching over hundreds of years, eutrophication is a normal phenomenon as long as it has a natural evolution.

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Figure 1. The eutrophication process.

In natural eutrophication, nutrient enrichment is owe only to natural agents, which have an intermittent flux, and the process is characterized by alternative growth and decrease in nutrients concentration, in relation to weather conditions. Artificial or cultural eutrophication, caused by man, has a very rapid evolution, its effects are important and impossible to overpass. In this case, the addition of nutrients is artificial and as a result of agriculture run-off, deforestation, livestock, urbanization and industrialization (Figure 2.).

Figure 2. Trophic evolution and aging of the lake ecosystems.

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2. HISTORY OF EUTROPHICATION Eutrophication is not a recent phenomenon, and its manifestations, in form of unusual color of water, have been mentioned since ancient times. Thus, occurrences of "red tides" are reported in the literature, which according to researchers are due to the explosive growth of phytoplankton and thus a red coloration of the sea appear. This phenomenon was most often associated with periods of heavy rainfall. Dragovich, 1969, reported that heavy rains generated plant explosions into Adriatic Sea, particularly in the northwest rivers mouth (especially in Po River). These areas with abundant vegetation expanded even offshore, affecting water costs of Yugoslavia (Dragovich and Kelly, 1964-1965). The concept of progressive eutrophication of a water body by changing from a low to a high production due to an increase of inorganic nutrients, appear in the early twentieth century (Hilton et al., 2006)). At that time, there were insufficient qualitative criteria for water bodies classification, so assignment to a particular trophic group were made mostly based on experience rather than on scientific criteria. A major step in the development of descriptors of the trophic status of aquatic ecosystems, which are widely accepted, was developed by Vollenweider in 1968 (Vollenweider, 1969). Thus, in an international research program, experimental measurements of quality indicators were conducted for 100 lakes from north-temperate areas. Then the lakes were classified using a set of qualitative descriptors based on measurable quantities such as nutrients concentration and especially total phosphorus load. Five trophic classes resulted: ultra-oligotrophic, oligotrophic, mesotrophic, eutrophic and hypertrophic (table 1). Table 1. The correspondence trophic status - total steady-state phosphorus concentration proposed by Vollenweider

Trophic State

Total Steady- State Phosphorus Concentration (μg/L) >60 6.5; 6.5-5; 10, Cu > 5 µg l-1) are limited by the 30 km zone at the expense of air emissions from the smelter; being in the structure of waste water, heavy metals migrate in water systems on distances up to 200 km in the direction of flow distribution (Moiseenko, 1999). By toxic pollution of waters (for example, with concentration of Ni 20-100, Cu 10-50 µg l-1) there takes place the formation of pure communities from eurybionthic species widely represented and cosmopolites as well.

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Table 1. The dominating communities’ species in natural waters of the Fennoscandia North Phytoplankton Aulacoseira distans, A. italica, A. islandica, Asterionella formosa, Cyclotella comta, Tabellaria fenestrata, Dinobryon divergens, Anabaena

Zooplankton Kellicottia logispina, Conochilus unicornis, C. hippocerpis, Bosmina obtusirostris, Daphnia longispina v. hyalina, Cyclops scutifer, Eudiaptomus gracilis

Zoobenthos Chironomus f. l. bathophilus, Ch. f. l. salinarius, Ch. f. l. plumosus, Procladius, Tanytarsus

Fish Salmo trutta, Coregonus lavaretus, C. albula, Salvelinus alpinus, Thymlus thymallus

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The reorganization of structure of phytoplankton communities occurs in the direction of mass development of green algae of genus Scenedesmus, Pandorina and a number of diatoms. The reduction of phytoplankton species’ diversity by increasing of nickel pollution is shown on the example of the Imandra lake and small water reservoirs in the smelters’ impact zone. By the maximum pollution level the number of structure-forming species does not exceed 2 (Sharov, 2002). In zooplankton there prevail rotifers - organisms with low individual mass, typical pollution indicators by domination of Asplanchna priodonta, Keratella nochlearis, Keratella quadrata, Kellicottia longispina. The role of large cladocerans (Leptodora kindtii, Daphnia cristata) and copepods (Eudiaptomus graciloides, Heterocope appendiculata) is insignificant. The number of structure-forming species in the pollution zone amounts 45(Vandish, 2002). Fauna of macrozoobenthos is represented extremely by larvae of Chironomus, Procladius (Holotanypus) steady against pollution by heavy metals. On the significant water area, where there are distributed waste waters of the metallurgical companies, the species diversity is reduced at the expense of disappearance of relict crustaceans, Ephemeroptera, leeches, Bivalvia and other typical inhabitants of the regional lakes (Yakovlev, 1998; Iliashuk, 2002). The fish population under the given pollution can be represented by 1-3 species. Among the northern species in the pollution zones is typical whitefish Coregonus lavaretus L. The predatory fishes of the freshwater-arctic complex ( brown trout Salmo trutta and arctic char Salvelinus alpinus) disappear from the faunal complex. At the coastal sites are met perch Perca fluviatilis and pike Esox lucius, in prophundal - burbot Lota lota (Moiseenko et al., 2002). In table 2 there is given the summary species’ characteristic of dominating complexes of water communities under conditions of water body’ toxic pollution; along with the tendency to the reduction of biodiversity indices there is shown their variability under various impact gradients. The water area, where there is fixed the significant degradation of biodiversity as a result of waters toxic pollution amounts by our estimations 5 % from general aqua area on the territory of Murmansk region. Water communities in the most polluted aqua areas by heavy metals are characterized along with the low species’ diversity by the minimum quantity and biomass as well.

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Table 2. Biodiversity indices (N -species’ quantity, HN -Shannon index) and dominating species in communities of aquatic ecosystems of the Kola North under conditions of toxic pollution Pollution marker Nickel, μg l-1

Phytoplankton N HN Zooplankton N HN Zoobenthos N HN Fish N

< 10

10-20

20-100

15 - 40 > 2.5

30 1.5 - 3.5

10 - 30 1.0 - 3.0

10 - 25 > 2.5

10 - 20 1.0 - 2.5

10 - 15 0.5 - 2.5

Dominating species under the high pollution gradient Aulacoseira islandica, Rhizosolenia, Eudorina, Pandorina Keratella cochlearis, Asplanchna priodonta, Kellicottia longispina, Polyarthra sp. Chironomus, Procladius, Dytiscidae, Nematoda

> 60 > 2.5

5 - 60 1.0 - 2.5

20 Phytoplankton N HN Zooplankton N HN Zoobenthos N HN Fish N

25 - 40 > 3.5

20 - 35 2.0 - 4.0

10 - 30 1.0 - 3.5

10 - 25 > 2.0

10 - 20 1.2 - 2.7

10 - 15 0.5 - 2.5

> 50 > 2.5

5 - 50 1.0 - 2.5

20 Microcystis aeruginoza, Stephanodiscus, Asterionella, Aulacoseira islandica, Synedra, Sphaerocyctis Keratella cochlearis, Asplanchna priodonta, Kellicottia longispina, Bosmina obtusirostris Chironomus, Tubifex tubifex, Limnodrilus hoffmeisteri, Procladius C. lavaretus, E. lucius, P. fluviatilis

>6

2-6

1-2

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Table 4. Biodiversity indices (N - species’ quantity, HN Shannon index) and dominating species in the communities of water ecosystems of the Kola North under conditions of thermal impact Pollution marker Temperature Δt 0C < 3.0 o 3.0 -8.0 o > 8.0 o Phytoplankton N HN Zooplankton N HN Zoobenthos N HN Fish N

25 - 40 > 3.5

20 - 35 2.0 - 4.0

10 - 30 1.0 - 3.5

10 - 20 > 2.5

10 - 15 1.0 - 2.5

10 - 15 0.5 - 2.5

> 90 > 3.0

10 - 90 1.0 - 3.5

< 15 0.5 - 2.0

15

14

4

Dominating species by the maximum Δt Rhyzosolenia, Diatoma, Fragilaria crotonensis, Cyclotella, Tabellaria Stephanodiscus astreae Asplanchna priodonta, Synchaeta sp., Kellicottia longispina, Bipalpus hudsoni , Bosmina obtusirostris Tubificidae, Procladius, Monodiamesa bathyphila, Polypedilum Phoxinus phoxinus, Salmo trutta, P fluviatilis, Cyprinus carpio

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Brown trout Salmo trutta, easily enduring the temperature increase up to 16-17 0C, uses this area for fattening practically for the whole year. Whitefish (Coregonus albula and Coregonus lavaretus) can make fodder daily and seasonal migrations choosing an optimum temperature mode in the warmed zone (Moiseenko et al., 2002). Changes of the biodiversity indices depending on the gradient of the temperature increase shows table 4. It should be noted that in the impact zones of the NPS discharge waters the significant influence on communities along with the temperature factor belongs to the hydrodynamic conditions making both by pumping over of large water volumes and compensatory currents

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ACIDIFICATION Acidification of waters in the Fennoscandia North becomes apparent at pH decrease of small lakes water, pH-shock at the streams during the high water period and decreasing of buffer capacity of the large water catchment areas. In the Kola North more than 10% of small lakes (from 500 explored ones) are acidified, more than 30% are in the critical state, when the acidneutralizing waters capability is less than 50 μeq l-1. There are found the heavily acidified lakes with pH 4.2-4.5 (Moiseenko, 2003). The water acidification leads to the biodiversity decreasing at the expense of reduction of the species sensitive to the pH decrease: in the flora of diatoms at the general background of the biodiversity decreasing there increase the part of acidobionthic and acidophylic species: Aulacoseira distans, Cyclotella antigua, C. kuetzingiana v. planetophora, Tabellaria fenestrata, T. flocculosa, T. quadriseptata. In the zooplankton of the lakes with low pH there dominate (in the number) the acidification steady species: Holopedium gibberum, Bosmina obtusirostris, Eudiaptomus gracilis (Vandish, 2003). From the bottom organisms of lothic and lenthic systems the most sensitive to the water pH lowering are beach fleas, snails Lymnaeà, Valvatidae, mayflies (excluding Leptophlebidae, Heptagenia fuscogrisea, Caenis horaria) stoneflies (excluding Nemoura spp.). Beach flea Gammarus lacustris is not found in water with pH 25 > 3.5

15 -20 1.5 - 2.5

< 15 1.0 - 2.0

> 30 > 3.5

10 -25 1.0 - 3.0

< 10 < 2.0

>100 > 3.0

20 - 100 1.5 - 3.5

< 20 0.5 -2.0

Aphanothece clathrata, Gloecarsa, Mycrocystis, A. distans, Tabellaria quadriseptata Holopedium gibberum, Bosmina obtusirostris, Eudiaptomus gracilis Chironomidae, Asellus aquaticus, Leptophlebidae, Nemoura, Polycentropodidae, Dytiscidae P. fluviatilis

>6

1-2

0-1

212

T. I. Moiseenko, A. N. Sharov, O.I. Vandysh et al. 3) As a result, by the disappearance of a number of species there takes place the simplification of the communities’ structure, what leads to simplification of trophic connections: in zoobenthos there takes place the replacement of filters-collectors, scrapers with collectorsdetritophages, pulverizers, pelophages and predators, the the zooplankton structure - replacement of large Cladocera and Calanoida with small “fine” filtrates (Bosmines) being unable to filter large food elements under conditions of the heightened water turbidity; there reduces the role of predatory pelagic salmon fish (brown trout Salmo trutta, arctic char Salvelinus alpinus) in the trophic structure of the ecosystems.

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Along with general ecological regularities in changes of waters biodiversity under all kinds of pollution there could be emphasised a number of specific communities’ responses to the various kinds of anthropogenic loads: 1) Under acidification in diatom flora there prevail acidibionte and acidophile species (Aulakoseira distans, Tabellaria quadriseptata, T. flocculosa), in zoobenthos the most steady are mayflies E. aurivilli, caddis flies Polycentropidae, stoneflies Nemouridae. 2) Under toxic pollution in plankton there dominate: diatom Aulakoseira islandica, green algae Eudorina, Pandorina and rotifers Asplanchna priodonta, Keratella ñochlearis, Kellicottia longispina steady to the pollution. 3) By eutrophication there increases the diatoms’ quantity: Stephanodiscus, Asterionella, Aulacoseira islandica, Synedra; and eurybionte zooplankton species: Keratella quadrata, Keratella cochlearis, Notholca caudata, Bosmina longirostris, Daphnia cristata. 4) Under thermal impact there increases the species’ diversity of diatom algae. The main directions determined in this work in forming the species’ structure of communities under the impact of various anthropogenic loads can become the basis for organization of the biodiversity control in the Subarctic freshwater ecosystems. It is possible to use the following indices: a) general number of species and class species’ distribution; structure of the dominating forms group; b) diversity of relict forms, including their quantity

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and biomass, and c) also systematic groups typical for the region: mayflies, stoneflies, chironomids Diamesinae, Orthocladiinae, Tanytarsini, large cladocerans and copepods, diatom algae, salmon fish.

ACKNOWLEDGMENTS The work was supported by the Russian Foundation for Basic Research (Projects no 10-05-00854) and grant Governments of Russia (№ 11G34.31.0036).

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REFERENCES Anthropogenic Modifications of the Lake Imandra Ecosystem. (2002) (ed. Moiseenko T.I.). Moscow: Nauka. 485p. Biological efficiency of the northern lakes. (1975) Lakes Zelenetskoye and Akulkino: Works of the Zoology Institute of USSR Academy of Sciences. Leningrad, V.57. 182 p. Flora and fauna of reservoirs of the European North (on the example of the lakes of Bolshezemelskaya tundra). (1978) (ed. M.V. Getsen). Leningrad: Science, 192 p. Galkin GG, Kolushev AI, Pokrovskiy VV. (1966) Ichthyofauna water basins and lakes of Murmansk area. In: Fishes of Murmansk area (ed. Galkin G.G.). Murmansk, P.177-193. Iliyaschuk B.P. (2002) Zoobenthos. In: Anthropogenic Modifications of the Lake Imandra Ecosystem (ed. Moiseenko TI). Moscow: Nauka; P. 200226. Illies J. (1978). Limnofauna Europea. Auflage: Stuttgart, 532 p. Intercalibration of invertebrate fauna. 9603. (1996). Bergen. Zoological Museum. University of Bergen, Norway, 19 p. Krokhin EM, Semenovich NI. (1940) The data on water bodies in the Kola Peninsula (The collection № 1, manuscript). Apatity: Funds of Kola science centre of RAN; 151 p. Letanskaya G.I. (1974). Phytoplankton and primary production of lakes of the Kola peninsula. In: Lakes of various landscapes of the Kola peninsula. Part 2. Leningrad, P. 78-119.

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Lillehammer A. (1988). Stoneflies (Plecoptera) of Fennoscandia and Denmark. In: Fauna Entomologica Scandinavica, V. 21. P. 1-158. Maurer B.A. (1994). Geographical Population Analysis: Tools for the Analysis of Biodiversity. Oxford, London, 130 p. Moiseenko T.I. et al. (1994). Pasvik River Watercourse, Barents Region: Pollution Impacts and Ecological Responses. Oslo: INEP-NIVA Report. No 0-93144, 87 p. Moiseenko T.I. Acidification of Water: Factors, Mechanisms and Ecological Consequences. Moscow: Nauka, 2003, 276p. Moiseenko T.I., Lukin, A.,A., Sarova Yu.,N., Koroleva I.M. ( 2002). Fish under of life conditions сhange In: Anthropogenic Modifications of the Lake Imandra Ecosystem (ed. Moiseenko TI) Moscow: Nauka; P. 284390. Moiseenko T.I., Yakovlev V.A. (1990). Anthropogenic transformations of water ecosystems in the Kola North. Leningrad: Science, 221 p. Moiseenko T. I. (1999) A Fate of Metals in Arctic Surface Waters. Method for Defining Critical Levels // The Science of the Total Environment, 236, P. 19-39. Moiseenko T.I., Sharov A.N,. Vandysh O.I., Yakovlev V.A., Lukin A.A. (1999). Changes in biodiversity of surface waters of the North under acidification, eutrophication and toxic pollution. Water Resources, 4, P. 492-501. Petrovskaya M.V.(1966) Characteristic of zooplankton of Murmansk area lakes. In: Fishes of Murmansk area (ed Galkin G.G.). Murmansk. P. 84 105. Poretskij VS, Zhuze AP, Sheshukova VS.(1934) Diatoms of Kola peninsula in connection with microscopic structure Kola Diatomite. Works Geomorphological institute AN USSR, Leningrad. 1934; V. 8. P. 96-210. Sharov A.N. (2002) Phytoplankton. In: Anthropogenic Modifications of the Lake Imandra Ecosystem (ed. Moiseenko T.I.) Moscow, Nauka, P. 130161. Vandish O.I. (2000) Zooplankton as an indicator of lake ecosystem conditions (case study of Subarctic Lake Imandra). Water resources. V. 3. P. 328 – 334. Vandish O.I. (2002). Zooplankton. In: Anthropogenic Modifications of the Lake Imandra Ecosystem (ed. Moiseenko T.I.). Moscow: Nauka, P.162199.

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Vandysh O. I. (2003). Influence acidification on zooplanktonic communities of small lakes of mountain tundra (on an example of Euro-Arctic of region). //Water resources. V. 5. P. 534-540. Yakovlev V.A. (1998) Response of zooplankton and zoobenthos communities on water quality change of subarctic lakes (by the example of lake Imandra). // Water resources, V. 6. P. 715-723. Yakovlev V.A. (1997). An estimation of the degree surface waters acidification in the Fennoscandia North-East on zoobenthos // Water resources, V.5. P. 340-349.

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INDEX

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A  acid, 5, 45, 124, 196, 197, 209 acidity, 122 activity level, 59 adaptability, 22 adaptation, 65, 75, 85, 190 adsorption, 121, 136 adverse effects, 89, 90 aerobe, 113 Africa, 7, 13, 104 agriculture, 14, 21, 28, 105, 116, 170 air emissions, 197, 200 air quality, 61 air temperature, 113 airports, 58 alanine, 10 Alaska, 29 algae, vii, viii, 2, 4, 6, 9, 13, 15, 16, 17, 22, 103, 104, 107, 109, 114, 117, 118, 119, 120, 121, 122, 123, 124, 125, 129, 130, 132, 133, 134, 199, 202, 204, 206, 212, 213 alkalinity, 119, 124 ALS, 48 amino acids, 10, 11, 17, 37 ammonia, 116, 117, 119, 132 ammonium, 9, 116, 130 amoeboid, 46 amortization, 127 amphibia, 13, 22, 31, 32, 39, 40, 48, 54

amphibians, vii, 2, 13, 27, 41, 43 amplitude, 204 anaerobe, 113 animal welfare, 62 anoxia, 24, 131 appetite, 53 aquaculture, 14 aquatic habitats, 2, 16, 18, 21, 24, 25, 50 aquatic life, 103 aquatic systems, 20, 23 Argentina, 1, 7, 8, 39, 43, 44, 48, 50, 51, 52 aromatic compounds, 21 aromatic hydrocarbons, 33 Asia, 104 aspartate, 10 aspiration, 83 assessment, 43, 59, 63, 84, 86, 89, 90, 91, 92, 93, 94, 95, 98, 108, 109, 127, 137, 141, 170, 191, 193, 196 assets, 96 atmosphere, 110, 116, 130, 131, 141, 172, 192 atrophy, 183 attachment, 26 Austria, 1 autonomy, 59, 70

B  bacteria, 10, 25, 26, 27, 32, 38, 133

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218

Index

base, 8, 28, 43, 58, 127 beetles, 209 Beijing, 95, 102 benefits, 122, 124, 125, 127 benzene, 21 bias, 8, 24 bifurcation theory, 82, 83 bioavailability, 137 biochemical processes, 104 biochemistry, 45 biodiversity, iv, vii, viii, xi, 2, 28, 36, 38, 39, 45, 50, 99, 100, 101, 195, 196, 197, 199, 202, 204, 205, 209, 210, 212, 214 biogeography, 8, 31 biological activity, 113 biological processes, 120, 121, 186 biological systems, 167 biomass, 12, 48, 63, 65, 75, 79, 107, 108, 109, 117, 118, 119, 120, 123, 128, 129, 130, 133, 134, 142, 159, 190, 202, 204, 205, 210, 213 biopolymer, 12 biosynthesis, 54 biotic, ix, 19, 38, 58, 63, 97, 100, 119, 140, 141, 150, 152, 159, 164, 165, 166 birds, 121 blood pressure, 61 body size, 100 Bolivia, 8, 30 bonds, 187 bounds, 191 Brazil, 7, 8, 41, 47, 49, 94 breakdown, 11 breathing, 119 breeding, 143 bryophyte, 45

C  cadmium, ix, 22, 139, 143, 150 calcium, 116, 123 carbohydrates, 10 carbon, 9, 10, 12, 16, 20, 21, 38, 45, 107, 117, 119, 130, 131, 190 carbon dioxide, 20, 117, 119, 130, 131

carotenoids, 171, 173 case study, 214 casein, 15 catalytic properties, 63 catastrophes, 82 catchments, 196, 200 catfish, 14 causal relationship, 84 causality, 71 cellulose, vii, 2, 5, 9, 10 ceramic, 128 challenges, 36, 53 changing environment, 26 chaos, 76, 77, 78, 79, 82, 145, 168 chemical, 10, 12, 16, 17, 24, 25, 27, 31, 32, 34, 40, 45, 61, 63, 94, 109, 113, 114, 116, 119, 121, 122, 123, 159, 181, 184, 186, 187, 190, 192, 196, 199 chemical characteristics, 113 chemicals, 90, 127, 129, 135 chemotaxis, 26 Chicago, 94, 100 Chile, 8, 55 China, 55, 102 chitin, vii, 2, 5, 9, 12, 33, 48 chitinase, 14 chitosan, 127 chlorine, 116 chlorophyll, 2, 8, 108, 134, 173, 175, 187 circulation, 15, 22, 53, 113, 129, 130, 131, 205 cities, 196, 198 clarity, 66, 84 classes, 4, 106, 176 classification, 29, 39, 54, 101, 106, 107, 193 cleaning, 134, 181, 184, 185, 204 climate, 110, 113, 116, 206 climates, 37 clone, 23, 49, 52 clusters, 185 CO2, 99 coherence, 79 collaboration, 85, 94 Colombia, 8, 41, 46

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Index colonisation, 79 colonization, 10, 11, 12, 19, 24, 25, 26, 27, 49 color, 106, 118, 122 communication, 86 communities, xi, 12, 24, 25, 41, 42, 45, 49, 54, 63, 65, 68, 69, 101, 129, 134, 141, 159, 195, 196, 197, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 215 community, 36, 43, 48, 55, 58, 66, 68, 70, 93, 98, 108, 132, 136, 152, 166, 187, 199, 206 compaction, 148, 149 competition, 18, 19, 25, 26 competitors, 25, 27 complement, 89 complex interactions, 23 complexity, 10, 59, 71, 75, 76, 78, 79, 97, 168 composition, 10, 16, 17, 24, 28, 66, 113, 119, 120, 159, 171, 181, 190 compounds, x, 9, 10, 17, 21, 75, 109, 114, 116, 121, 169, 181, 182, 187 compression, 146, 149, 150, 152, 154, 166, 177, 178 computer, 69, 168 conception, xi, 59, 79, 84, 146, 195, 196 conceptual model, 90, 91, 92 conductivity, 110, 127 configuration, 82 conflict, 90 conservation, 36, 59, 79, 94, 96, 98, 100, 196 constituents, 9, 21 construction, 102, 143, 173, 174, 177, 179, 181 consumers, 9, 16, 17, 104, 120, 121 consumption, 107, 113, 117, 119 contaminant, 90, 92, 127 contradiction, 73, 146 controversial, 15 cooling, 110, 111, 206 copper, 22, 121, 137, 181, 197 correlation, 22, 68, 108, 133, 188

219

correlations, 23 corrosion, 119 cost, 93, 127 covering, 18, 133 critical state, 209 critical value, 154, 165 criticism, 62 crops, 14, 52, 116 crystalline, 10 cultivation, 121 culture, 10, 16, 27, 29, 34, 54 cycles, 6, 18, 36, 63, 85, 97, 110, 112, 113, 144, 149, 154, 165, 166 cycling, 135 Cyprus, 55

D  damping, 14 danger, 114 data collection, 93 data set, 199 decay, 46, 72, 78 decomposition, 10, 11, 34, 43, 44, 45, 46, 48, 49, 116, 117, 118, 119, 131, 135 decomposition reactions, 118 deficiency, 104, 118, 129 deforestation, viii, 2, 21, 28, 105 degradation, 18, 33, 72, 75, 134, 196, 202, 204 denitrification, 121, 134 Denmark, 214 deposits, 109 depth, 110, 111, 112, 113, 125, 127, 129, 130, 131, 132, 134, 143, 190, 206 desiccation, 40 destruction, ix, x, 18, 80, 139, 140, 141, 142, 143, 145, 146, 148, 149, 150, 152, 156, 157, 161, 164, 165, 166, 167, 168, 186, 190, 206 destruction processes, ix, 140, 143, 146, 148, 150, 161, 164, 165, 190 detection, 156 developed countries, 121

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220

Index

diatoms, 6, 15, 40, 118, 202, 206, 209, 210, 212 diet, 27, 31 diffusion, 129, 132 digestion, 33 dimensionality, 157, 159, 166, 193 discharges, 104, 126 diseases, 13, 15, 16 disorder, 72 dispersion, 24, 115, 128, 178, 179 displacement, 148, 150, 154, 157, 165 dissolved oxygen, 19, 20, 22, 109, 114, 115, 118, 119, 127, 129, 130, 131, 132, 135, 143, 205 distress, 58, 59 distribution, vii, viii, 2, 6, 7, 8, 18, 19, 20, 21, 22, 23, 24, 25, 28, 31, 35, 36, 42, 44, 45, 48, 53, 55, 57, 70, 90, 120, 131, 132, 171, 198, 200, 204, 205, 212 diversification, 104 diversity, vii, 2, 7, 8, 11, 12, 17, 21, 23, 25, 28, 36, 38, 39, 42, 43, 46, 48, 49, 50, 53, 54, 59, 62, 68, 75, 82, 94, 200, 202, 205, 206, 210, 212 DNA, 6, 8, 28, 31, 43, 48 DNA sequencing, 28 dominance, 26, 130 dosing, 137 drainage, 24 drinking water, 118 drying, 37

E  ecological roles, vii, 2, 16 ecological structure, 73 ecological systems, 88, 89, 96, 98, 141 ecology, iv, viii, 28, 30, 33, 34, 35, 37, 40, 47, 54, 57, 58, 63, 68, 71, 76, 100, 168, 194 economic losses, 14 economic problem, 104 Ecuador, 8 editors, 136 Egypt, 36

electromagnetic, 171, 176, 177 elongation, 3 elucidation, 28 encyst, 26 energy, 9, 16, 17, 21, 24, 26, 63, 65, 72, 73, 74, 78, 97, 107, 110, 115, 117, 118, 132, 141, 142, 148, 156, 166, 192 energy supply, 110 energy transfer, 9, 17 England, 46 entropy, ix, 73, 74, 75, 140, 142, 146, 154 environment, ix, 10, 17, 18, 27, 28, 34, 47, 65, 71, 73, 74, 78, 85, 86, 87, 89, 90, 91, 95, 120, 132, 133, 140, 141, 166, 170, 172, 173, 193, 210 environmental conditions, 7, 23, 26, 164, 196 environmental factors, 19, 23, 25, 55, 86, 90 environmental policy, 89 Environmental Protection Agency, 33, 85, 101 environmental quality, 96 enzyme, 29 enzymes, 9 EPA, 90, 91, 92, 93, 101 EPC, 45 epidemic, 37 epidemiology, 44, 52 equality, 82 equilibrium, 63, 64, 66, 67, 73, 75, 76, 77, 78, 79, 82, 110, 111, 144, 185, 186, 187, 190, 191 equipment, 125, 126, 127, 133 ERA, 90, 91, 92, 93, 94 erosion, 116 eukaryote, 32, 52 eukaryotic, 3, 8, 15 Europe, 7, 14, 104, 200 European policy, 104 evaporation, 125 evidence, 12, 31, 35, 42, 69, 131, 150, 152, 206

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Index evolution, x, 3, 5, 33, 36, 40, 79, 83, 87, 99, 104, 105, 140, 141, 142, 154, 156, 159, 161, 165, 167, 205 exchange rate, 128 exclusion, 27, 109, 114 expenditures, 142, 148, 149, 165 exploitation, 79, 98 exposure, 89, 90, 92, 93, 144 external environment, 65, 71, 73 external influences, 75 extinction, 172 extraction, 121

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F  faith, 104 families, 5, 200 famine, 7 farm land, 58 fatty acids, 17, 34, 190 fauna, xi, 104, 120, 130, 190, 195, 196, 197, 200, 204, 206, 213 fertility, 109 fertilization, viii, 99, 103, 104 fertilizers, 21, 22, 116, 121 films, 26 filters, 118, 212 fish, 9, 12, 14, 15, 41, 92, 118, 119, 120, 130, 131, 132, 133, 135, 136, 143, 197, 202, 205, 206, 212, 213 Fish and Wildlife Service, 136 fisheries, 136 flagellum, 3, 5 flooding, 24 floods, 190 flora, xi, 104, 120, 195, 196, 197, 200, 206, 209, 212 flora and fauna, xi, 120, 195, 196, 197, 200, 206 fluctuations, 18, 22, 38, 76, 83, 183 food, vii, 2, 9, 15, 16, 17, 25, 27, 28, 31, 34, 37, 41, 42, 46, 68, 69, 114, 131, 133, 212 food web, vii, 2, 9, 15, 25, 28, 34, 37, 42, 46, 68, 69

221

force, 144 forest ecosystem, 97 formation, 72, 117, 118, 145, 149, 156, 157, 166, 171, 183, 193, 196, 200 formula, 173, 174 France, 136, 192 freedom, 89 freedom of choice, 89 freezing, 125 frequency distribution, 70 freshwater, vii, x, xi, 2, 3, 6, 9, 10, 11, 13, 14, 15, 19, 20, 21, 22, 25, 26, 28, 31, 36, 37, 38, 42, 43, 45, 46, 49, 50, 51, 55, 125, 141, 143, 144, 169, 181, 191, 194, 195, 199, 202, 211, 212 freshwater species, 51 fruits, 10 fungal metabolite, 27 fungi, vii, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 49, 50, 53, 54, 55 fungus, 6, 15, 16, 21, 22, 26, 31, 32, 33, 38, 39, 42, 46, 54

G  GDP, 65 genetic mutations, 80 genus, 35, 36, 202 geographical origin, 7 Germany, 136 germination, 24 global scale, 28, 88 global warming, 18 glucose, 10, 17 glutamate, 10 glycogen, 17 goods and services, 63 governance, 88 gracilis, 201, 204, 209, 210, 211 grazers, 17 grazing, 18, 25 green alga, 16, 118, 130, 132, 202, 212

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Index

gross domestic product, 65 grounding, 75 growth, viii, 10, 16, 17, 19, 20, 21, 22, 26, 27, 29, 37, 38, 46, 54, 59, 63, 74, 76, 103, 104, 105, 106, 107, 108, 110, 118, 120, 122, 123, 125, 126, 128, 129, 130, 131, 132, 206 growth rate, 20, 107, 128, 132 growth temperature, 19

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H  habitat, 6, 45, 129, 131 habitats, vii, 2, 3, 6, 9, 10, 16, 18, 19, 20, 21, 23, 24, 25, 26, 49, 50, 210 harvesting, 121 hazards, 89, 90 health, vii, viii, 57, 58, 59, 61, 62, 63, 64, 65, 68, 70, 74, 75, 76, 79, 82, 84, 85, 86, 87, 88, 89, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 114, 120 heavy metals, x, xi, 22, 127, 169, 195, 196, 197, 200, 202 hemisphere, 210 hemp, 10 heptane, 21 herbicide, 122 herbivorous vertebrate, vii, 2, 5, 16 history, 30, 54, 97, 98 homeostasis, 59, 64, 191 Hong Kong, 36, 39 hormone, 43 host, 6, 13, 15, 16, 35, 39, 45 House, 194 human, viii, 13, 18, 21, 53, 57, 58, 63, 86, 87, 88, 89, 90, 93, 97, 102, 103, 109, 110, 114, 115, 116, 119, 156, 170 human actions, 58 human activity, viii, 57, 58 human health, viii, 59, 86, 87, 88, 89, 93, 102, 103 Hunter, 66, 97 hydrocarbons, 21, 33 hydrogen, 41, 43, 109, 114, 117, 118, 119 hydrolysis, 12, 15

hydroxide, 123, 124 hypothesis, 63, 68, 92, 93, 101, 194 hypothesis test, 92 hypoxia, 24 hysteresis, ix, x, 140, 162, 168, 170, 186, 191, 192, 194

I  ideal, 94 identification, 23, 28, 32, 192 identity, viii, 35, 57, 61 illumination, 189 image, x, 170, 176, 183, 184, 185, 187, 191 images, x, 162, 170, 172, 174, 176, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 191 imagination, 179 imbalances, 117, 118, 134 immigrants, 200 immobilization, 134 impact assessment, 86 improvements, 122 independent variable, 172 India, 29, 38, 41, 44, 45, 47, 48, 50, 57 individual characteristics, 171 individuals, 206 induction, 93 industrialisation, 105, 199 industry, 170 inertia, 186 infancy, 76 infection, 14, 15, 17, 24, 32, 50 infestations, 13 inhibition, 129, 149 initial state, 146, 150, 152, 154, 155, 157, 162, 182, 186, 187, 191 injury, 89 insects, 9, 12, 13 insertion, 5 integration, viii, 57, 61, 93, 102 integrity, 58, 61, 79, 82, 95, 97, 102 integument, 206 interface, 87, 113, 126, 129 intermediaries, 11

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Index internal processes, 65 intervention, 86, 110, 121, 134 invasions, 102 inversion, 154 invertebrates, vii, 2, 11, 13, 30, 34, 37, 133 ions, 22, 116, 121 Iowa, 41 Iraq, 48 Ireland, 7 iron, 119, 120, 123, 127, 129, 130, 131, 132, 198 irrigation, 116 isolation, 28, 33, 34 issues, viii, 62, 103, 124

J 

223

lens, 89 lesions, 15 leucine, 10 life cycle, 6, 8, 16, 18, 26, 36 lifetime, 89 light, 24, 40, 115, 119, 129, 133, 141, 172, 173 light beam, 172 light scattering, 173 lignin, vii, 2, 9 linear model, 64, 67 linear systems, 71, 73, 75, 76 litigation, 90 livestock, 105, 121 lysine, 5

M 

Japan, 53

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K  keratin, vii, 2, 9 kerosene, 21 kill, 22 kinetics, 107 Kola Peninsula, 198, 213

L  lactic acid, 45 lakes, viii, 22, 24, 34, 41, 103, 104, 106, 107, 109, 110, 112, 114, 118, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 197, 199, 200, 202, 204, 205, 206, 209, 210, 213, 214, 215 landscapes, 101, 213 larvae, 15, 47, 202, 205, 209 laws, x, 83, 140, 141 leaching, 10 lead, 6, 18, 66, 71, 82, 88, 108, 116, 118, 119, 122, 124, 126 leakage, 125 legislation, 61

macromolecules, 101 magnitude, 39, 67, 68 majority, 12, 199 maltose, 10 mammals, 14 man, x, 105, 169 management, viii, 44, 57, 59, 61, 62, 83, 86, 87, 88, 90, 93, 94, 95, 97, 101, 102, 108, 122, 125, 129, 131, 135, 137 manganese, 22, 117, 119, 130, 132 mapping, 82 marine environment, 6, 15, 33 mass, 48, 110, 111, 112, 113, 116, 123, 124, 130, 142, 171, 177, 178, 179, 193, 202 materials, xi, 12, 73, 171, 195 matrix, 177, 178, 179 matter, iv, ix, 9, 10, 11, 21, 83, 94, 109, 113, 114, 116, 117, 118, 119, 121, 122, 131, 133, 134, 139, 141, 143, 145, 159, 171, 173, 186 measurement, x, 92, 170, 172, 173 measurements, x, 106, 124, 145, 158, 169, 173, 177, 184, 188, 189, 192, 199 media, 46 median, 92 Mediterranean, 48

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224

Index

melt, 24 mercury, 143, 181 MES, 39 metabolism, 20, 38, 59, 63 metabolites, 27, 32 metabolized, 75 metabolizing, 10 metals, x, xi, 22, 45, 127, 152, 170, 195, 196, 197, 200, 202 meter, 24 microbiota, 27 microhabitats, 7, 25 microorganisms, vii, 1, 3, 6, 8, 9, 10, 18, 21, 25, 26, 27, 28, 53, 90 Middle East, 7 mineral resources, 196 mineralization, 190, 204 mitochondria, 5 mixing, 107, 110, 112, 113, 128, 129, 130, 131, 137 modelling, 70, 76, 82, 83 models, ix, 15, 25, 63, 66, 67, 70, 87, 89, 92, 95, 102, 108, 139, 140, 143, 181 modernization, 134 modifications, 62 modules, 159 modus operandi, 131 molds, 33, 41, 54 molecular biology, 33 molecular mass, 171 Mongolia, 95 morbidity, 38 morphology, vii, 2, 42 mortality, 15, 31, 38, 48, 119 mortality rate, 15 Moscow, 139, 167, 168, 193, 195, 213, 214 Moses, 128 mussels, 209 mutations, 80, 81 mycology, 34, 47

N  naphthalene, 33 National Research Council, 99

natural evolution, 104, 205 natural habitats, 19, 210 natural polymers, 9 natural resources, viii, 57 necrosis, 15 negative effects, 122, 127, 128, 135 nematode, 15 neutral, 20, 128 New South Wales, 42 New Zealand, 51, 52, 96, 99 nickel, 197, 202 Nigeria, 29 nitrification, 121 nitrite, 9 nitrogen, 9, 10, 12, 21, 35, 55, 104, 107, 109, 114, 116, 120, 121, 134, 190 nitrogen compounds, 121 nonequilibrium, 95, 97, 142 nonequilibrium systems, 142 North America, 7, 45, 101, 104 Norway, 213 NPS, xi, 195, 197, 205, 209 null, 78, 92 null hypothesis, 92 nutrient, 10, 16, 17, 18, 19, 23, 26, 38, 47, 105, 107, 108, 120, 122, 123, 124, 125, 126, 127, 128, 130, 133, 134, 197, 199, 204 nutrient concentrations, 18, 19, 107, 108, 125, 133 nutrient enrichment, 105 nutrients, viii, 9, 17, 18, 19, 23, 24, 103, 104, 105, 106, 107, 109, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 128, 129, 132, 133, 134, 135 nutrition, 2, 54

O  OH, 124 oil, 15, 21 omission, 173 optical parameters, 183, 184 optical properties, 119

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Index organelles, 47 organic matter, ix, 9, 10, 11, 21, 109, 113, 114, 116, 117, 118, 119, 121, 122, 131, 133, 134, 139, 141, 143, 171, 173, 197 organism, 61, 119, 129 organize, 57 orthogonal functions, 178 orthogonality, 178 oscillation, 69, 77, 150 oxidation, 113, 114, 117, 129 oxygen, 19, 20, 21, 22, 23, 45, 104, 109, 113, 114, 115, 117, 118, 119, 127, 129, 130, 131, 132, 135, 143, 205 oxygen consumption, 117, 119

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P  P. ramorum, 14 Pacific, 7, 31 parallel, 186, 188 parasite, 14, 15, 17, 29 parasites, vii, 2, 6, 9, 13, 14, 15, 17, 22, 24, 30, 35, 37, 41, 121 participants, 87 pathogens, 13, 14, 15, 16, 30 pathology, 40 pathways, 66, 82 PCR, 8, 28, 43 periodicity, 23, 29, 38, 44, 48, 165 pH, 19, 20, 22, 23, 35, 37, 42, 124, 127, 130, 197, 209, 210, 211 phenylalanine, 10 Philadelphia, 168, 194 phosphate, 9, 41, 136 phosphorous, 9, 21, 107, 127 phosphorus, 44, 104, 106, 107, 108, 109, 114, 115, 116, 120, 121, 123, 124, 126, 127, 128, 129, 131, 132, 134, 137, 190, 197, 204, 205, 207 photosynthesis, 113, 119, 129 phylum, 4, 5, 40 physical environment, 63, 87 physics, 141, 167, 176 physiology, 22, 37

225

phytoplankton, 9, 13, 39, 41, 100, 106, 107, 108, 114, 118, 120, 129, 132, 133, 134, 136, 137, 171, 173, 175, 180, 183, 184, 185, 187, 199, 202, 204, 206 pigmentation, 38 planets, 192 plankton, 42, 46, 133, 173, 183, 187, 212 plants, viii, 6, 13, 14, 15, 22, 24, 62, 103, 104, 117, 118, 119, 120, 121, 122, 196 PM, 33, 37, 39, 40, 41, 42, 44, 45, 47, 53, 54 policy, 61, 87, 89, 95, 104 pollen, 9, 10, 16 pollutants, ix, x, 33, 139, 141, 143, 169, 170, 181, 183, 184, 204 pollution, viii, ix, x, xi, 2, 18, 22, 28, 38, 53, 104, 110, 140, 143, 151, 167, 169, 170, 183, 187, 190, 193, 194, 195, 196, 197, 200, 202, 203, 204, 210, 212, 214 polyacrylamide, 127 polycyclic aromatic compounds, 21 polymer, 12 polymers, 9 ponds, 20, 45, 121, 143 population, 9, 18, 26, 27, 30, 31, 44, 68, 69, 70, 92, 102, 107, 202, 206 population growth, 107 population size, 9, 18, 26 portraits, 144, 145, 148, 151, 152, positive correlation, 22 potassium, 116 potato, 7, 16, 44 precipitation, 116, 119, 121, 123, 204 predation, 130, 133 predators, 26, 30, 212 predictability, 80 principles, viii, 57, 71, 76, 79, 87, 96, 100, 177, 194 probability, 69, 89, 94, 172 producers, 15, 16 productivity growth, 118 professionals, 28 project, 127 proliferation, 117 proline, 10

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226

Index

proposition, 69 protected areas, 32 proteins, 9, 17, 30 public health, 61, 89, 94 public support, 62 pumps, 190 purification, 53, 157, 159

Q  quasi-equilibrium state, 185

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R  radiation, x, 75, 90, 110, 111, 135, 170, 171, 172, 173, 176, 177, 181, 193 Radiation, 172 rain forest, 31, 68 rainfall, 106 reactions, 113, 118, 119, 143 real time, 191 reality, 141, 157 reasoning, x, 170 receptors, 90 recommendations, iv, 93 reconstruction, 150, 154, 165 recovery, 66, 84, 166, 186 recreational, 122 recurrence, 186, 187, 191, 192 recycling, 9, 134 refractive index, 171, 173 regions of the world, 7 regression model, 108 rehabilitation, vii, viii, 103, 104, 122, 123, 124, 125, 126, 127, 128, 130, 131, 132, 133, 134 rehabilitation program, 122 reinforcement, 148 rejection, 179 relevance, 14, 34, 92 reliability, 167 relict species, 200 remediation, 133, 136 remote sensing, 95, 172, 173, 186, 192, 193

repair, 59 reproduction, 29, 35 reputation, 125 requirements, 46, 191 researchers, 106 reserves, 26, 52 residues, 21 resilience, 59, 67, 68, 70, 74, 75, 81, 84, 94, 98 resistance, 62 resolution, 171 resource management, 61 resources, viii, 25, 57, 65, 79, 80, 148, 193, 196, 214, 215 respiration, 22, 113, 142, 149, 165 response, ix, x, xi, 19, 37, 67, 84, 90, 92, 140, 143, 154, 159, 164, 170, 183, 186, 187, 191, 192, 195 restoration, 98, 108, 122, 126, 134, 135, 136, 190 ribosomal RNA, 36, 53 rights, 62 risk, 89, 90, 91, 92, 93, 94, 124 risk management, 90, 93, 94 risks, 90, 92, 93 RNA, 53 RNAs, 36 Romania, 103 root, 14, 16, 52 rotifers, 30, 41, 199, 202, 206, 212 Royal Society, 32 rules, 194 runoff, 23, 190 Russia, vii, xi, 139, 140, 169, 195, 196, 199, 213

S  safety, 123 salinity, 27, 29 salmon, 14, 15, 22, 55, 200, 210, 212, 213 salt concentration, 204 salts, 9, 123, 124, 190 saturation, 107, 205 savannah, 102

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Index scale system, 96 scattering, 171, 172, 173, 192 science, viii, 48, 57, 213 scientific knowledge, 90 scope, 59, 66, 82 seasonal changes, 20, 53, 189 seasonality, 23, 39, 45 security, 89, 102 sediment, 109, 113, 120, 121, 122, 123, 125, 126, 127, 129, 134, 135 sedimentation, 107, 109, 126, 183 sediments, x, 122, 123, 124, 125, 126, 127, 129, 131, 141, 143, 159, 169, 181, 190 selectivity, 28 sensing, 95, 172, 173, 186, 192, 193 sensitivity, 119, 156, 197 sequencing, 28 serine, 10 services, iv, 63, 88, 89, 95 settlements, 196 sewage, 21, 116, 121, 197, 204 shallow lakes, 124, 134, 136 shape, 54, 128 shear, 110 shellfish, 119 shock, 150, 209 showing, 176, 198 side effects, 122 signs, 131 silicon, 107, 116, 120 silver, 121 simulation, 46, 69, 136 simulations, 83 skin, 9, 10, 15, 27, 38 sludge, 121, 126, 127, 190 SO42-, 117 social relations, 89 society, viii, 63, 89, 90, 103 sodium, 123, 143, 152 SOI, 172, 176, 179, 180, 181, 182, 183, 185, 186, 187 solubility, 129 solution, 122, 124, 143, 180 South America, 7, 8, 104 Spain, 32, 55

227

specialists, 28, 171 species richness, 15, 23, 25, 42 spore, 2 St. Petersburg, 167, 168 stability, viii, ix, x, 15, 46, 57, 58, 59, 64, 67, 68, 69, 70, 75, 79, 82, 96, 98, 140, 145, 154, 157, 159, 167, 181 stabilization, 121 stable states, 165 stakeholders, 88, 90, 94 standardization, x, 140, 167 starch, 17 stars, 192 stasis, 79 states, ix, x, 63, 66, 67, 78, 79, 82, 98, 136, 140, 141, 143, 146, 152, 154, 156, 157, 159, 160, 161, 165, 166, 167, 171, 176, 177, 182, 187 statistics, 178 steel, 119 sterols, 17 storage, 17, 74, 75, 119, 121, 125, 127, 190 storms, 93 stratification, 22, 109, 110, 111, 113, 114 stress, 24, 25, 26, 42, 58, 59, 67, 69, 100, 187, 190, 199 stretching, 104 striatum, 27 structural changes, 75 structural characteristics, ix, 140, 164 structure, viii, ix, 15, 28, 43, 55, 57, 59, 70, 72, 73, 74, 78, 79, 84, 107, 108, 111, 113, 116, 140, 141, 148, 149, 150, 152, 156, 159, 164, 165, 166, 171, 174, 186, 196, 199, 200, 202, 204, 205, 206, 212, 214 substrate, 10, 12, 26, 27, 34 substrates, vii, 2, 10, 11, 12, 18, 20, 24, 25, 26, 27, 28 succession, 58, 63, 64, 65, 66, 67, 71, 79, 135, 141 sucrose, 10, 17 sulfate, 121, 123, 124, 143, 152 sulphur, 9 Sun, 141

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228

Index

surface layer, 113, 205 surfactants, 53 survival, 18, 20, 21, 22, 27, 69, 74, 76, 172 susceptibility, 90, 92 sustainability, 62, 86, 87 Sweden, 126 symptomatic treatment, 122 symptoms, 118 syndrome, 15, 43, 58, 59 synthesis, 88, 96

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T  tanks, 38, 119, 181 taxa, 2, 7, 8, 12, 23, 28, 29, 40, 200 taxonomy, 5, 28, 29, 36, 52, 199 techniques, 6, 28, 122, 123, 124, 125, 127, 128, 130, 131, 132, 133 temperature, 19, 21, 22, 61, 110, 111, 112, 113, 119, 127, 128, 130, 131, 189, 197, 206, 209 territory, 197, 202 testing, 46, 92, 98 thermal energy, 110 thermodynamic equilibrium, 73, 76, 78 thermodynamic parameters, ix, 139, 149, 167 thermodynamic properties, 98 thermodynamics, 72, 141, 143 third dimension, 81 threats, 36 tides, 106 time lags, 93 toluene, 21 topology, 159 toxic substances, 22, 119, 120, 132, 144 toxicity, 21, 45, 124, 127 toxicology, 92 trade, 13 traditions, 75 training, 28, 115 traits, xi, 195 trajectory, 63, 66, 71, 80, 82, 83, 150, 152, 154, 159, 162, 165, 166, 176, 182, 183, 185, 187

transformation, 33, 107, 141, 156, 178, 186, 196 transformations, 72, 186, 214 transmission, 33 transparency, 108, 118, 124, 128, 130, 134 transport, 107, 109, 125, 129 treatment, ix, 118, 120, 121, 122, 123, 124, 126, 127, 140, 143, 146, 154, 157, 161, 190, 192 trophic state, 108, 109, 135 tundra, 99, 197, 200, 213, 215 turbulent mixing, 110 turnover, 113

U  ultrastructure, 43 underlying mechanisms, 63 uniform, 113 United, 31, 46, 85, 136 United Nations, 85 United States, 31, 46, 136 urban, 58, 110 urbanization, 105 USSR, 193, 213, 214 UV light, 40

V  valuation, 98 variables, x, 65, 70, 80, 170, 172, 173, 187 variations, 112 vector, 13, 157, 158, 159, 177, 178 vegetables, 13 vegetation, 21, 24, 42, 48, 101, 106, 109, 119, 122, 136, 142 velocity, 24 Venezuela, 8, 41 vertebrates, vii, 2, 5, 16 Viking, 96 viruses, 15, 29, 33, 47, 135 visualization, 182 vitalism, 71 vulnerability, 196

Aquatic Ecosystems, edited by Sheila A. Browne, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

Index

W 

World Health Organization, 85 worldwide, 3, 6, 7, 8, 13, 14, 18, 28 worry, 96

Y  Yale University, 95 Y-axis, 166 Yugoslavia, 106

Z  zinc, 22 zooplankton, 17, 100, 108, 119, 120, 130, 132, 133, 134, 190, 197, 199, 202, 204, 206, 209, 212, 214, 215 zoosporangia, 20 zoospore, 2, 5, 20, 22, 23, 24, 43, 53

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

Wales, 42 Washington, 33, 95, 96, 97, 99, 100, 101, 128 waste, 126, 133, 200, 202, 204 waste water, 200, 202, 204 wastewater, 120 water ecosystems, 181, 193, 207, 208, 214 water quality, viii, ix, 61, 103, 104, 114, 116, 118, 119, 122, 125, 126, 129, 130, 134, 135, 193, 199, 215 wavelengths, 176 web, 9, 15, 37, 41, 42, 46, 68, 69 weight reduction, 125 welfare, 62, 187 well-being, 61, 88, 89 West Africa, 7 woodland, 42, 44, 47, 48

229

Aquatic Ecosystems, edited by Sheila A. Browne, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,