Ecology of Fire-Dependent Ecosystems: Wildland Fire Science, Policy, and Management 2020950459, 9781138597174, 9781138597150, 9780429487095

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Ecology of Fire-Dependent Ecosystems

Ecology of Fire-Dependent Ecosystems

Wildland Fire Science, Policy, and Management

Devan Allen McGranahan Carissa L. Wonkka

First edition published 2021 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2021 Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright. com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@ tandf. co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Control Number: 2020950459 ISBN: 9781138597174 (hbk) ISBN: 9781138597150 (pbk) ISBN: 9780429487095 (ebk)

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To our folks Cindy, Therese, and the Allens.

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Contents

Chapter 1 

Introduction 1

A BRIEF HISTORY OF FIRE IN THE EARTH SYSTEM Natural burning

2

Anthropogenic burning

3

Industrialised fire

3

A MODERN APPROACH TO WILDLAND FIRE SCIENCE

Section I

1

5

Wildland fire science literacy

5

New perspectives

7

Fire Fundamentals

Chapter 2 

From flame to flame front

THE CHEMISTRY AND PHYSICS OF FIRE

13 14

Phases of combustion

15

Heat transfer and propagation

17

FUELS IN THE WILDLAND FIRE ENVIRONMENT

19

Fuel size classes

19

Fuel moisture

20

The fuelbed and wildland fuel structure

21

FIRE BEHAVIOUR

22

Fire spread and the flame front

23

Observing fire behaviour

23

Wildland fire anatomy

24

Environmental factors

26

Extreme fire behaviour

30

Chapter 3 

Fire regimes past and present

DEFINITIONS AND DESCRIPTIONS

33 33

Core parameters

35

Modulators of fire regime

42

Wildland fire effects

44

THE GLOBAL FIRE FOOTPRINT

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viii  Contents

Extant fire regimes

45

Reconstructing past fire regimes

48

Chapter 4 

The role of humans

51

A BRIEF REVIEW OF HUMANS AND FIRE

51

Indigenous burning

52

Fire suppression—from the colonial era to Smokey Bear

53

Anthropogenic alterations

54

FIRE, MANAGEMENT, AND CHANGE

59

Range of variability

59

Future fire regimes

60

Section II Fire Effects Chapter 5 

Fundamentals of wildand fire impacts and ecology

THE IMPORTANCE OF SCALE

65 65

Relevant ecological sub-fields

66

Fire effects and scale

69

CULTURAL RESOURCES

71

Direct effects

71

Indirect effects

71

FIRE-ADAPTIVE TRAITS

72

Evolution and natural selection

73

General considerations

74

The origin of fire-adaptive traits

78

Chapter 6 

Soil properties

FIRE, HEATING, AND SOIL PROPERTIES

85 85

Fire effects on soil physical properties

86

Fire effects on soil chemical properties

89

NUTRIENT POOLS

89

Considerations for soil nutrient research

90

Fire effects on nutrient pools

91

SOIL ORGANISMS AND MINERALISATION

96

Soil microbes

96

Mineralisation rates

98

Chapter 7 

Water and the atmosphere

EROSION, AQUATIC IMPACTS, AND WATER RESOURCES Post-fire run-off and erosion

99 99 99

Fire effects on aquatic ecosystems

101

Fire effects on water quality and supply

102

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Contents  ix

AIR, WEATHER, AND CLIMATE

103

Fire effects on air quality

104

Fire effects on weather

105

Climate-fire interactions

107

Chapter 8 

Individuals and populations

DIRECT EFFECTS ON INDIVIDUALS

111 111

General considerations

112

Plant-specific responses

113

Animal-specific responses

115

POPULATION-LEVEL IMPACTS

116

Plant population dynamics

116

Indirect effects on animal populations

120

CONDUCTING ROBUST POPULATION ECOLOGY

126

Metapopulation dynamics

126

Animal-specific considerations

126

First-order fire effects models

127

Demographic models

128

Chapter 9 

Pyrodiversity

BIODIVERSITY AND COMMUNITY ECOLOGY

131 132

Succession and assembly

133

Emergent properties of biodiversity

134

Quantifying biodiversity

134

Evidence for pyrodiversity

138

COMMUNITY-LEVEL INTERACTIONS

140

Interactions among disturbances

140

Interactions among community members

142

THE LANDSCAPE PERSPECTIVE

145

Patchiness

146

The functional role of heterogeneity

149

Section III Human Dimensions Chapter 10  Cultural connections to fire

153

FIRE AND EARLY HUMAN CULTURE

153

Early human fire use

154

The role of fire in shaping human diet

157

Primate response to fire

159

Implications of human fire control

160

LOCAL AND INDIGENOUS FIRE USE

162

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Historical and intact western fire cultures

162

Grey areas between sanctioned and unsanctioned ignitions

165

Extant indigenous and non-western fire cultures

166

Chapter 11  Institutional management and policy HISTORY OF US FIRE MANAGEMENT POLICY

171 171

Establishment

172

Systemisation and centralisation

173

Reevaluation

175

Redirection and reorganisation

177

INTERNATIONAL FIRE MANAGEMENT POLICY

178

Africa

178

Mediterranean Europe

182

Australia

183

Chapter 12  Coexisting with wildland fire LIVING WITH FIRE

185 185

The coupled socio-ecological system

186

Grass roots fire management

187

Fuel management and resource protection

188

THE WILDLAND-URBAN INTERFACE

189

A sprawling problem

189

Building fire-safe communities

191

WILDLAND FIRE USE: BARRIERS AND OPPORTUNITIES

192

Norms, attitudes, and perceptions related to fire

192

Risk and risk management

193

Regulatory restrictions

197

About the authors

200

References cited

201

Image attributions

245

Index

249

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Foreword Years ago now I remember when Devan McGranahan, as a visiting postdoc at my institution, captivated a class of third year students with his elegant explanations of fire behaviour. Having spent time in the Midwestern United States conducting prescribed burns and on farms using fire as a management tool in Africa, he had first-hand anecdotes to help reinforce his introduction to this fascinating topic. Devan and his co-author Carissa Wonkka bring the same fresh approach to this text on fire and inspire similar enthusiasm from the reader. Those of us who study and work with fire have been somewhat taken aback by the expanding interest and relevance that this field has to people today. Society at large has woken up to the fact that fire interacts with their lives and our biosphere in myriad ways and no one I meet at dinner parties these days changes the topic of conversation when I bring up what I do. However, that does put us in a novel situation: it is all very well to provide empirically justified warnings of changing fire regimes, extreme fires, and feedbacks to climate, biodiversity and livelihoods. It is quite another thing to have the world turn around and stare you in the face and ask—well, what should we do about it? What this book achieves is to provide existing fire practitioners and scientists, as well as future generations of students, with the tools and insight to make our knowledge useful. One of the key problems with discussions on fire today—both within academia and in society at large—is lack of context. Public outcry over the deforestation fires in the Amazon in 2019 morph into outrage at the savanna fires burning across southern Africa that are essential to sustain ecosystem health. Predictions of feedbacks between drought and fire in the boreal forest are applied inappropriately in ecosystems where drought tends to decrease the area burned, resulting in confused policy and suspicious policy-makers. No one really wants to know about nuance: trying to add nuance to debates in the media or at scientific conferences can be met with dismissal. Devan and Carissa, with their wide range of field experience and immaculate referencing, provide context without confusing or frustrating the reader, and they reinforce this message with simple summary tables and figures based on empirical data from a range of situations. One example is a table in which they summarise five broad categories of human impacted fire regimes, and help the reader to understand why our interventions can result in either too much or not enough fire in an ecosystem. As a forward-looking contribution to the fire literature, this book does a

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xii  Foreword

great job of highlighting the range of creative approaches that people today are exploring to live with and manage fire in the complex socio-ecological systems we have created. Fire science is full of “wicked problems”—issues that are difficult to solve because of incomplete, contradictory, and changing requirements. However, as we emerge from the befuddled confusion of colonial anti-fire policies that reverberated around the globe, it appears that there might be some constructive approaches that can be taken. The urgency of finding these solutions in a world where fire regimes and extreme fire events are rapidly changing is not lost on the authors, and physicists, ecologists, climate modellers, firefighters, and social and economic scientists all have a role to play. Living in an environment where fire brings life and renewal and sustains human livelihoods and biodiversity makes me sensitive to negative portrayals of fire. This textbook is brimming with intriguing ecological stories of how life has evolved with and diversified within the varied fire regimes that are experienced on earth. Moreover, the book places itself as a communication between students, fire scientists, and fire fighters, and each of these groups will find some familiar ground, and some challenging aspects in this text: something which ultimately will help to bring us closer together and enrich our different approaches to understanding and managing our changing planet.

Sally Archibald Johannesburg September 2020

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CHAPTER

1

Introduction

Fire has flourished on Earth for nearly 500 million years. Fire began with the origin of terrestrial vegetation, which provided two critical components: fuel, and atmospheric oxygen. Although fire appeared in various forms through the eons, the nature of fire consisted generally of a simple chemical reaction turning plant material and oxygen into heat, light, and gasses whenever a spark was introduced to a sufficient amount of dry vegetation. Humans transformed fire into a phenomenon. Fire not only shaped communities but pushed ecosystems beyond previous boundaries; fire cooked food and stoked cultures; and now, fire has altered the atmosphere that brought it into being. As human capacity to understand the Earth system developed, so too did understanding of fire chemistry and physics. But understanding the nature and effects of fire as an ecological disturbance has been slower, hampered by the complexity of the dynamic interactions between vegetation and climate and by fear of the destruction fire can bring. We intend for this book to help those who study, manage, and use wildland fire to develop new answers and novel solutions, based on an understanding of how fire functions in natural and social environments. We review literature, synthesise concepts, and identify research gaps and policy needs. We seek to help readers develop the knowledge base to design and conduct robust wildland fire research projects and critically interpret and apply fire science in any management, education, or policy situation.

A BRIEF HISTORY OF FIRE IN THE EARTH SYSTEM Fire historian Stephen Pyne defined Three Fires to describe the progression of fire on Earth:

• First Fire—natural burning in an oxygen-rich atmosphere, which shaped today’s geographical distribution of species and ecosystems.

• Second Fire—anthropogenic fire, when humans learned to use fire for their advantage. Long periods of intentional human burning af-

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2  Ecology of Fire-Dependent Ecosystems

fect ecological structure and composition today, and humans still use Second Fire for the same reasons as their ancestors.

• Third Fire—industrialised fire, when fire moved into novel habitats like furnaces, engines, and bombs. In Third Fire, fire went from simply combining vegetation, heat, and oxygen to the exploitation of concentrated, high-energy fossil fuels, whose byproducts will affect the distribution and composition of life on Earth into the future.

Natural burning The history of fire on Earth is limited to what can be reconstructed from the geologic record. Evidence consists largely of fossilised charcoal, called fusian (Fig. 1.1), and other charcoal deposits found in sedimentary layers.

James St. John CC BY 2.0

Figure 1.1: Fusian is fossilised charcoal, apparent here in a chunk of weathered coal from the Middle Pennsylvanian, found in Ohio, USA.

Earth was probably fire-free until ca. 400 million years ago, prior to which fire was impossible without two major components: fuel and oxygen. The evolution and global spread of terrestrial plants addressed both issues, providing vegetation biomass for fuel and oxygen as a byproduct of photosynthesis, which then accumulated to levels in the atmosphere sufficient for combustion. The first fusian records date to the late Devonian, just before substantial deposits from the beginning of the Carboniferous period (Scott 2000). Alternating periods of fire activity and inactivity generally track fluctuations in atmospheric oxygen levels (Pausas & Keeley 2009). Fire was among the dynamic ecosystem processes that changed in response to major shifts in climate worldwide throughout the Quaternary period, which began nearly 3 million years ago and continues through the present. There is considerable evidence of fire from the Quaternary period in the geologic record. Fire histories can be inferred from biological clues into broad climate and ecosystem patterns such as pollen cores taken from the bottom of ancient lakes and isotopic ratios of fossilised creatures like mollusks. Many of these records point to dramatic but cyclical variability in plant community composition, with concurrent variability in the frequency and type of fires through the pre-modern epoch known as the Pleistocene. The Pleistocene shaped the current distribution of species, ecosystems, and processes. Climatic variation during the Pleistocene was driven by waxing and waning glaciers, which were so prevalent that the Pleistocene is commonly known as the Ice Age. During periods of glacial maxima, Earth’s water was bound up as ice, sea levels dropped, and global climate became arid. Under these conditions, grasslands and savannas spread worldwide. The forests that dominated the early Eocene slowly opened up as the climate warmed and dried. Through the Miocene and Pliocene, grasses adapted to a warmer, drier climate, and fire drove their evolution and spread (Hoetzel et al. 2013, Scheiter et al. 2012). By the Pleistocene, grass-dominated, fire-prone ecosystems characterised much of Earth’s terrestrial surface not covered by ice. It is likely that by evolving fire-tolerant traits grasses themselves promoted fire, and as if playing leap-frog, fireadapted grasslands and fire itself made incursions into areas long held by forests.

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Introduction

 3

Anthropogenic burning Another consequential product of the Pleistocene was humans—humans who used and promoted fire as they followed the worldwide spread of grasslands and savannas. While many animals have adapted to fire, humans are unique in having captured fire and put it to work for them. Fire played a central role as human culture and technology developed, and humans applied novel uses of fire everywhere they settled. Since the Pleistocene, anthropogenic fire has modified landscapes worldwide, although changes are tempered by the local biota and human culture. The archaeological record cannot say how early hominins interacted with natural fire, but primates today suggest it was calm and utilitarian. In the field, chimpanzees respond to grass fires as part of their daily adventure: a lookout climbs a tree and vocalises to others before they file through the flame front at a point with low fuels and small flames (e.g., Pruetz & LaDuke 2010). And in the laboratory, great apes prefer cooked food (Wobber et al. 2008). It is easy to imagine how early hominins engaged natural fire and brought it back to the hearth (Gowlett & Wrangham 2013).

Ian Sewell CC BY-SA 2.5

Figure 1.2: Two San men in Botswana start a fire by hand. Anthropological research of extant hunter-gatherer cultures provides insight into how prehistoric humans made use of fire.

Domesticated fire both served and catalysed human culture. The human brain developed for social interaction, and after the light of the workday waned, humans gathered around fires for storytelling and communal bonding (Dunbar 2014). In extant Stone Age cultures like those of the Kalahari (Fig. 1.2), night-time conversation shifts towards stories, songs, and religious ceremonies, and is conducted around the fire (Wiessner 2014). Humans probably first burned landscapes for the convenience of stimulating wild food production and attracting game. Intentionality in fire use developed into what is now known as “fire-stick farming” (Jones 1969), which predated the sedentary Neolithic agriculture that persists in indigenous agroforestry practices worldwide: deliberate, low-intensity fires remain components of tropical home garden traditions and Australian Aboriginal culture today (Nigh 2008). In the tropics, swidden agriculture—or, colloquially, “slash and burn”—relied on fire to clear early-succession vegetation and restore soil fertility after deliberate fallow periods (Fig. 1.3). In Europe, fire was similarly used to clear land and manage field stubble (Fig. 1.4).

Alzenir Ferreira de Souza CC0

Figure 1.3: Tropical agriculture worldwide continues to use fire to clear fallow land in preparation for planting. Fire clears vegetation and increases soil nutrient levels, at least temporarily.

Industrialised fire Third Fire ignited the Industrial Revolution by unlocking fossil fuels like coal, natural gas, and oil—the mineralised remnants of biomass that escaped First Fire in the Carboniferous period. Millions of years of geologic pressure turned lignin into lignite, which fueled dense cities and productive societies. The Industrial Revolution created new habitat for fire in boilers, stoves and furnaces, then in internal combustion engines and bombs.

public domain

th

Figure 1.4: A 19 -century print by Berndt Lindholm (1841–1914) depicts burning to clear land in Iisalmi, Finland.

The consequences of Third Fire affect the pattern and process of both natural and anthropogenic fire in the modern age. Broadly speaking, the power Third Fire gave humans to spread across the world and alter their environments affects wildland fire in three ways:

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4  Ecology of Fire-Dependent Ecosystems

Figure 1.5: Examples of fire suppression efforts in California, USA. (L) An air tanker drops chemical retardant on the Rim Fire in 2013. (R) Engine crew members hold a line on the 2017 Thomas Fire. The California Department of Forestry and Fire Protection ranks these among the worst fires in California’s history: As of 2019, the Thomas and Rim Fires were the 2nd and 5th largest, respectively. The Thomas Fire was the 10th -most destructive, with over 1,000 structures destroyed and two fatalities. Together, suppression costs exceeded US$2 billion.

USFS

Aggressive fire management The internal combustion engine is an important technology in wildland fire management. While humans have always been able to clear vegetation and create roads, fire prevention via the manipulation of the biophysical environment prior to mechanisation was mostly defensive. But heavy equipment has dramatically increased human capacity to respond quickly and aggressively to ignitions almost anywhere in the landscape, rather than simply prevent fire from spreading through specific areas (Fig. 1.5). Modern anthropogenic burning reflects the industrial mindset: small parcels, hard edges, and fire behaviour largely limited by moderate weather conditions that afford predictability and control.

Altered settlement patterns Shifts in human settlement and associated demand for natural resources create conflict with ecosystem processes. Colonialism and industrialisation by resource-hungry Europeans disrupted regions dominated by natural fire or indigenous burning. Similar processes drive deforestation burning in the Amazon basin and fires in the boreal regions of Russia and Canada subject to energy and mineral exploration.

Top: USFA; Bottom: US NIST

Figure 1.6: Two perspectives on the wildland-urban interface (WUI). Top: The WUI is where wildland fire occurs in close proximity to human settlement. Bottom: Fighting fire in the WUI requires different skills and tactics.

Human settlement is encroaching on wildlands worldwide, creating zones of conflict between fire and infrastructure known as the wildland-urban interface (Fig. 1.6). Defending communities nestled tightly into flammable landscapes demands considerable fire fighting resources, and still many structures have been lost to wildland fire. A critical assessment of how and where humans build their homes is necessary to prevent future losses.

Climate change Fossil fuel use has increased greenhouse gasses more rapidly than ever before, making it difficult for the Earth System to adapt at pace. Wildland fire is affected by two general trends in global environmental change: altered precipitation patterns, and altered air temperature. As wildland fire professionals have come together from around the world to manage emergent fire challenges, the global scientific community is raising alarm about the consequences of climate change-driven fire on future biodiversity (Nature Ecology & Evolution editorial 2020).

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Introduction

 5

A MODERN APPROACH TO WILDLAND FIRE SCIENCE Fire presents challenges in many aspects of society, scientific research, and management policy. Students of wildland fire find themselves at the end of reams of research and a long line of perspectives and policies that have, over time and across continents, disagreed with each other and even contradicted themselves. Wildland fire science, management, and policy require a breadth of knowledge and versatile skills. The work of physicists and chemists in the lab inform incident commanders in the field, and the ecologist must understand each of them while being themselves understood by the public and policy-makers.

S. Adams, US NPS

Ecologists record plant cover and seedling density following a wildfire in the Yukon-Charley Rivers National Preserve.

Wildland fire science literacy With volumes already available on wildland fire science, what justifies a new book on the topic? Indeed, in the sentiments of Isaac Newton—and more recently, Google Scholar—we do stand on the shoulders of giants (Table 1.1). We seek to promote wildland fire science literacy: Wildland fire science literacy is the capacity for wildland fire professionals to understand and communicate three aspects of wildland fire: (1) the fundamentals of fuels and fire behaviour, (2) the concept of fire as an ecological regime, and (3) multiple human dimensions of wildland fire and the socio-ecological elements of fire regimes (McGranahan & Wonkka 2018; Fig. 1.7). To this end, we give more attention to fire fundamentals (Part I) and human dimensions of fire (Part III) than the typical fire ecology textbook, and emphasise variability and scale in conventional fire ecology topics. We also offer several new perspectives that have yet to be synthesised in a single text that uses the most recent research.

Figure 1.7: Two arenas of wildland fire science—the field and the office. This figure helps fire professionals from each arena identify characteristics of the fire environment or fire regime that dominate their colleagues’ perspective.

McGranahan & Wonkka (2018)

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6  Ecology of Fire-Dependent Ecosystems

Table 1.1: The wildland fire canon that has influenced the authors of the present book.

General textbooks

• • • • • • • • • • • • •

Belcher, CM. 2013. Fire Phenomena and the Earth System. John Wiley & Sons. Bond, WJ & BW van Wilgen. 1996. Fire and Plants. Chapman & Hall. ` A & PR Robichaud, (Eds.). 2009. Fire Effects on Soils and Restoration Strategies. CRC Press. Cerda, Chandler, C et al. 1983. Fire in Forestry, vols 1 & 2. John Wiley & Sons. Davis, KP. 1959. Forest Fire: Control and Use. John Wiley & Sons. DeBano, LF et al. 1998. Fire’s Effects on Ecosystems. John Wiley & Sons. Drysdale, D. 2011. An Introduction to Fire Dynamics (3rd ed.). John Wiley & Sons. Johnson, E, (Ed.). 2001. Forest Fires: Behavior and Ecological Effects. Academic Press. Kozlowski, TT & C Ahlgren, (Eds.). 1974. Fire and Ecosystems. Academic Press. Pereira, JMC et al. 2019. Fire Effects on Soil Properties. CRC Press. Pyne, SJ et al. 1996. Introduction to Wildland Fire. John Wiley & Sons. Scott, AC et al. 2013. Fire on Earth: An Introduction. John Wiley & Sons. Whelan, R. 1995. The Ecology of Fire. Cambridge University Press.

Regional textbooks

• Agee, JK. 1996. Fire ecology of Pacific Northwest Forests. Island Press. • Booysen, P de V & NM Tainton. 1984. Ecological Effects of Fire in South African Ecosystems. Springer. • Bradstock et al., (Eds.). 2002. Flammable Australia: The Fire Regimes and Biodiversity of a Continent. • • • •

Cambridge University Press. Cheney, P & A Sullivan. 2008. Grassfires: Fuel, Weather and Fire Behaviour. CSIRO. Goldammer, JG & C De Ronde, (Eds.). 2004. Wildland Fire Management Handbook for Sub-Sahara Africa. Global Fire Management Center. Moreno, JM & WC Oechel, (Eds.). (1994). The Role of Fire in Mediterranean-Type Ecosystems. Springer. Wright, HA & AW Bailey. 1982. Fire Ecology: United States and Southern Canada. John Wiley & Sons.

Non-academic historical books

• • • • •

Courtwright, J. 2011. Prairie Fire: A Great Plains History. University Press of Kansas. Egan, T. 2009. The Big Burn: Teddy Roosevelt and the Fire that Saved America. Houghton Mifflin Harcourt. Maclean, N. 1992. Young Men and Fire. University of Chicago Press. Pyne, SJ. 1995. World Fire: The Culture of Fire on Earth. Henry Holt & Co. Scott, AC. 2018. Burning Planet: The Story of Fire Through Time. Oxford University Press.

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Introduction

 7

New perspectives The influence of vegetation on fire Fire ecology is widely regarded as the study of responses to fire. This approach is incomplete—it fails to account for the nature of the fire that effects a response. Fire is not simply a treatment that is either on or off. Variability in the nature of fire—e.g., fire type (ground, surface, or crown) or season (growing vs. dormant)—drives variability in fire behaviour and fire effects; e.g., variability in the intensity and rate of spread of a flame front determines fire effects via the amount of heat to which organs and organisms are exposed. Much variability in fire behaviour relates to the vegetation that serves as fuel. Within a given set of topographic and weather conditions, the structure, overall load, and moisture content of fuel determines fire behaviour. In the face of global change, shifts in plant communities due to management or invasive species alter fuelbeds and affect fire behaviour.

Emphasis on variability Most conventionally trained scientists have been taught to minimise variability, but accounting for environmental variability addresses fundamental challenges in general ecology. Whether explicitly analysed as co-variates or simply reported and discussed, information on fuels and fire behaviour aids interpretation of fire science research, helping to understand the environmental context of a study and inform its applicability to other locations. When applicable, we illustrate how fire ecologists can gain—and use—insight into variability. Knowledge of a system’s variability allows one to detect when system properties deviate from the historical range of variability. Many ecosystems worldwide long appreciated to be fire-dependent are burning at frequencies, intensities, and extents previously undocumented by wildland fire managers. For example, as we write this, Australia is experiencing its worst fire season on record (Fig. 1.8), and recent wildfire seasons in California, USA continue to set and reset records. Despite the aggressive fire management discussed above, the fire regimes of these systems appear to be shifting beyond their historical range of variability.

USFS

Figure 1.8: Dave Soldavini, one of hundreds of North Americans deployed to fight wildfires in Australia 2019–2020, holds a rescued baby kangaroo. Over 12 million hectares (ha) in Australia have burned. In comparison, 2018 was the worst fire season in the modern history of California, USA, with 2 million ha burned.

Distinguishing non-spatial and spatial responses Landscape ecology has given wildland fire science an appreciation for the spatial dependence of ecological processes. Some problems in fire ecology and management appear intractable even when a high degree of ecological nuance is applied; in many such cases, a spatially-explicit solution is viable. As a spatially and temporally variable disturbance, fire generates patterns in biotic responses at multiple scales. Focus at one scale rarely provides a complete ecological picture of fire effects, as it obscures ecologically meaningful cross-scale interactions (Falk et al. 2007). Considering fire effects and response patterns at different temporal and spatial scales helps reconcile potential conflicts of logic about fire, such as individual mortality of species vs. maintenance of critical habitat and management trade-offs among species with different responses to fire (Driscoll et al. 2010).

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8  Ecology of Fire-Dependent Ecosystems

L: C. Rossier, USDA; C: USFS Pacific SW Research Station Research Highlight #1383; R: C. Rossier, USDA

Figure 1.9: US fire suppression policies have harmed many Native American cultures who traditionally burned. (L) Frank Lake, a US Forest Service Research Ecologist and member of the Karuk Tribe, studies the intersection of traditional cultural management and Western systems of knowledge and management (e.g., Lake et al. 2017). (C) The USFS involves members of the Karuk and Yurok Tribes in prescribed fire training and management in California. (R) A member of the Karuk Food Crew gathers gooseberries.

Human dimensions In addition to variability and scale-dependence, robust fire science must also integrate the traditional spheres of natural science—ecology, physics, biogeochemistry—with those of the social sciences—sociology, anthropology, and political science. Given the interconnectedness between our species and fire that has increasingly blurred the lines between “natural” and anthropogenic fires (Bowman et al. 2011), a robust understanding of fire requires understanding feedbacks between the natural and human components of the Earth System (Folke 2006). Ecology has mostly failed to consider humans in the study of natural systems (Alberti et al. 2003, Reznick et al. 2002). Recent developments toward an integrative social-ecological framework provide means to explore human-natural linkages (Redman et al. 2004). Models of hierarchical social-ecological systems include feedbacks between the social and ecological sub-systems that increase overall system complexity (Levin 1999). Social-ecological systems frameworks have roots in complexity science, which describes nonlinear relationships between process and pattern, cross-scale interactions, uncertainty, and emergent properties (Kauffman 1996, Levin 1999). Throughout this book, we aim to present information consistent with the social-ecological systems framework and explain how fire ecologists and managers can employ this framework themselves.

Diversity: Geographical and otherwise Given the global occurrence of fire and its varied nature across continents, there is obvious value in drawing upon research from many biogeographic regions. The value of different cultural perspectives on fire has been less obvious, but is increasing.

Brent Johnson, US NPS

Figure 1.10: A wildland fire crew looks on as members of the Southern Sierra Miwuk Nation engage in a ceremony and traditional methods to ignite a prescribed fire at Yosemite National Park, USA.

Traditional ecological knowledge is the intersection of social-ecological perspectives and cultural diversity, and is gaining traction in natural resource management. Agencies are connecting traditional approaches to land use with Western structures of knowledge and management (Figs. 1.9 & 1.10). Indigenous practices have been implemented in fire management in the US and Australia (Levy 2005, Marks-Block et al. 2019).

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Introduction

 9

We have strived to cast a broad net for regional and cultural diversity along with ecological diversity. Some biases are difficult to overcome—regardless of one’s own awareness, there are historical and institutional limits on participation, access, and overall representation. For example, in the human arena, fire is not gender-neutral. Even beyond firefighter stereotypes (e.g., Batty & Burchielli 2011), differences in expectations between male versus female landholders with respect to wildland fire translate to differences in readiness and safety in fire-prone landscapes (Eriksen et al. 2010). Likewise, nearly twice as many men than women author scientific papers (Sugimoto et al. 2013); a recent editorial highlights the contributions of 145 women to wildland fire science specifically (Smith & Strand 2018). Whether considering a fire crew or a bibliography, one disproportionately encounters men, and experience suggests they are also probably white. While we cannot affect the existing literature here, we do make a conscious effort to highlight both regional and cultural diversity of fire, including indigenous approaches to burning. We want everyone to be able to see themselves as a future wildland fire scientist, manager, or policy-maker.

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I Fire Fundamentals

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CHAPTER

2

From flame to flame front How the physics and chemistry of combustion scale up from the ignition of individual fuel particles to fire behaviour across wildland landscapes

Targets of suppression efforts can be counter-intuitive. Firefighters use water to fight structural fires, but most consumer fire extinguishers use a dry powder. When extinguishing a campfire, water will put out flames, but bigger logs often flame again. While one might imagine water having a cooling effect—i.e., interrupting the heat component—the effect is actually to smother the fire and interrupt the oxygen component.2 Heat volatilises the dry powder of the fire extinguisher, producing carbon dioxide and displacing oxygen. Water on the campfire smothers the flames but big logs hold their heat—once water evaporates from the hot log, oxygen returns to the fuel and flames reappear.

en Flame

at

Should just one component of the flame triangle—fuel, heat, or oxygen—be removed, fire cannot exist. This is essential to understanding whether fire will or will not be sustained or spread, and is a tenet of fire prevention and suppression. Materials are treated with fire retardants to reduce their availability as fuel, as in aircraft interiors and some baby products (Stapleton et al. 2011). The adage “fight fire with fire” is based on the idea that fuel that has already been burned up will prevent subsequent fire.

We got curious and looked this up. Apparently the ratio is usually between 14-15 parts gasoline to each part air (Hillier & Coombes 2004).

He

The wildland fire environment is open and therefore less predictable. Fuel consists of plant material in many shapes and sizes, wet and dry. The ambient air holds oxygen, and while wind can increase oxygen input, air flow can also be blocked. Heat must come from an ignition such as lightning or an incendiary device, or from an existing fire nearby (this is, in fact, how wildland fire spreads—by the movement of heat through the landscape).

1

Ox yg

Whether in the compressed cylinder of a Ferrari or the leaf litter of a forest, all fire results from the combination of the three fundamental components of the Flame Triangle: Fuel, heat, and oxygen (Fig. 2.1). Fire in an internal combustion gasoline engine is controlled and predictable: a specific amount of fuel1 is injected just as the cylinder is drawing in air, and heat enters as a perfectly-timed spark after the air-fuel mixture is compressed.

Fuel Figure 2.1: The three components of the Flame Triangle. It is also known as the Fire Fundamentals Triangle in recognition of the fact that if just one component is removed, there can be no fire. 2

In a compartment fire, water does target the heat component not by cooling fuel, but by drawing energy out via steam. Hence structural firefighters often cut a hole in a roof or break windows and constantly apply water.

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14  Ecology of Fire-Dependent Ecosystems

THE CHEMISTRY AND PHYSICS OF FIRE We abandon consideration of industrial fire and structural firefighting to focus on plant biomass burning in the wildland context. Biomass burning is a decomposition process known as combustion, which is essentially a chemical reaction: the rapid oxidation of plant material (fuel), which produces energy as heat. Frequently, flames are also associated with combustion. While flames rely upon the biomass combustion, they exist independent from it. Flames are actually created by the burning of gasses heated and released from the biomass as it decomposes under combustion.

3

Other liberties include the fact that cellulose is far from the only compound included in the fuel component of the wildland flame triangle. While on one hand, fire ecology textbooks have depicted an overly-simple model of combustion for decades (Sullivan 2017a), on the other hand we cannot expect everyone interested in wildland fire science to add a minor in chemistry or physics. Throughout this volume we emphasise the complexity of the wildland fire environment and highlight references that go into more depth on important topics. Readers interested in the complexity of wildland fuel combustion should consume reviews by Andrew Sullivan (Sullivan & Ball 2012, Sullivan 2017a,b).

4

Finney et al. (2013) caution that this is an overly-simplistic model for the wildland fire environment. Heterogeneous wildland fuelbeds are comprised of particles in different conditions and are exposed to different rates of heating. Thus the idea of a single “kindling temperature” is misguided. See discussion of the ignition phase below.

As a decomposition process for plant material, combustion reverses the processes that composed the plant material in the first place, indicating fire is a natural entropic process. Taking some stoichiometric liberties3 to demonstrate fire releasing elements bound up by plant life, Trollope (1984) showed how photosynthesis (Eq. 2.1) is undone by combustion (Eq. 2.2):

CO2 + H2 O + energysolar → (C6 H10 O5 )n + O2

(C6 H10 O5 )n + O2 + energy

kindling temperature

(2.1)

→ CO2 + H2 O + energyheat (2.2)

The left-hand side of Eq. 2.2 is simply the three components of the flame triangle in Fig. 2.1: Firstly, (C6 H10 O5 )n , or cellulose, represents fuel. Cellulose is often used as a model compound for laboratory combustion experiments. While cellulose is a major constituent of plant biomass, two points explain wide variation in wildland fuel combustion chemistry: (1) species vary in the amount of cellulose in their tissue, and (2) several other compounds are also major contributors to fuel for combustion reactions, including hemicellulose and lignin; content of each also varies (Fig. 2.2). Secondly, O2 comes from the air, and thirdly, kindling temperature is the temperature to which fuel must be heated before it ignites and combustion proceeds as a self-sustaining reaction (approximately 500◦ C).4 As scientists learn more about wildland fire’s role in the Earth system, they must also better understand the fundamental reactions and mechanisms of biomass combustion. Given that approximately 3% of Earth’s terrestrial surface burns in a given year (Archibald et al. 2018), combustion reactions convert a substantial amount of plant biomass into volatile compounds that affect local and regional air quality as well as global climate. A better understanding of combustion supports a better understanding of how fire modulates the relationship between vegetation and geophysical processes such as climate and nutrient cycles (Sullivan & Ball 2012). Furthermore, understanding the fundamentals of combustion—the finest scale of fire behaviour—will support the protection of human and natural resources from fire with better predictions for how fire moves through landscapes. Thus, whether one wants to use, fight, or study fire, a basic understanding of how it burns is important for anyone involved in wildland fire science. Here we review the main processes of combustion, heat transfer, and fire spread.

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From flame to flame front  15

Grasses & Forbs Not yet burned

30

40

New growth

30

10

10

0

0

lo s lu el C

lo s lu el

Suckers

e

20

e

20

C

Stems

ce He llu m lo i− se C pr ru ot de ei n Li gn in M in er al s

Recent burn

ce He llu m lo i− se C pr ru ot de ei n Li gn in M in er al s

Mean +/− s.e. (%)

40

Western Snowberry

Constituent Data: NDSU Central Grasslands Research Extension Center, Streeter, North Dakota, USA

Figure 2.2: Examples of variation in fuel composition within a single ecosystem, the northern mixed-grass prairie from the North American Great Plains. (L) Composition of the herbaceous component varies with time-since-fire; more mature plants invest more into structural polymers (cellulose and hemicellulose). (R) When plants like shrubs have different organs and maintain aboveground parts through different seasons, composition can vary considerably within the plant. The frequency of fire—which reduces stems and increases suckers and new growth—affects the composition of fuel available to burn.

Phases of combustion Combustion proceeds in distinct phases as fuel particles heat to kindling temperature, ignite, and burn.5 But combustion in the wildland environment does not proceed in an orderly fashion. Sources of fuel variability that contribute to variability in ignition and heat release include the random arrangement of fuel particles of different shapes and sizes, and differences in chemical composition (Sullivan & Ball 2012, Finney et al. 2013). Bearing in mind flame generation is just one component of the combustion process, Albini (1980) describes heating, ignition, and flame generation from the perspective of a hypothetical fuel particle. Imagine a pine needle lying on the forest floor, awaiting oncoming flames. Generally, wildland fire scientists are more interested in how those flames move from particle to particle. But here we take a moment to understand the experience of a single particle, which helps explain the process of combustion.

5

Different authors draw different distinctions between these phases. For example, Whelan (1995) briefly describes the three observable stages originally delineated by Byram (1959): preheating, flaming combustion, and glowing combustion. Pyne et al. (1996) elaborates on four stages related to the chemical reactions: preignition, ignition, combustion, and extinction; they give the topic a very thorough treatment that merits your perusal but exceeds the necessary level of detail here.

Preheating begins ahead of the point at which one would say our fuel particle is on fire, but is nonetheless an essential part of combustion. Preheating, also called the preignition phase, involves heating fuel particles to kindling temperature. Once fuel temperature reaches 100◦ C, dehydration begins, which drives moisture out of fuel particles and evaporates it. Raising fuel temperature above that of the ambient air to the kindling temperature, plus dehydration, requires energy input as heat. At this stage, the combustion reaction is endothermic because energy required for preheating is coming in from elsewhere. Since the main source of heat energy is often the fire that is approaching, preheating proceeds slowly until the fire is quite close—15–60 cm, depending on environmental conditions —after which heating to kindling temperature occurs rapidly (Fig. 2.3). We describe how heat is transferred to fuel particles in greater detail below.

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16  Ecology of Fire-Dependent Ecosystems

head fire

400

Temperature (C)

Figure 2.3: Fine fuel temperature rises rapidly only once flames are very close to the fuel particle (numerals in circles represent distance between fuel and flame, in cm). Rothermel’s discussion of these data highlights the complex relationship between radiation, convective heating, and convective cooling, which wildland fire scientists have yet to fully sort out (Finney et al. 2013). The problem is that known heat transfer mechanisms alone don’t bring fine fuel particles all the way up to kindling temperature. It is likely that contact with flames pushed out ahead onto new fuel by convection ultimately leads to ignition of new particles (Finney et al. 2015).

no wind

300

backing fire

3

200

10

100

3 5 15

30 61

0

1

122

2

3

61

91

30 91

4

5

6

15

61 30

7

Time since beginning of fire (minutes)

8

9

Data: Fig. 1 in Rothermel (1972)

Pyrolysis begins once the temperature of our fuel particle reaches about 200◦ C. Pyrolysis is the thermal degradation of matter; this is the chemical decomposition through the application of heat described by Eq. 2.2. Our fuel particle is now breaking apart at a molecular level and actually losing mass as solid structural components like cellulose volatilise. Volatilisation rates accelerate as fuel temperature rises until our fuel particle reaches about 500◦ C and becomes solid carbonaceous char.

Ignition is classically defined as the point at which a fuel particle reaches kindling temperature. But recent research on the dynamic and heterogeneous nature of the wildland fire environment suggests ignition is better defined in terms of energy flux, rather than an absolute temperature: Ignition of fuel particles occurs after the solids are heated at a rate high enough to produce a sufficient quantity of pyrolysis gases, that when mixed with air can ignite and burn with a heat release rate greater than the heat loss rate to the surroundings (Finney et al. 2013, p. 30). In either case, ignition denotes the transition from the previous endothermic reaction that required heat as an input, to a self-sustaining exothermic reaction in which heat is instead being generated. CC0

Figure 2.4: In the wildland fire environment, both flaming and glowing combustion often occur simultaneously. Flames are produced by the combustion of gases released as pyrolysis causes vegetative biomass to break down. Glowing combustion is the continued thermal degradation of the carbonaceous char, which releases substantial heat.

Flaming combustion produces flames, which occur when the gases produced by pyrolysis themselves burn (Fig. 2.4). Between 200–500◦ C, our fuel particle is engulfed in flame as the volatilised compounds combust. Actual temperatures, rates, and even colour of flames vary among wildland fuels due to the variability in the chemical constituents among different plant species; these chemicals include oils, resins, and waxes. Smoke is a combustion product that scales up from molecular- to landscape-level processes. The visual components of smoke are comprised of vapourised water forced out as fuel is heated, and small particles

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From flame to flame front  17

that didn’t completely burn up. Smoke can also include gases released by combustion.6 Because these gases and water vapour have been heated by combustion, they are buoyant and tend to rise through the air. The stronger this flow, the greater the number—and larger the size—of particles smoke can carry aloft. Thus, the colour of smoke can indicate the intensity of the combustion that produced it: white smoke is generally comprised of water vapour, suggesting complete combustion, especially of fine fuels; darker smoke indicates heavier fuels and high rates of energy release.

Glowing combustion is the continued thermal degradation of biomass fuel into carbonaceous char, and can occur without observable flame once volatilisation has ceased (Fig. 2.4). Also known as smouldering, the energy produced can pre-heat subsequent fuel particles, and in this way fire propagates from fuel particle to fuel particle. The amount of flaming vs. glowing combustion can vary substantially. Densely-packed fuel particles tend to smoulder, while those with more airflow generate more flames. The degree of biological decomposition prior to a fire also affects the chemical composition of fuel particles, and can thus affect the nature of combustion.7

Extinction is the point at which combustion ceases. Pyne et al. (1996) include extinction as a distinct phase of combustion because it acts as a limiting condition on the heat balance within the reaction zone. Residual moisture and inorganic minerals are heat sinks, absorbing heat but not generating exothermic heat. Thus, even if fuel remains available and glowing combustion continues, if the exothermic reaction cannot generate net energy over that absorbed by heat sinks, propagation potential declines.

Heat transfer and propagation

6

Ward & Hardy (1991) describe smoke composition from woody debris. Take-away points include:

• Of released carbon, nearly 90% is in the form of CO2 , followed by CO, particulate matter < 2.5 µm wide (PM2.5 ), and CH4 . • PM2.5 ratios increase as fire intensity increases. • Flaming and smouldering combustion differ in smoke composition. • PM2.5 and CO amounts decline with combustion efficiency.

7

Previous texts give two good examples. Pyne et al. (1996) speculate that rotten wood ignites more easily than sound wood because the former is more broken down and thus takes less energy to release combustible gases. Conversely, Whelan (1995) describes how one study found it took substantially more energy to ignite eucalyptus leaves than previously reported because the leaves in the second study had been oven-dried and the easily-ignitable volatile compounds were already driven out of the leaves prior to experimental burning.

Above we described the phases of combustion from the perspective of a single fuel particle as it absorbed and released energy as heat. But no matter how well our hypothetical fuel particle burned, fire will not continue unless the heat produced by exothermic combustion reactions is absorbed by new fuel particles. Thus, once one understands how a single fuel particle absorbs and releases heat through the various phases of combustion, one must shift perspective to considering how heat moves among fuel particles, a process known as heat transfer. The transfer of heat energy from combusting fuel particles to nearby fuel particles such that new particles are heated, ignite, and undergo glowing combustion—i.e., combustion is self-sustaining and heat-releasing—is known as propagation. Heat transfer in the wildland fire environment involves several broad mechanisms. Three are standard physical processes, while others are unique to fires burning in open environments (Williams 1982, Sullivan 2017a). All play a role in the wildland fire environment, although considerable knowledge gaps remain about the conditions under which each process dominates heat transfer in various wildland settings (Finney et al. 2015).

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Temperature (C)

18  Ecology of Fire-Dependent Ecosystems 675

Particle size

545

12 mm 1 mm

Conduction is the transfer of heat through direct contact, where energy is transferred by the activity of individual molecules within an object (Sullivan 2017a). Conduction is probably the least important heat transfer mechanism in the wildland fire environment because contact between fuel particles is often minimal and plant matter is generally a poor conductor of energy, especially when dry. That said, conduction is likely the main mechanism by which heat penetrates large-diameter fuels (Sullivan 2017a).

415 285 155 25 0

20

40

Seconds

60

Data: Finney et al. (2013)

Figure 2.5: Fine fuels are subject to convective cooling. Finney et al. (2013) exposed 12-mm and 1-mm particles to the same radiative heating for 1 min. Temperature for the 12-mm particle rapidly rose to ignition (the spike at 30 s), and burned with an open flame. The 1-mm particle fluctuated around 155◦ C despite identical radiation exposure. 8

Technically convection refers to the transfer of heat via the movement of a fluid, but non-air fluids in the wildland fire environment are rare and short-lived (e.g., Boutin et al. 1998). 9

“. . . heat exchange by radiation depends primarily on the temperatures of the surfaces, while that for conduction and convection depends on variables such as the physical properties of the fluid, the shape of the solid surface, and the velocity of the fluid past the solid boundary (Fons 1946, p. 96).”

10

Some have interpreted hot, ground-level gasses moving ahead of flame fronts as evidence of convective preheating (Clements & Seto 2015), while others attribute pre-heating to radiation from tilted flames (Morandini et al. 2018). Others conclude convection and radiation are comparable (Xie et al. 2017) or vary with slope (Silvani et al. 2012, Tihay et al. 2014, Silvani et al. 2018).

Convection is the transfer of heat through air flow.8 Two types of convection in the wildland fire environment include free or natural convection, in which heated air rises because it expands and becomes buoyant relative to the surrounding air, and forced convection, in which the flow of heated air is driven by an outside force, such as wind. Both types of convection are important in determining how fast fire spreads in a given direction.

Radiation is the transfer of energy via electromagnetic waves. Uniquely, radiative heat transfer requires no medium; while convection and conduction require contact between molecules, electromagnetic radiation can move through a vacuum.9 Although radiative heat transfer can therefore occur over long distances, it also requires line-of-sight travel and in dense fuels, particles block radiative transfer to other particles in their shadow. With so much emphasis on heat transfer, kindling temperature, and ignition, it is tempting to believe that all energy transfer is moving from one combustion reaction to support a subsequent combustion reaction. But the wildland fire environment is complex and not all energy goes into combustion. In fact, some heat transfer processes work against propagation: water and minerals act as heat sinks, and convection can actually cool down fuel particles far enough ahead of the flames (Silvani et al. 2018; Fig. 2.5). Recent studies suggest direct flame contact is an important ignition factor, especially in fine fuels. This might resolve some of the debate around convective vs. radiative mechanisms driving pre-heating,10 and explain the rapid temperature rise as flames approach new fuel particles (Fig. 2.3). Essentially, flames can be pushed into unburned fuel by short, horizontal bursts of heated air. In controlled laboratory fires, researchers have observed periodic temperature fluctuations ahead of flame fronts that suggest convective flow of hot, buoyant air creating vortices that push flames ahead of the flame combustion zone to transfer heat to, and ignite, new particles (Finney et al. 2015, Morandini et al. 2018). In this way, a fire’s own dynamics—buoyant hot air bursts, rather than local wind—can push flames into unburned fuel (Tang et al. 2017, Morandini et al. 2018). Unique to the wildland fire environment is solid fuel transport, the physical movement and deposition of material already in the latter phases of combustion. Solid fuel transport can transfer heat beyond the range of the standard mechanisms above: locally, larger fuel particles can break apart, fall, or roll as pyrolysis degrades structural compounds, and burning chunks of fuel can transfer heat and cause ignition downhill. More broadly, burning

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From flame to flame front  19

embers, or firebrands, can be carried by wind and deposited far from the original fire. But ignition is never a certain outcome of solid fuel transport. Subsequent ignition requires the transported particle to encounter suitable fuels, and still have enough heat to transfer to those fuels once it reaches them. Fires started by solid fuel transport away from the fire of their origin are known as spot fires and are discussed more below.

Many properties can be used to describe wildland fuels: vegetation type (e.g., grasses, brush, coniferous or deciduous trees, logging slash, etc.); arrangement and structure (e.g., horizontal or vertical, continuous or discontinuous); particle size, along with surface area to volume ratio; particle density (loft, or packing ratio); whether the vegetation is alive or dead, standing or fallen, and whether it was derived from leaves or stems. Given the plethora of plant species on Earth and the myriad combinations within plant communities, it might seem impossible to make any meaningful simplification to consider how fire spreads through wildland ecosystems. But remember: fire spread is essentially the physical transfer of heat through the environment, and it turns out that the diversity of plants can be reduced to just a few categories of wildland fuel based on their thermal properties.

Fuel size classes The primary breakdown for wildland fuels is by diameter. The distinctions among size classes are defined by meaningful thresholds in surface area:volume ratio, which affects the rate at which a fuel particle gains heat in the pre-heating stage of combustion (Fig. 2.6), and the rate at which heat is lost while smouldering prior to extinction. This emphasis on heating rate and retention forms the basis for the odd convention of using times to refer to fuel particle diameters, which are categorized into 1-hour, 10hour, and 100-hour size classes, corresponding to diameters of < 0.6 cm, 0.6–2.5 cm, and 2.5–7.6 cm, respectively (Fosberg & Schroeder 1971).11 Sometimes it is sufficient to differentiate fine fuel (1-hr) from coarse fuel (10-hr and above). Physically, the fine fuel category represents “thermally thin” fuel particles: those with low surface area:volume ratios that take on heat quickly—and uniformly—and fully combust more or less in the time it takes for the flame front to pass. In the field, the fine/coarse distinction might apply to grasses vs. larger shrubs, trees, and downed woody debris. Outside of severe weather and drought situations, these larger fuels are likely either alive or have a higher moisture content and are thus unlikely to reach kindling temperature by the time the flame front passes.

Thermal response time (min)

FUELS IN THE WILDLAND FIRE ENVIRONMENT

100 75 50 25 0

1.0

1.3

1.7

2.0

Radius (cm)

2.3

Data: Fosberg (1973)

Figure 2.6: The non-linear relationship between fuel particle size and heat transfer rate. Small increases in fuel particle radius leads to disproportionately large increases in thermal response time—the speed at which the material changes temperature. Thus, a fuel particle with radius 1 cm has a response time of 1.4 min, while the response time jumps to 56 min for a particle of 2 cm radius. 11

The same categories also describe fuel moisture. Lag time refers to how quickly fuel particles gain and lose moisture, and increases with each size class. The metric delineations might seem odd, but the original categories were in inches: < 0.25, 0.25–1, 1–3. Some recognise a 1,000-hr class for very large fuels.

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20  Ecology of Fire-Dependent Ecosystems

Fuel moisture

12

Scientists have struggled to account for live vegetation when it occurs in wildland situations. In the parlance of wildland fire science, live fuels are often not available to burn because they are insufficiently cured, i.e. they haven’t been dried out through exposure to warm air or radiation. Initially, live fuels were generally ignored; a focus on wildfires during extremely dry and likely droughty conditions meant all vegetation could be assumed to be dead and available. But interest in fire outside of dry, dormant seasons (Engle & Bidwell 2001) and some invasive species (McGranahan et al. 2012) require fire managers to think beyond dead fuels alone.

Data: Byram & Jemison (1943)

Figure 2.7: Wind (ventilation) and sunlight (radiation) interact to affect wildland fuel drying rates. These lab data with thin slats of wood suggest shady, sheltered fuels will dry slowly. 13

For this reason, long-term drought can increase fire danger even if total fuel load remains constant: more of the coarse material is dry and is likely to burn, whereas those same fuels would have had a lower likelihood of ignition under high-moisture conditions.

Fuel moisture is a critical property of the wildland fire environment and wildland fuel moisture dynamics relate closely to the size classes described above. But first a much broader distinction must be made between all fuel types and sizes: living versus dead vegetation. Live fuels must often be treated as a class by themselves, for two reasons:12 firstly, moisture dynamics of live vegetation are actively modulated more by the ecophysiology of the organism than a passive interaction between plant tissue and the environment, and secondly, tissue moisture of live vegetation often exceeds levels that will support combustion. Within dead fuels, the concept of lag times applies to fuel moisture gain and loss as well as heating and cooling: dead fuel moisture is a function of the ambient environment and all dead fuels passively gain and lose moisture through their exposure to the atmosphere; thus, fine dead fuels gain and lose moisture faster than coarse fuels due to greater surface area to volume ratios. Within a fuel size class, several environmental variables affect fuel moisture transfer rates. Sources of moisture include the atmosphere itself—humidity and precipitation—and contact with other sources capable of holding and releasing moisture, such as soil and forest duff. Barring a rapid change, fuel particles eventually reach an equilibrium moisture content with the surrounding air at which gains and losses of moisture via diffusion with the air are net neutral. Generally speaking, equilibrium moisture content is a few percentage points higher for coarse fuel than fine fuel. Fine fuels can return to equilibrium moisture content within a matter of hours, while large fuels can take days or weeks to become available for combustion if they had previously been soaked through.13 The rate at which wildland fuels lose moisture is increased by exposure to both wind and solar radiation (Fig. 2.7). Fuel moisture can vary seasonally as well as diurnally, especially in live fuels. While dead fuel moisture can vary, as well, seasonal variability tracks closely with soil moisture and humidity, and diurnal variability is driven by precipitation (including frost and dew) and solar exposure. These variables are easily observable and quite predictable; in fact, tables have been calculated to allow one to look up dead fuel moisture based on just a few parameters (Rothermel 1985). Live fuel moisture follows the biology of individual plants, which can have both seasonal and diurnal variability (Fig. 2.8). Live fuel moisture presents a specific challenge for wildland fire science. Finney et al. (2013) argue against simply considering live fuels as highmoisture dead fuel in three specific points: (1) Live fuels support fire spread with fuel moisture content well above that which would support spread through dead fuels; (2) Unlike dead plant material—which is mostly dessicated structural carbohydrates and a little bit of moisture—live fuels contain substantial non-structural compounds (Fig. 2.2), the amount of which varies seasonally according to species biology; (3) Live fuels retain a lot of moisture through pre-heating until their cells fail; the presence of this water absorbs energy and delays ignition for the entire fuelbed.

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From flame to flame front  21

Figure 2.8: Variation in moisture content of foliage among four species of shrubs from North Carolina, USA at two scales: Seasonal and hourly across two days in summer. Dotted lines denote 120% moisture, a conventional threshold for live fuel availability in steady-state models (Jolly 2007). Note temporal patterns but also differences among species. Live fuels are unique in that (1) they can burn at moisture contents well above that of dead fuel, and (2) variability in moisture content is often due more to plant biology than atmopsheric interactions, as with dead fuel (Finney et al. 2013). Data: Blackmarr & Flanner (1968)

The fuelbed and wildland fuel structure In the wildland context, fuels occupy a three-dimensional space called the fuelbed. It is through the fuelbed that flame fronts spread via propagation of successive ignitions among individual fuel particles. As such, the structure of plant biomass in this three-dimensional space has a major effect on fire. Within the landscape, the type of vegetation—grassland, brush, or forest—determines the type of fuel available to burn. Within the combustion reaction zone, the arrangement and density of fuel particles regulate the flame triangle, affecting how much fuel is available, how readily oxygen flows in, and how quickly heat transfers to new fuel particles. Three broad fire types are defined by the fuel layer through which fire spreads (Fig. 2.9): ground fires burn through organic material below the soil surface, such as peat and heavy forest duff;14 surface fires burn through herbaceous and brushy aboveground plant biomass rooted in, or laying on, the soil surface; and canopy fires burn through the foliage of standing trees. Although fires rarely start in the canopy layer, canopy fuels can be ignited by extremely high surface flames or via ladder fuels—hanging branches, vines, or shrubs and trees of younger age classes that carry fire into the canopy after first being ignited by surface fires.15 A distinction between two types of canopy fires illustrates how vegetation structure affects fire behaviour. When the canopy of a single tree burns, the tree is said to be torching, while sustained propagation through fuels above the surface is called a running crown fire. Although all the fuel properties (e.g., moisture content and fuel load) and fire physics (e.g., rate of heat transfer) apply, at the scale of the three-dimensional fuelbed, the overall consideration is horizontal continuity—how far apart the trees are. All other factors equal, greater canopy density increases the probability of a running crown fire should fire transition from the surface to canopy, and less-dense canopies are more likely to produce single torching trees even if vertical continuity between surface and canopy fuels is high.

14

We discuss ground fires more in Chapter 6.

15

Ladder fuels cause consternation among wildland firefighters, who prefer to battle fire on their level, and prescribed burners, who often target surface fuels and seek to avoid canopy fires.

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Figure 2.9: Four types of wildland fires. Ground fires burn underground through organic material like peat, while surface fires move aboveground through fuels on the soil surface. Fire can transition to tree canopies, as well. Torching occurs when the canopy of a single tree burns, while running crown fires involve canopies of multiple trees.

We a

hy rap og

the

r

p To

Fire behaviour Fuel

Figure 2.10: Topography, weather, and the fuelbed are the three major drivers of wildland fire behaviour.

FIRE BEHAVIOUR Fire behaviour describes energy release by the combustion of vegetation. Direct observations include how fast a fire moves, how long fuels burn, and the length of flames. Indirect observations include how much unburned fuel is left behind, or how high up trees are scorched by flames. At a landscape level, spatial descriptions of fire behaviour can be important, such as the size, shape, and internal continuity (patchiness) of burned areas. In this section we scale up from individual flames to look at how successive ignitions lead to the movement of fire across flammable landscapes. Fire behaviour is the sum of the factors affecting combustion, which in the wildland fire environment is complex, due to the variability inherent in wildland fuel composition and structure. This variability is compounded when preheating, ignition, and combustion processes occur simultaneously in different types of vegetation, in different terrain, and under variable weather conditions (i.e., the three sides of the Fire Behaviour Triangle; Fig. 2.10). Each of the components of the Fire Behaviour Triangle differ in the degree and scale of spatial and temporal variability (Table 2.1). While multiple sources of variability at multiple spatial and temporal scales make it difficult to predict how fire will behave, the effects of individual factors (e.g., wind, moisture, and terrain) are typically consistent. Thus, with knowledge and experience, wildland fire professionals can learn to anticipate how environmental factors influence fire behaviour in a particular fuelbed.

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Table 2.1: The three components of the

Component Spatially dynamic? Temporally dynamic? Manageability fire behaviour triangle—topography, weather, and fuel—in terms of their Topography Yes No None variability and capacity to be managed. Weather Not very Yes Low The table mostly considers the spatial Fuels Potentially Yes High scale of a single burn unit over which the

Fire spread and the flame front Fire spread is the movement of fire through the fuelbed. The flame front defines a zone of flaming combustion with unburned fuel ahead and ash, char, and smouldering fuel behind. Fire movement depends on propagation—heat release from combusting fuel particles to nearby fuels that subsequently warm to kindling temperature and ignite. Thus, the heating of fuel ahead of the flame front is the critical mechanism for propagation ´ ˜ Torralbo 1967), which is basically a series of suc(Sanchez Tarifa & Munoz cessive ignitions controlled by the ignition time of subsequent particles and the distance between them (Fons 1946). Technology has advanced scientific understanding of fire spread.16 A dynamic approach studies fire behaviour as energetic interactions between the combustion zone and the surrounding environment (Yedinak et al. 2018). Early wildland fire scientists were limited by a lack of data due to the challenges of making precise observations of heat transfer at fine spatial and temporal scales. As a result, variability inherent in the wildland fire environment is often simplified and movement of flames averaged over space and time. Such simplifications are described as steady-state assumptions because fire spread and the influence of factors such as wind are assumed to be constant within the spatial and temporal scales being considered.

same general weather patterns apply. Thus the manageability of weather refers to broad decisions as to whether to ignite a prescribed burn, or the direction firefighters might begin their operations. Topography is fixed through time. Fuels are variable in both time and space, depending on vegetation and interactions with weather. Fuels are also the easiest to alter via management.

16

Applying and teaching the dynamic perspective is challenging. Measuring energetic interactions remains the realm of specialists with technical—and often expensive—sensors. We lead with the simplified steady-state approach here because it serves as a good heuristic model for introducing fire behaviour.

Observing fire behaviour For decades, fire behaviour observations were based on the steady-state assumptions described above, with measurements averaged over space and time. Better understanding of the dynamic wildland fire environment has increased capacity for finer-scale, more informative observations. Incorporating such measurements into wildland fire research, and incorporating their findings into wildland fire management and policy, is ongoing. Many simple observation methods based on steady-state assumptions have been developed and are widely taught and applied; quantifying the variability of a dynamic wildland fire environment can require specialised equipment and knowledge. Here we describe basic observations of fire behaviour to connect properties of flame fronts and environmental factors.

Thomas Schoch CC BY-SA 2.5

Figure 2.11: A head fire moves through grass understory in the Kakadu National Park, Australia. Note how the wind lays the flames down—thus flame length, not flame height, is a reliable way to visually estimate fire intensity.

Two classical descriptors of fire behaviour are rate of spread and intensity. Rate of spread refers simply to how fast the flame front moves through the fuelbed, and is readily observed (at least from a safe vantage point unobscured by smoke). Intensity is not directly observable. Generally, intensity

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17

Rothermel (1983) derives the value for fireline intensity from two measurable variables: the amount of energy available for release within the unburned fuel (heat per unit area), and how fast the flame front moves through the fuel (rate of spread). Byram (1959) provides an equation to calculate intensity I after the fact—I = H · w · r —if one collects the relevant data: H is heat yield, obtainable by putting fuel clipped before the fire in a bomb calorimeter; w is the amount of fuel consumed, determined by subtracting post-burn fuel from pre-burn fuel load measurements; and r is the rate of spread. 18

It is important to note that because wind tends to push flames down, as illustrated in Fig. 2.11, the flame parameter best associated with fireline intensity is true flame length—the distance from the base of the flame in the fuelbed, to its tip—and not just flame height, which does not increase in proportion to wind speed precisely because the wind lays the flame down. The non-linear relationship between intensity and flame height complicates post-hoc measurements of fire behaviour such as scorch height on trees.

refers to energy release; more specifically, fireline intensity is the amount of heat released per length of flame front within a period of time (Rothermel 1983).17 While fireline intensity itself cannot be observed, flame length is directly related to the rate of energy release and thus serves as an alternative measurement when combustion produces flames (Rothermel 1983). Both fireline intensity and flame length increase with fuel load and decrease as fuel moisture increases (Kreye et al. 2013). When fuels are constant, variability in fire behaviour is driven primarily by wind and the flatness or steepness of the terrain, or slope. Wind and slope are important because they affect heat transfer and facilitate pre-heating, ´ which is the critical mechanism for flame propagation (Sanchez Tarifa & ˜ Torralbo 1967). Wind pushes heat ahead of the flame front and hot Munoz air rises upslope, both of which increase convective heat transfer (Sharples 2008). Both also reduce the angle between the flame and surface fuels, increasing the probability of heat transfer via flame contact.18 The effect is for fires to move faster with the wind, or up a slope, and more slowly against the wind, or down a slope, than in the absence of either. Dynamic approaches to wildland fire behaviour focus on energy flux—the energy exchanged in a given area over time (Yedinak et al. 2018). Total energy flux differs from fireline intensity, which refers to energy released at the flame front. Energy is both released through combustion and absorbed by non-flammable materials like water and minerals; energy release can also continue after the flame front passes as large or dense fuels smoulder.

Wildland fire anatomy Predictable effects of slope and wind on heat transfer allow one to predict the shape and direction of wildland fire spread through a given fuelbed. As wildland fire spreads in a predictable shape, so too does fire behaviour vary predictably at different points along the fire perimeter relative to wind direction and slope. Changes in the fuelbed—vegetation type, fuel load, or structure and arrangement—can also affect fire behaviour. Assume a single-point ignition—e.g., a lightning strike, match, or cigarette butt—in an even, level grass fuelbed. In the absence of wind, the flame front slowly spreads at a constant rate in all directions. Heat transfer is limited to those particles very close to the reaction zone. The resulting shape of a fire under a no-wind scenario is a slowly-expanding circle. But if the wind were to come up, heat transfer rates will vary between the upwind and downwind directions (Fig. 2.3) and the shape of the fire becomes elliptical as different parts of the fire spread at different rates (Fig. 2.12). Head(ing) fires are flame fronts that move with the wind. Wind assists convective heat transfer by pushing warm air ahead of the fire, which accelerates pre-heating and accelerates propagation and fire spread. As wind speed increases, so does the rate of spread in the same direction. It is essential to know the speed and intensity of a head fire when making tactical decisions. While lower-intensity head fires can be attacked directly—by specialised crews with hand tools and water hoses—fast-moving flame

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fronts with long flames must be attacked indirectly—by creating fire breaks and removing fuel between the barrier and the oncoming flame front.

Figure 2.12: The anatomy of a surface fire. Here the wind moves from left to right, giving the fire an elliptical shape as the heading fire moves fastest with the wind, and has the longest flames. Conversely, the backing fire moves the slowest, creeping into the wind. On each side, flanking fires spread perpendicular to the wind at a rate between the backing and heading fires; they are aerated by the wind but are neither moving fully against it nor with it. The burned area in the centre of the fire is often called “the black” and is an important safety zone for fire personnel due to the lack of remaining fuel. The spot fire was ignited by a firebrand, or ember, carried ahead of the main fire by the wind.

Backing fires are flame fronts that crawl into the wind and move more slowly as wind speed increases. Blowing heat back over previously burned areas denies unburned fuel the convective mechanism of heat transfer, which delays the rise to kindling temperature. Flanking fires are the sides of the fire that burn parallel to the wind, between the head and backing fires. As such, their speed and intensity are between the head and backing fires. The lower intensity of flanking fires can be important tactically for wildland firefighters, as it is easier to fight these flames directly and work toward the head fire, reducing the total area burned. But a wind shift could easily turn one of the flanking fires into a head fire. Thus, it is important that personnel working on a flanking fire do so from the burned area within the perimeter, which serves as a safety zone should a wind change increase fire spread rate or intensity. Spot fires start when firebrands—burning embers carried ahead of a flame front by wind or convection—ignite fuels beyond the perimeter of the fire. While fire spreads outward as a flame front via propagation, fire growth is driven by increases in burned area, including the spot fires that accelerate the increase in burned area beyond the spread of individual flame fronts. The smoke plume is a component of a wildland fire often left out of descriptions of fire anatomy. The smoke plume is defined as the “gases, smoke, and debris that rise slowly from a fire while being carried along the ground because the buoyant forces are exceeded by those of the ambient surface wind (NWCG 2019).” Plumes that develop strong updraft are called convective columns, highlighting their effect on fire behaviour. Many fireatmosphere interactions that relate to convective lift aren’t directly visible. Plume structure provides insight into atmospheric conditions (Fig. 2.13), and plume properties such as colour and roiling indicate fire behaviour.

Meghan LE Kirkwood, North Dakota State University

Figure 2.13: Despite low fuel loads, high live fuel moisture, and virtually no wind, atmospheric instability facilitated the development of this smoke plume over a prescribed burn in North Dakota, USA. Note how smoke from all visible fire lines leans into the center of the burn unit, drawn in by convective updraft. This interaction generates airflow and increases fire spread in the absence of wind.

On level terrain with a continuous, even fuelbed and constant wind, the shape of a fire’s burned area depends primarily on wind speed.The higher the wind speed, the longer and more narrow the burn perimeter (Fig. 2.14);

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26  Ecology of Fire-Dependent Ecosystems

due to the ratio of perimeter to area in ellipses, higher wind speeds actually result in lower total burned area for fires spreading over the same period of time. But the shape and patchiness of most fires is determined by the ruggedness of the terrain; presence of unburnable areas and barriers like roads, fields, or surface water; or heterogeneity in vegetation (Fig. 2.15).

Length:Width ratio

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Environmental factors

0123 5

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Figure 2.14: Simulated fire ellipses become long and narrow in the direction of wind as wind speed increases, represented here as length:width ratio of fire ellipses calculated with BehavePlus.

Just as flame-level variability is driven by complexity in wildland fuel composition and structure, complexity in several environmental factors contribute to variability in fire behaviour. Slope and wind direction and speed can vary substantially over both space and time as flame fronts move through real landscapes. Nor are wind and slope the only environmental factors to affect wildland fire behaviour. Weather, fuel, and topography all interact to determine the outcomes of fire spread (Holsinger et al. 2016). Here we describe several other weather and terrain influences on fire behaviour that can modulate ecological effects of wildland fire.

Figure 2.15: Heterogeneous landscapes can be described as fuelbeds comprised of patches of fast and slow fuel types, depending on whether fuels increase or decrease fire spread relative to other fuels in the landscape (Finney 2003). (L) Fire spreading through a homogeneous, fast-fuel landscape has the expected elliptical shape. (R) Patches of slow fuels impede fire spread: the fire perimeter is tortuous and the overall burned area reduced. Triangles are ignition points. The fast fuel is cured tallgrass prairie and the slow fuel is an invasive grass with high live moisture (McGranahan et al. 2013a). When patches are large enough, fire might not reach their centres at all (McGranahan et al. 2018a).

Topographic influences

19

General winds are driven by pressure gradients across scales broad enough to show on weather maps while local winds are produced by pressure gradients from fine-scale differences in air temperature due to land cover, shading, etc. (Schroeder & Buck 1970).

Topographic influences extend beyond general slope effects, especially in rugged terrain. Even in flat and rolling landscapes, variability in soil and topo-edaphic effects can cause differences in productivity and patchiness in vegetation. Wetlands, especially, can either slow passing flame fronts due to standing water, or increase intensity due to high plant productivity; the same wetland could have either effect, depending on the season. Several features of rugged landscapes drive variability in fire weather or fuels in ways that affect fire behaviour. Topographic features such as mountains and canyons interfere with general wind patterns19 by either blocking or converging air flow, respectively. In both cases, eddies can form and cre-

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Data: US Geological Survey’s National Map

Figure 2.16: 3D models of box canyons in which wildland firefighters were trapped after descending into the narrow ravines that extend to the ridgelines. In both cases, fire ran up from the bottom, accelerated by the chimney effect these features create. (L) At the boundary of the Angeles National Forest in California, USA, where 10 firefighters died in the 1966 Loop Fire (Loop Fire Analysis Group 1966). (R) Yarnell Hill, Arizona, USA, where 19 firefighters died in 2013 (Yarnell Hill Serious Accident Investigation Team 2013).

ate local winds with substantially different speed and direction than general wind patterns, which can further interfere with fire spread. Box canyons and saddles increase the rate of spread of wildland fires. Box canyons are steep-sided ravines, typically with a dead end at the top, which creates a chimney effect that draws warm air up (Fig. 2.16). The narrow canyon concentrates and accelerates air flow.20 Likewise, saddles are low points, or passes, between mountain peaks that similarly concentrate and accelerate air flow, and create turbulent eddies on the lee side. Aspect is the direction that a slope faces, a nuance of topography that can affect fire behaviour in two ways, depending on how much, and when during the day, sunlight reaches the surface. Although topography is the least manageable component of the fire environment, in that it is fixed and doesn’t change at human time scales, it is also therefore very predictable. Thus, firefighters and prescribed burn managers alike can anticipate changes in fire behaviour as fire spreads onto different aspects and into potentially different vegetation types and microclimates.

20

Fire often seems to explode up the length of a box canyon, which Xie et al. (2017) attribute to the concentration of heated air in the narrow canyons and ease of flame contact with unburned fuel under steep slopes. Eruptive fire behaviour makes box canyons especially dangerous for wildland fire fighters and unfortunately a number of lives have been lost from entrapment situations (Fig. 2.16).

Firstly, aspect determines how much sun a slope receives (Fig. 2.17), which can affect vegetation by creating local differences in temperature

Annual Radiation Index

30o North

50o North

60

S

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50 40 30 20

ESE

SE

E

ESE

ENE

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ENE

NNE N

NE

Figure 2.17: Not only does the amount of solar radiation received vary by slope and aspect, but the effects of these factors differ with latitude. Note the greater degree of variability in radiation received among different slopes further from the equator, despite lower radiation on level ground.

NNE

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Data: Byram & Jemison (1943)

Figure 2.18: Aspect, slope, and season affect the amount of solar radiation received over the course of the day. Note the pre-noon peak in solar radiation on east-facing slopes, and the substantial seasonal difference away from south-facing slopes.

and moisture availability. Slopes receiving more sunlight are often drier and warmer, and thus might have more grass, while the shady side is often wetter, cooler, and better habitat for denser woody vegetation. Thus, in steep terrain, which side of a slope a fire occurs on can affect several aspects of fire behaviour, from fire type (surface vs. crown, based on vegetation) to rate of spread and intensity (based on fuel moisture).

21

Insolation effects are most apparent on low-intensity flame fronts: While clouds pass in front of the sun, flames in light fuels are noticeably smaller.

22

Instability is difficult to observe but meteorologists can measure and predict associated variables. US fire weather forecasts include the Haines Index, an index of the potential for rapid fire growth derived from correlations between observed fire behaviour and atmospheric stability (Haines 1988).

Secondly, aspect can affect fire behaviour over the course of a day, depending on when and for how long a slope is exposed to sunlight (Fig. 2.18). East-facing slopes receive sunlight earlier in the day, and thus begin to warm and dry earlier. West-facing slopes receive direct sunlight later, but fuels can dry through the day via exposure to air warmed by convective heating. South-facing slopes receive sunlight more consistently throughout the year than other aspects, while north-facing slopes remain consistently cooler. These differences affect fire behaviour primarily through fuel moisture, but solar radiation can also contribute additional energy to combustion and increase the intensity of energy release.21

Atmospheric influences Atmospheric stability refers to how resistant the atmosphere is to vertical air movement, which can modulate rate of spread and fire growth (Schroeder & Buck 1970). Hot combustion gasses and heated air are buoyant, and naturally seek to rise until the air expands and cools. In an unstable atmosphere, warm air continues to rise without additional heat input, which increases convective airflow away from the combustion zone.22 Fire-atmosphere interactions occur mostly in the troposphere, the lowest layer of Earth’s atmosphere, which varies between depths of 8–10 km above the Earth’s surface. Above the troposphere is the stratosphere, where air is quite stable and resists mixing. Thus, the top of the troposphere defines the mixing height, which in all but the most extreme cases represents the maximum height at which surface-based convection can

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reach (Schroeder & Buck 1970). High mixing heights can facilitate fast convection rates and rapid fire growth (Fig. 2.19). The bottom of the troposphere is called the boundary layer, “that part of the troposphere that is directly influenced by the presence of the earth’s surface, and responses to surface forcings with a timescale of about an hour or less (Stull 1988, p. 2).” Boundary layer depth can vary between 100 m and several kilometres (Stull 1988). Surface forcings relevant to wildland fire include advection and convection—horizontal and vertical air movement, respectively—whether from wind, in the case of advection, or convection from surfaces warmed by sunlight or from combustion itself. Between the boundary layer and the mixing height is a mixing layer that is not typically resistant to convection and air mixing. Variability in the mixing height and depth of the boundary layer account for substantial variability in fire weather and fire behaviour. Potter (2002) divides the lower atmosphere into regions relevant to a three-stage model of wildfire development (Fig. 2.20). Basically, as a fire grows, it can engage higher parts of the atmosphere and subsequent fire development depends increasingly on upper-level atmospheric conditions in addition to those near the surface.

John Fowler CC BY 2.0

Figure 2.19: Smoke from the 2011 Las Conchas Fire in the Santa Fe National Forest, New Mexico, USA, reached beyond the mixing height.

Figure 2.20: Three stages of wildland fire growth and the relevant atmospheric strata each involves (Stull 1988, Potter 2002).

An inversion is a departure from the typical pattern in which air gets colder with distance from the Earth’s surface (Fig. 2.20). Surface inversions are deep layers of cool air near the surface, also known as night inversions because such cooling often occurs at night (Schroeder & Buck 1970). Inversions are essentially the surfacing of the mixing height (Fig. 2.20), which forms a low, stable air barrier, or thermal belt, that prevents mixing, convection, and smoke dispersal. In mountainous areas, inversions form in canyons and trap smoke (and other air pollutants) below the thermal belt (Fig. 2.21). Night inversions can form over broad areas without topography. The effect of an inversion on fire behaviour is sometimes most noticeable as the inversion lifts. By suppressing convection, inversions have the obvious effect of reducing the intensity of wildland fire burning through the night. But as solar heating in the morning warms the surface, warm air begins to circulate in the surface layer and rise into the mixing layer. The inversion weakens and finally lifts entirely, allowing convection to extend through the troposphere. Thus multi-day fires often repeat fire development stages (Fig. 2.20) as inversions form and lift (Potter 2002). The consequence for fire behaviour is greater intensity as convection develops.

Jordan Mallory, NPS helicopter crew-member

Figure 2.21: Smoke from a prescribed fire at Sequoia National Park, California, USA, is trapped by an inversion layer.

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Extreme fire behaviour Definitions for extreme fire behaviour vary with the context of application. The term can refer to specific instances of unpredictable or dangerous fire behaviour that threaten the security of lives, resources, or property. Previous definitions focus on whether a wildfire is within or beyond control efforts. But a revised definition focuses on the process, rather than outcomes, of extreme fire behaviour: “Fire spread other than steady surface spread, especially when it involves rapid increases (Werth et al. 2011, p. 2).” We discuss specific examples of rapid increases in spread below. Extreme fire behaviour includes spread rate, energy release, or spatial extent beyond that considered normal or acceptable for a given fuel type under given conditions. The subjectivity in “normal” and “acceptable” renders this definition far from straightforward, and indeed we discuss this perspective on extreme fire behaviour in the chapters on fire regimes. Jan van Rooyen CC0

Figure 2.22: An example of a fire whirl reaching high above a grassland fuelbed. 23

Other terms include eruptive fire behaviour, flashovers, fire storms, and conflagrations (Byram 1954, Viegas & Simeoni 2011). Other definitions focus on changes in fuel strata—e.g., transition from surface fire to entire vegetation complex—in addition to rate and intensity (Viegas & Simeoni 2011).

24

At least outside of specific topographical features like canyons, for which the causes and outcomes of danger are well-known and discussed above.

A blow-up fire is an extreme fire behaviour event that “suddenly, and often unexpectedly, multiplies its rate of energy output many times. . . sometimes . . . in a matter of minutes (Byram 1954).” 23 Several atmospheric conditions have been associated with blow-up fires, such as generally unstable air; rapid development of convection, especially after a weakened inversion; extremely rough topography; and rapid weather changes that introduce high winds and/or low humidity (Arnold & Buck 1954). But understanding patterns between extreme fire behaviour and fire weather has long been confounded by the fact that many blow-up situations occur in the absence of obviously extreme weather conditions (Byram 1954). More recently, Viegas & Simeoni (2011) lament both the number of lives lost to blow-up fires and the lack of progress in scientific understanding of what causes fire eruptions;24 they conclude their review with a call for more research on the phenomenon and a warning that fire safety personnel must stop assuming blow-ups are rare or limited to extreme conditions. Related to blow-up fires are landscape-level mass fires. In mass fires, one or more spatially-discrete fires burning within the same landscape interact with each other, which often results in greater energy release, faster rates of spread, and even altered direction of spread and fires coming together and joining their perimeters (Finney & McAllister 2011).

25

The preservation of angular momentum: “The fire whirl, the dust devil, and other buoyancy whirls are ordinary buoyancy plumes in a vorticity bearing atmosphere and owe their violence in major part to turbulence suppression by centrifugal forces (Emmons & Ying 1967, p. 476).”

Fire whirls are vertical, rotating gas columns found along or near a flame front (Forthofer & Goodrick 2011). The phenomenon is similar to dust devils and is visible because the swirling air carries flame, smoke, and ash (Fig. 2.22); similarly, ash whirls move in previously burned areas and only carry ash. Fire whirls are similar to tornadoes and waterspouts, except that the energy that drives rotation in the fire whirl is released by combustion instead of atmospheric moisture (Goens 1978). A fire whirl begins when a vortex created by rising hot air is concentrated into a narrow area around its own spinning axis by air drawn in by the fire, much as figure skaters spin faster as they draw in their arms (Forthofer & Goodrick 2011).25 Fire whirls can form under almost any fire weather condition given suffi-

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L-R: Blake Scott, USFS; Kyle Smith, USFS; NASA Earth Observatory

Figure 2.23: Three views of the 2018 Ferguson Fire, California, USA. (L) 14 July: Smoke rises above a ridgeline. (C) 1 August: View of the smoke plume from the Incident Command Post. (R) 2 August: Pyrocumulus clouds seen from the International Space Station.

cient flow of buoyant hot air. Fire professionals too often associate fire whirls only with extreme fire weather and rugged terrain, while in reality fire whirls frequently form during prescribed fire operations and on flat terrain (Powell 1963, Haines & Updike 1971, McRae & Flannigan 1990).26 Although Forthofer & Goodrick (2011, p. 91) claim “it is commonly accepted that the formation of fire whirls requires a source of ambient vorticity and a concentrating mechanism,” it had already been established that fire whirls could form in the absence of ambient vorticity—that adjacent flames diverted air indrafts sufficiently to effect rotation around a focal flame that then reached higher than before the vortex (Zhou & Wu 2007). Thus, the concentrating mechanism—the convective movement of buoyant hot air—is likely the primary factor behind fire whirl formation, a factor that can be predicted and observed by wildland fire professionals.27 This seems an example of a misperception identified by Viegas & Simeoni (2011): that extreme fire behaviour is most attributable to a rare or unlikely combination of factors, when in fact instances of dangerous extreme fire behaviour are not unlikely under any conditions, and wildland fire personnel should always be on the lookout for signs of extreme fire behaviour development. Haines & Smith (1987) describe horizontal vortices that generally appear as double or single vortices aligned longitudinally with the flame front, or a single vortex laying transverse. By pushing flames forward, horizontal fire whirls can accelerate fire spread and pose a safety hazard to firefighters.

26

Early publications on fire whirls consisted mostly of anecdotal descriptions of very large vortices (e.g., Graham 1952, 1957, Pirsko et al. 1965). But later Goens (1978) described four size classes of fire whirls and noted smaller ones are quite frequent.

27

Recall convective flow is a consequence of atmospheric instability, which is often given in fire weather forecasts (e.g., Haines Index) and observable on the ground as flames standing upright and minor vortex action (i.e., dust devils) in ash, dust, and leaf litter in the afternoon or after an inversion has lifted.

Finally, pyrocumulus clouds are extreme events that can also drive extreme fire behaviour. Pyrocumulus clouds form when smoke plumes push high above the mixing height (Fig. 2.23). Pyrocumulonimbus clouds are thunderstorms that develop from the intense convection of a wildland fire plume (Peterson et al. 2017). These storms are particularly dangerous because the lightning they produce can create additional ignitions and promote a mass fire event: New ignitions up to 100 km from existing fires were blamed on lightning from pyrocumulonimbus clouds during Australia’s 2009 Black Saturday fires (Dowdy et al. 2017).

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CHAPTER

3

Fire regimes past and present

Cli

Fire regime

ns

For hundreds of millions of years, climate and vegetation shaped fire on Earth. The emergence and global spread of humans initiated a new phase in the control of fire, with substantial impacts on ecosystems as humans first learned to use, and then suppress, wildland fire. While the science of reconstructing pre-historical fire regimes has advanced considerably, several factors of global anthropogenic change—including climate alterations and invasive species—add uncertainty to the trajectory of future fire regimes and challenge wildland fire management.

itio Ign

The fire regime of an area is the product of climate, vegetation, and the pattern of ignitions averaged over a given period of time (Fig. 3.1). Many of the specific parameters of fire regimes—fire frequency, spatial extent, seasonality, intensity, and fire type—can also describe individual fire events.

ma te

Defining and describing fire regimes; evolution of the fire regime concept; reconstructing pre-historical fire regimes

Vegetation Figure 3.1: The three sides of the Fire Regime Triangle. See Fig. 3.2 for specific components with each side of the triangle.

DEFINING AND DESCRIBING FIRE REGIMES According to fire historian Stephen Pyne (1995, p. 3), humans “are uniquely fire creatures on a uniquely fire planet.” As such, the various types of wildland fire28 that occur around the globe can be broadly categorised by their relationship to humans into wildfire, that which ignites and spreads naturally; prescribed fire, that which spreads under parameters defined by humans meant to replicate, to some extent, fire as a natural ecosystem process; and agricultural burning, or fires set by humans primarily as part of farming, ranching, or land-clearing activities. While there is broad variability within each category and considerable grey area between them—much of which is discussed throughout the book, and in this chapter, specifically—we identify the different categories here because the fire ecology literature draws from all types of wildland fire. For example, rare is the ecosystem that has been exhaustively studied under “natural” and managed fire regimes in both the present and in the past. Instead, fire ecologists must often make inferences about the behaviour and effects of wildfires and prescribed burns at relatively narrow points of time to predict

28 We use the term wildland fire with intention. We neither include nor discuss purely anthropogenic forms of combustion, such as domestic biomass burning for heat and cooking, waste incineration, or burning fuel for energy.

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34  Ecology of Fire-Dependent Ecosystems

how fire of either type shaped the ecosystem in other time periods. The limitations and opportunities of this situation are discussed below. Conventionally, the term fire regime refers to a “core group of parameters describing which fires occur when and where according to frequency, size, seasonality, intensity, and type” (Krebs et al. 2010, p. 61). Each of these core parameters is a function of at least one side of the Fire Regime Triangle, which is made up of climate, vegetation, and ignitions (Fig. 3.1).

Figure 3.2: The fire regime concept can be divided into core parameters—those that describe the physical characteristics of either one fire event, or the typical fire event—as well as biological, meteorological, and social factors that modulate fire events, and direct and indirect effects of fire. Figure inspired by Krebs et al. (2010).

At the same time, many parameters are a product of two or more sides of the Fire Regime Triangle and are controlled at multiple temporal scales. Thus, in describing fire regimes, it is difficult to isolate fire-specific parameters from causal factors (e.g., ignition sources, fuel flammability, meteorological conditions such as drought or wind) and fire effects (e.g., smoke plume characteristics, duration of smouldering combustion, depth of heat penetration, vegetation mortality, infrastructural damage, suppression and restoration costs), leading to the conclusion that In a complex process like fire that involves temporal cascades, interactions and feedbacks, every cause is also an effect, every effect may be a causal variable, and no variable is truly independent (Krebs et al. 2010, p. 61). The fire regime concept shapes how researchers, managers, and policymakers consider wildland fire, and has become a fundamental concept even as it defies definition. Krebs et al. (2010) divide modern applications of the term fire regime into strict references to the physical parameters of combustion and fire behaviour, and those that expand the concept to in-

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Core parameters The core parameters of fire regime are the physical parameters of a specific fire, or the typical fire in an area—the classical definition of a fire regime in the strict sense (Krebs et al. 2010).

Intensity

80

25

Fire Size (kha)

clude conditions prior to and during the burn, and subsequent outcomes. Similarly, we delineate core parameters, modulators, and effects (Fig. 3.2).

Spread rate (ha/d)

Fire regimes past and present  35

60 40 20 0

20 15 10 5 0

EU NA

EU NA

Severity Fire type and intensity

While fire type depends on vegetation composition and structure, a given vegetation type does not necessarily lead to a specific type of fire. For example, vast northern areas of North America and Eurasia are covered by boreal forest, but dominant tree species differ in the arrangement and flammability of fuels (Rogers et al. 2015). Thus, North American boreal forest fires are characterised by high-intensity crown fires, while Eurasian boreal forest fires are characterised by low-intensity surface fires (Fig. 3.3).

Proportion

Describing either a single fire event, or the typical fire event in an area, begins with two physical characteristics: fire type and fire intensity. Together, these characteristics describe the distribution of energy released to the environment, which accounts for exposure risk to soil, plant organs, and organisms. Fire type refers to the vegetation layer that burns: ground fires, surface fires, crown fires in the canopy of isolated trees, and running crown fires that spread through the canopy layer (Fig. 2.9). Fire intensity refers to the energy released by the combustion of wildland fuel. Variability in intensity occurs in the magnitude and duration of energy release.

0.8 0.6

●

Eurasia N. America

●

0.4 0.2

●

Crown scorch

Tree mortality Data: Rogers et al. (2015)

Figure 3.3: Two proximate measures of fire intensity (derived via remote sensing) and two measures of severity are all greater in North American boreal forests compared to Eurasian boreal forests. Differences due to greater flammability of black spruce Picea mariana & jack pine Pinus banksiana in North America, which embrace fire, while Eurasian boreal forest is dominated by fire-avoiders.

Intensity is a measurable, objective fire characteristic that refers to the energy produced by combustion. Strictly speaking, intensity is the product of the average energy flux for a given volume and the velocity of energy movement; more broadly, wildland fire intensity is quantified as fireline intensity—energy flux per length of flaming front over time—flame temperature, or released radiative energy (Keeley 2009).

Temporal distribution 29

Seasonality and fire frequency describe the temporal distribution of wildland fire.29 Both can vary with temporal scale. Over long time frames, regional or global climate shifts can substantially alter one or more sides of the fire regime triangle (Fig. 3.1). For example, both fire return interval and seasonal distribution of fires varied substantially between 1600–1900 in longleaf pine savannas of the Southeastern US (Stambaugh et al. 2011). Here, we focus primarily on short-term temporal patterns of fire regimes within discrete periods of relative climate stability, and discuss long-term changes in fire regimes in the next section of this chapter. Of course, global climate changes that impact one or more sides of the fire regime triangle at

Strictly speaking, fire frequency means the number of fire events per period of time, while fire return interval expresses time between fire events. But both terms can refer to either a specific periodicity or an average or typical periodicity, and in few situations do they meaningfully differ. We merge the two, and use the shorter fire frequency but often express it as the time interval between fire events.

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36  Ecology of Fire-Dependent Ecosystems

human-relevant time scales pose an interesting challenge to studying and managing fire regimes, which we discuss in the final section of this chapter.

Fires at different points within seasons can have distinct effects. For example, just three weeks’ difference (early vs. late spring burns) in tallgrass prairie can shift the sward from a broad diversity of C3 graminoids and forbs to one dominated by C4 grasses in Kansas, USA (Towne & Owensby 1984).

Frequency

5 4

Fire

Dry

Wet

3 2 1 0 Spring Summer Fall

Winter

Data: Platt et al. (2015)

Figure 3.4: A distinct fire season with optimal fire weather peaks between dry and wet seasons in Florida, USA. Frequency denotes the percentage of instances in which a given day of the year in the 13-year dataset was assigned to each season.

Seasonality affects burn severity through direct and indirect interactions with phenology—the timing of organisms’ life histories relative to the climate. Generally, physiologically active organisms are more sensitive to fire damage. For example, shrubs that resprout from roots after aboveground biomass burns exhibit less regrowth after growing season burns than dormant season burns because root reserves are depleted by spring growth (Robertson & Hmielowski 2014). Likewise, prickly pear cactus Opuntia phaeacantha mortality is much greater after growing season burns than fires that occur in the dormant season (Ansley & Castellano 2007), likely because the succulent’s physiologically active cells are full of water and more susceptible to lysing—cell bursting—due to heat expansion. Vegetation phenology modulates fire behaviour, which determines the energy to which organisms are exposed. Live fuel moisture content varies seasonally, and for several species, drier fuels ignite more quickly (Fig. 3.5). Recall that greater moisture requires more energy to dehydrate plant tissue ahead of combustion. By absorbing combustion energy, highmoisture fuels reduce fire intensity and can thus reduce heat exposure when fires occur during active growing seasons. In Sardinia, patterns in

species

North America

●

Fuel moisture content (%)

Figure 3.5: Fuel moisture content varies seasonally, which affects combustibility. (L) Live fuel moisture content for five North American trees and shrubs, and three shrubs from Sardinia’s Mediterranean shrublands. (R) As tissue moisture increases, fuels resist combustion, measured here as ignition delay—time taken for fuels exposed to heat to ignite. Combustion energy is required to force fuel moisture out of tissues before raising them to kindling temperature, which can reduce fire intensity.

200 100

●

●

●

●

●

● ● ● ● ● ●

Jun Aug Oct Dec Feb Apr

Sardinia

300

● ●

200 100

Arctostaphylos glandulosa Artemisia tridentata Ceanothus crassifolius Pinus contorta Pseudotsuga menziesii

species

●

● ●

●

●

●

Dec Feb Apr Jun Aug Oct

Cistus monspeliensis Helichrysum italicum Rosmarinus officinalis

North America 25 20 Ignition delay (s)

30

Seasonality describes when in the year a specific fire occurs, or when the typical fire is likely to occur, and is broadly categorised as occurring in dormant or active seasons.30 Fuels tend to be driest in dormant seasons, and thus tend to be more completely consumed and burn at higher intensity (Sparks et al. 2002). In most temperate regions, winter defines dormancy. While tropical and sub-tropical growing seasons are often defined in terms of rainy seasons, meteorological distinctions don’t necessarily best describe fire occurrence. For example, a prominent late spring/early summer fire season peaks between dry and wet seasons in a grassland/savanna ecosystem in southern Florida, USA (Platt et al. 2015; Fig. 3.4).

15 10

40

●

● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ●

50 75 100 125 150

Sardinia

30 20 10 100

200

300

Moisture content (%)

Data: North America, McAllister & Weise (2017); Sardinia, Pellizzaro et al. (2007)

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both wildfires and anthropogenic burning are linked to seasonal variability in fuel phenology (Bajocco et al. 2017, De Angelis et al. 2012). In Texas, USA, seasonal drought reduces live fuel moisture content of Ashe juniper Juniperus ashei (McCaw et al. 2018), which at low fuel moisture burns at greater intensity and suffers greater mortality (Twidwell et al. 2013a). In managed ecosystems, fixed fire seasonality can have long-term effects on species composition even when the general vegetation type remains constant (Fig. 3.6). For example, after 37 years of prescribed fire in Louisiana, USA, pine plantations that burned in spring had both higher stand densities of longleaf pine Pinus palustris and greater herbaceous fuel loads, while stands burned in the summer had less longleaf pine but more hardwoods and loblolly pine P. taeda (Haywood et al. 2001). As a general control on fuel load, primary productivity modulates fire frequency; thus, arid environments tend to have less-frequent fires than areas with more rainfall. In ecosystems with multiple vegetation layers, primary productivity interacts with fire type to shape the fire regime. For example, high-intensity crown fires are generally less frequent than surface fires even under the same amount of precipitation because tree biomass and canopy density take longer to develop than herbaceous fuel loads.

Change in stem density relative to unburned

Fire regimes past and present  37 2

●

1

● ●

0 −1

Rhus copallina Rubus spp. Sassafras albidum Myrica cerifera

March

May

July

Month of burn

Data: Haywood et al. (2001)

Figure 3.6: After 37 years, abundance of several understory species in a longleaf pine forest varied by which month the stand was burned. Y axis is the difference in stem density vs. unburned plots, scaled to account for major differences in magnitude by preserve relative responses.

Ecosystems differ in sensitivity to different fire frequencies. For example, after four decades of prescribed burning in longleaf pine savanna of the Southeastern USA, all fire treatments reduced midstory shrubs relative to unburned stands but 2-year fire return intervals stood out from 1- and 3year fire intervals, with greatest reductions in understory shrub biomass and increases in grasses and overall understory diversity (Brockway & Lewis 1997). Likewise, in mesic grasslands of South Africa and Kansas, USA, that have been burned similarly for several decades, Kirkman et al. (2014) found that differences in grass composition between annual and intermediate fire return intervals (3-4 years) were as great as the differences between burned and unburned plots (Fig. 3.7). Conversely, succession in the boreal forest of Sweden can take a century to play out and distinct compositional stages are associated with time-since-fire periods that can ¨ 1997). span a decade or more (Schimmel & Granstrom Fire frequency affects biological and geophysical dynamics by determining how long communities and properties have to recover after each fire event. Ecosystems characterised by highly-frequent fires—those with short intervals between individual fire events—have less time to accumulate the plant biomass necessary for subsequent fires. While soil carbon storage can decline under high-frequency fires in temperate and tropical regions (Pellegrini et al. 2017), long-term soil carbon levels in the boreal forest can actually increase when fire alters the habitat of soil microbes and decomposition rates decline (Holden et al. 2016). Fire behaviour can depend on community composition changes with timesince-fire. Less-frequent fires allow vegetation succession to advance and potentially alter fuel properties. For example, in northern Sweden, low fuels limit fire spread for the first 20 years after the boreal forest burns, until mosses reestablish and progressively increase fuelbed depth and fire in-

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38  Ecology of Fire-Dependent Ecosystems

Konza (USA) Abundance (% cover)

Figure 3.7: After several decades of experimental management, abundance of individual grass species varies widely across fire regimes in mesic grassland of Kansas, USA (Konza) and KwaZulu-Natal, South Africa (Ukulinga). Intermediate fire return intervals for Konza and Ukulinga are three and four years, respectively. All treatments were spring burns.

60 40

●

●

●

Ukulinga (RSA)

Andropogon gerardii Poa pratensis

Aristida juncea Cheilanthes viridis

●

20

● ●

0

Sorghastrum nutans

Cymbopogon validus

Schizachryium scoparium

Themeda triandra

e d diat rne ual Ann nterme Not bu I

e d diat r ne ual Ann nterme Not bu I

Fire return interval

Data: Kirkman et al. (2014)

tensity; these factors stabilise once 50 years have passed since the previ¨ 1997). In the Southeastern US, where ous burn (Schimmel & Granstrom high moisture promotes both tree growth and litter decomposition, the microbial communities found in longleaf pine savannas after fire seem to resist breaking down herbaceous plant litter (Semenova-Nelsen et al. 2019); accumulated fine fuels promote a surface fire regime of sufficient intensity and frequency to limit pine recruitment. 31

Many fire ecologists bound the duration of heating to the period of time temperatures exceed a threshold assumed to be biologically relevant to mortality, e.g., 60◦ C or 100◦ C. But the concept of the mortality threshold, as well as the accuracy of typical wildland fire sensors in describing time-temperature relationships, is questionable (Pingree & Kobziar 2019, McGranahan 2020). Although meaningfully incorporating heat exposure into wildfire science is challenging, it does not make it less ecologically important.

Duration of heating is often an important temporal characteristic of a fire regime.31 Many organisms have traits or behaviours that allow survival when exposed to heat from wildland fire for short periods of time, but surviving long exposures to even moderate heat often requires specialised adaptations. Thus, the evolutionary history of an ecosystem in which the typical fire event is characterised by long durations of heat exposure is more likely to show fire as a selective force than in an ecosystem in which heat durations are short and fire is less of a selective pressure relative to other environmental factors. Long heat exposures also affect physical properties such as soil structure, organic matter, and moisture content, with knock-on effects on soil organism communities and decomposition rates. Therefore, the duration of heating can have substantial impacts on the distribution of biodiversity as well as ecological structure and function.

Spatial pattern

32

Burned area is a common metric in fire regime research using remote sensing, as fire scars can be observed from space. Behaviour of active fires can be estimated as rate of spread by dividing changes in burned area over time between observations.

Much of the spatial component of wildland fire falls under the purview of landscape ecology; as such, we give it a full discussion in Chapter 9. Here we introduce terminology used to describe the spatial pattern of fire. Extent refers to the total area burned and is thus often reported as burned area.32 For individual fires, extent is simply calculated as the area within a perimeter, which can be defined as either the established lines of a prescribed burn, or constructed and natural firebreaks used to contain a wildfire. For broader areas that might contain many burns, especially over

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Fire regimes past and present  39

time—e.g., a management district, state, or province—extent is the sum of all burned areas, perhaps expressed as average area burned per year. When interested in how populations might respond to a burn, one must understand the spatial pattern of a fire, not just how much area burned (Fig. 3.8). On one hand, species unable to survive fire might repopulate the burned area. The rate at which a given spot is recolonised, and by which species, often depends on the distance to source populations in unburned areas; populations of such organisms would recover more quickly in smaller burned areas. On the other hand, another species might require substantial, contiguous burned areas, and would probably not find a landscape comprised of many smaller burns to be functionally useful.

Completeness of burn refers to how much area within a fire perimeter actually burned. A fire with low burn completeness will have unburned areas where fire did not spread. Such areas might have been protected by fuel gaps created by streams or wetlands, rocky features, areas of intense herbivory, roads or trails, or defensive firebreaks. The resulting landscape is a patchwork of burned and unburned areas; thus patchiness also describes the degree to which a fire did not burn completely.

Figure 3.8: Four different patterns of wildland fire spread. Each landscape has the same spatial extent of fire—50% of the total area of each landscape burned, as denoted by dark grey fill—but they vary in terms of patchiness, completeness of burn, and connectivity of unburned areas.

Whether a burn is patchy or not can be subjective, depending on both the spatial scale of concern and the spatial scale of observation. In the hypothetical landscape examples in Fig. 3.8, one could describe the first landscape as either a patchy burn, with one patch comprising 50% of the total area, or a complete burn, as all cells within the burned patch burned completely. The second and third landscapes certainly burned patchy at the broad scale, but differences in the spatial scale of observation give different perspectives on the degree of patchiness and connectivity between them. Shape of burned areas has implications on post-fire processes such as recolonisation and landscape connectivity. When patches have larger edgeto-area ratios—e.g., are generally more narrow or have a dissected pattern—the average distance from the center of the patch to neighbouring unburned areas is shorter than burns that are more round or square.33 The shorter distances of narrow patches can increase recolonisation rate. Landscape connectivity refers to how easily one could move across a landscape and only encounter similar patches. Wildlife might not be able to use areas of a landscape if certain patches are inhospitable and too wide to traverse. And subsequent fire spread depends on the connectivity of unburned fuels. In Fig. 3.8, wildlife or a flame front could move from top to bottom in the first, second, and third landscapes, although might not be

33

After fires raged in Yellowstone National Park in 1988, most high-severity burn areas were within 50–200 m of unburned areas (Turner et al. 1994). High winds caused the high severity, but recall from Ch. 2 that high wind speeds cause narrow fires (Fig. 2.14).

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40  Ecology of Fire-Dependent Ecosystems

34

This is a central tenet of wildland fuel reduction treatments—managers need not treat an entire area if spatially-discrete treatments can be arranged such that the landscape acts as a barrier to fire spread, or at least reduces its intensity.

35

The original authors conceded the point, but emphasised that managers should not count on patchy fires to protect biodiversity (Griffiths et al. 2015a). If unburned patches are desired, they should be specifically included in the fire management plan.

36

Others include spontaneous combustion of decaying vegetation from the metabolic heat of decomposers, and the rare spark from rocks.

able to access all patches in the third landscape. Movement from left to right is only likely in the second landscape. Movement and spread would be at least slowed, if not blocked, by the narrow areas of contact between unburned patches in the fourth landscape.34

Interactions There are several important interactions between spatiotemporal patterns of fire and fire behaviour that shape fire regime. For example, in the Florida Everglades, seasonality and fire frequency interact to affect the patchiness and intensity of prescribed fires (Slocum et al. 2003). Fires early in the lightning season demonstrate greater variability in spatial extent and fire behaviour. Perhaps counter-intuitively, areas that burned frequently—3-year fire return intervals—also burned intensely, as earlysuccession vegetation tended to be more flammable. A recent debate in the scientific literature highlights the complex interactions between space, time, and fire weather conditions in managing fire regimes for biodiversity. Griffiths et al. (2015b) presented population models suggesting frequent fires—not fire size—in Kakadu National Park, Australia, increased local extinction risk of a small mammal. Russell-Smith et al. (2015) disagreed, citing details on the weather conditions under which fires in that experiment were conducted to argue that fire intensities were higher than typical for the region; as such, fire frequency would not increase extinction risk when fires burn patchily at moderate intensity.35

Ignitions Recall from the Flame Triangle (Fig. 2.1) that there can be no fire without heat. The ways by which heat initially enters the wildland fire environment and raise fuels to kindling temperature can be divided into nonanthropogenic and anthropogenic sources of ignition. Many ecosystems have distinct patterns in the timing, frequency, and spatial distribution of ignitions such that these patterns help define the fire regime. Lightning is the main non-anthropogenic source of ignitions,36 while a number of human activities cause fires, both intentional and unintentional. Both lightning and humans have been setting fires for millenia, shaping ecosystems around the world. While we elaborate on the role of humans in the next chapter, it is important to consider here the potential implication to fire regime due to differences in ignition sources. For example, in recent decades, humans accounted for 84% of wildfires on conservation land in the continental US (Balch et al. 2017), and 87% of fires in Russia’s boreal forests were set by humans (Mollicone et al. 2006). Human ignitions substantially increase annual burned area and alter the seasonality of wildland fire (Nagy et al. 2018, Ganteaume et al. 2013). The relative importance of lightning and humans as ignition sources is especially variable at management-relevant scales. For example, Keeley & Syphard (2018) document complex interactions between geography and population density over time in the frequency of various sources of wildfire ignitions in California since records began in the early 20th century

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Fire regimes past and present  41

Northern California Fire frequency (#/yr/million ha)

300 200 100 0

Lightning

Arson

Smoking

● ●●●●●●●●● ● ● ●●●●●●●

● ● ●●●●● ●●●● ●●●●● ● ●●●●●●● ● ●●

● ● ● ● ●●● ●● ●

●●● ●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●

Southern California

Jurisdiction ●

Federal State

300 200 100 0

●● ● ● ●● ●● ● ● ●●●●● ●●● ● ● ● ● ● ●●● ● ●●●● ●

●● ●● ●●●● ● ● ●●●●● ●●●●●●●●●●●● ●

Figure 3.9: For over 100 years, sources of wildfire ignitions in California have varied by geography and managing agency, natural vs. anthropogenic sources, and have changed over time. Jurisdiction refers to federal land, managed by the US Forest Service, or state land managed by the state fire agency CalFire. These data have been simplified for clarity; arson and cigarette smoking are just two of many human ignition sources reported.

● ●●● ●● ●●●●● ●●●●●●● ●●●● ●● ●● ●●●

1920 1950 1980 2010 1920 1950 1980 2010 1920 1950 1980 2010

Year

Data: Keeley & Syphard (2018)

Lightning Whether a lightning strike starts a fire depends primarily on fuel conditions.38 Overall, the type and condition of fuel that lightning strikes—plant species, canopy or litter, and moisture content (Fig. 3.10)—exert more control over lightning ignitions than lightning activity level; put plainly, “lightning is a necessary but not a sufficient condition for ignition (Latham & Williams 2001, p. 387)”. In general, lightning is inefficient at setting fires due not only to variability in fuel conditions but in the nature of lightning itself. A comprehensive review of lightning and forest fires concluded ignition in wildland fuels depends on continuing current—a sustained, low-amplitude current that develops when clouds have unusually large reservoirs of electrical charge (Latham & Williams 2001). When lightning “strikes”—i.e., an ionised path between cloud and ground is established—temperatures in the zone of the electrical arc exceed the kindling temperature of wildland fuels by a factor of 10. In the few milliseconds of peak current, plant matter in the zone of the electrical arc is ablated—essentially vaporised—but the continuing current widens the channel around the arc, and sustains heating at temperatures between pyrolysis and ablation, which can ignite wildland fuels. Not all lightning strikes are equal. Lightning carries either a negative or positive charge, and the likelihood of a continuing current varies. Data primarily from the western US indicate continuing currents follow most positively charged flashes, but only about 10% of cloud-to-ground charges are positive; meanwhile, only about half of negative cloud-to-ground strikes are followed by a continuing current (Latham & Williams 2001).

37

More ignitions on state-managed land is likely due more to proximity to human population than policy or management differences between federal and state agencies. Keeley & Syphard (2018) note that US Forest Service lands are at higher elevation and are likely more remote. 38

Wildfires caused by extreme lightning activity receive a lot of attention—e.g., Australia’s 2009 Black Saturday wildfires that killed 173 persons and destroyed 2000 homes—but the severity of such lightning-caused events often derives from interactions between fire weather and drought (Dowdy et al. 2017).

Fuel moisture content (%)

(Fig. 3.9). Lightning ignitions dominated land managed by the US Forest Service in Northern California, but was less frequent in southern areas and northern forests managed by the state fire agency. Arson and other anthropogenic ignitions were more frequent in the state-managed areas.37

40 30

● ● ●● ● ●● ● ●● ●●●● ●

20

● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ●● ● ●● ●● ●

10 0

200

● ●●

Ignition? ●

No Yes

400

600

Duration of exposure (ms)

Data: Latham & Williams (2001)

Figure 3.10: Electrical arcs are more likely to ignite Ponderosa pine needles when the fuel is dry and exposed longer to the current.

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42  Ecology of Fire-Dependent Ecosystems

Subsequent work, however, suggests the roles of continuing current and polarity are less clear. Strikes with both polarities have similar ignition probabilities in Florida, USA: 92% of cloud-to-ground strikes had a negative charge, and caused 93% of ignitions (Duncan et al. 2010). Larjavaara et al. (2005) report equal proportions of positive and negative charges in Finland, and no difference in the probability of ignition due to polarity.

Fire regime modulators and fire effects The core parameters of wildland fire regimes are sometimes insufficient to characterise context-dependent fire patterns, or are inadequate to describe important responses to wildland fire. Thus, the concept of wildland fire regimes is often expanded to include environmental conditions; socially determined conditions such as other management practices and policies that influence core parameters; and typical physical, biological, and societal responses to fire (Krebs et al. 2010). We divide these associated factors into modulators, which influence the fire environment prior to and during combustion, and effects, which include direct and indirect impacts of combustion and heating in the fire environment after fire passes.

Figure 3.11: Biophysical and social modulators of the wildland fire environment that occur prior to and during fire. These modulators are often considered in broad concepts of wildland fire regimes (Krebs et al. 2010). 39

Antecedent weather—weather in one season affects fire in another. The 2017 fire season in California, USA, was devastating after an exceptionally wet growing season generated high fuel loads and was followed by an exceptional drought, which cured the accumulated fuels (Nauslar et al. 2018). In eastern Iberia, greater burned area in dry season fires lags two years behind high summer rainfall (Pausas 2004).

Modulators of fire regime Wildland fire is modulated by biophysical and social factors that determine the context of a fire event prior to and during the burn (Fig. 3.11). Weather and management affect fuel load and availability.39 Once ignited, fuelbed conditions and local fire control policies determine if and how fire spreads.

Biophysical modulators Biophysical factors that modulate fire regime include climate and weather; soil and topography; and plant community composition and productivity. Recall from Chapter 2 that these are the factors in the Fire Behaviour Triangle (weather, topography, and fuels; Fig. 2.10); indeed, they determine how flames spread during a specific fire event. Here, we focus on longterm or spatially broad patterns that set the context for fire in a specific area for a given period of time, and as such help shape fire regimes. Climate is an important modulator of wildland fire regimes. Long-term climate trends drive vegetation composition and productivity, which affect fire type (via vegetation structure and fuel size class distribution), as well as intensity and frequency (fuel loads). Short-term climate trends and weather events such as seasonal drought and local temperature and humidity patterns affect fire behaviour. Moisture and temperature trends also affect local weather patterns that in turn determine ignition frequency (lightning) and fire intensity (wind speed and relative humidity). Physical factors such as soil and topography can also modulate fire

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regime. In the longleaf pine savanna of the Southeastern US, soils modulate fire frequency effects on understory composition (Glitzenstein et al. 2003). Fires tend to burn more frequently and more intensely on plains, ridges, and sun-exposed slopes (Wood et al. 2011, Takaoka & Sasa 1996). Interactions between biophysical modulators can create characteristic patterns. For example, in mountainous regions worldwide, topography and local air flow combine to create foehn winds—strong down-slope winds that drive intense or even extreme fire behaviour (Sharples 2018).40 In West Africa, the Harmattan wind has long been associated with widespread dessication and fires (Dobson 1781, Jen´ık & Hall 1966). And fire patterns in the Florida Everglades are shaped by climate dynamics that follow the ˜ decadal scale of El Nino—hot, dry periods with high lightning activity levels alternate with cool, wet periods with less lightning activity (Beckage et al. 2003; Ch. 7 p. 108). Large fires caused by human ignitions are disproportionately driven by high wind speeds, while large lightning-started fires are associated with hot, dry conditions (Abatzoglou et al. 2018).

40

Santa Ana winds in southern California, USA, and “sundowner winds” caused by the Catalina Eddy further up the coast have fed dozens of severe wildfires (Westerling et al. 2004, Ryan 1996). “Bergwinds” are foehn-like winds that drive fire in South Africa (Geldenhuys 1994); similar winds increase fire danger in the Australian Alps (Sharples et al. 2010).

Social modulators Several social factors modulate wildland fire. These include management—e.g., human activity on a site that affects vegetation composition, structure, and overall amount—and policy—practices and regulations that determine fire use.41 Social influence increases as humans expect more productivity, or a greater variety of products, from fire-prone landscapes. Harvest of wildland products is a major potential modulator of the core parameters of fire regimes. Timber harvest has major impacts on fuel structure, load, and moisture. Logging removes a substantial portion of very large fuel particles—tree boles, large branches, and other 100- or 1,000hour fuels—and converts much of the high, living canopy fuels to dead, dry surface fuels (often known as “slash”). Thus logging can both reduce the connectivity of canopy fuels and increase the connectivity of surface fuels.

41

One must also consider how human–climate interactions affect wildland fuel conditions. We address human influences on fire regimes in the next chapter.

Herbivory modulates core fuel parameters including fuel load, size class distribution, and connectivity. Herbivory effects on fire behaviour depend on the degree to which herbivores focus on the primary fire-carrying fuel layer. For example, targeted cattle grazing on cheatgrass Bromus tectorum—which increases fine fuel load and connectivity when it invades otherwise patchy arid ecosystems dominated by Wyoming big sagebrush Artemisia tridentata var. wyomingensis—reduces fine fuel load and dampens fire behaviour (Diamond et al. 2009). But cattle grazing in heathlands of the Australian Alps had little effect on fire behaviour (Williams et al. 2006), likely because cattle forage on fine herbaceous fuels and fire in this ecosystem spreads primarily through the shrub layer. In fire-prone shrublands, herbivores that prefer to forage on woody plants—e.g., goats and deer (Lovreglio et al. 2014, Lecomte et al. 2019)—more effectively reduce fuel loads and disrupt vertical continuity between surface and canopy fuels.

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44  Ecology of Fire-Dependent Ecosystems

Wildland fire effects 42

This is similar to the National Wildfire Coordinating Group’s distinction between “first-order” and “second-order” fire effects (online glossary, NWCG 2019).

There are both direct and indirect effects of combustion and heating.42 We discuss fire effects on soil and organisms in Part II. Here we review general differences between direct and indirect effects, and intensity and severity.

Direct effects Direct, or first-order, fire effects refer to the immediate impacts of heating on soil, water, and organisms. Because exposure to the heat from combustion is the primary driver, direct effects generally have a positive correlation with fire intensity, i.e. the amount of energy released and available to increase heat exposure in the fire environment. But the actual impacts of a given amount of energy depend on the environmental context. Severity describes physical or biological impacts of heat exposure. It is a subjective concept that can be difficult to interpret and compare among—and even within—fire events. Severity depends heavily on environmental conditions at the time of heat exposure and the sensitivity of soil, organisms, or infrastructure to heating (Keeley 2009). Thus, two fires of the same intensity—an objective measure of energy release via combustion—could vary widely in severity depending on which fuel layer the fire burns through, and specific traits of affected organisms. Severity can be influenced by modulating factors. For example, soil moisture modulates soil heating effects—due to the high specific heat capacity of water, wet soils absorb more combustion energy and resist physical degradation and organic matter combustion. Thus, the severity of soil heating depends on moisture content even when fire intensity—an objective fire characteristic—is constant. But heating rate and aggregate stability vary with soil composition, meaning that even under constant heat input and similar initial moisture conditions, observed burn severity—e.g., grass bud mortality or fine root loss—can vary among different types of soil. Severity can also be complicated by the amount of time between the fire and measurement of effects. For example, it is common to measure burn severity as the proportion of organic matter or size class of woody material consumed by a fire (Williams et al. 2006, Bergner et al. 2004). In the case of plant species that resprout after fire, or animals that flee ahead of flame fronts and quickly recolonise revegetated areas, a stand in which 100% of aboveground biomass is completely lost to combustion would show very high severity immediately after the fire but much lower apparent severity, if sampling occurs after plants resprout or animals recolonise.

Indirect effects Indirect, or second-order, effects refer to vegetation succession, plant and animal community composition, and altered productivity and nutrient dynamics after fire. Indirect effects are first determined by first-order effects,

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and variability in fire type or intensity can alter the trajectory of succession or rate of nutrient cycling. But indirect effects are also modulated by environmental variables, such as post-fire precipitation and management. Indirect effects can also include social responses to fire, such as suppression and management policies. These are discussed in Part III of this book.

THE GLOBAL FIRE FOOTPRINT Fire has been a critical process in the Earth system for hundreds of millions of years, ever since plant life evolved to prop up the Flame Triangle (Fig. 2.1) with oxygen and biomass for fuel (Bowman et al. 2009). Fossilised carbon first appears in the paleological record in the late Devonian period, and global fire activity rose and fell with atmospheric oxygen concentrations (Scott 2000, Pausas & Keeley 2009). Fire shaped the evolution of life and the processes that maintain biodiversity (Pausas & Keeley 2009, He et al. 2019). The role of fire in plant evolution is widely recognised (He & Lamont 2017), and evidence suggests fire contributed to the evolution of animals, as well (Pausas & Parr 2018).

Extant fire regimes The footprint of fire reaches around the world. An estimated 350–464 million hectares burn each year, 80% of which occurs in woodlands and shrublands (Randerson et al. 2012, Tansey et al. 2018). More than 30% of fire-affected areas burn very frequently (Chuvieco et al. 2008). As a “global herbivore,” fire shapes the ecological dynamics of ecosystems (Bond & Keeley 2005). Soil properties, local nutrient cycling, and belowground communities are ˜ et al. 2018, Pressler et al. also affected by fire (Butler et al. 2018, Alcaniz 2019). Likewise, emissions from fire affect global nutrient cycles, climate trends, and weather patterns (Jacobson 2014, Liu et al. 2014, Littell et al. 2016, Lasslop et al. 2019).

Class & Description 1 Infrequent light surface fires (> 25 yrs) 2 Frequent light surface fires (1–25 yrs) 3 Infrequent, severe surface fires (> 25 yrs) 4 Frequent (25–100 yrs) crown fires + severe surface fires 5 Infrequent (100–300 yrs) crown fires + severe surface fires 6 Rare (> 300 yrs) crown fires + severe surface fires Table 3.1: Forest fire regime classes for northern North America (Heinselman 1981). Kilgore (1987) presented a similar classification that used “stand-replacing fires” instead of “crown fires”. Pyne et al. (1996) gives North American vegetation type examples for each class. Years refer to fire return interval.

Describing fire regimes Two approaches to fire regime description include directly observing fire occurrence and vegetation response, and using remotely-sensed data from earth observation systems.43 Prior to satellite data, fire regimes were described using physical fire metrics—fire type, season, intensity—and fire effects—severity, resulting vegetation types. For example, Heinselman (1981) presented six categories for forests of northern North America based on fire type, severity, and frequency (Table 3.1). This approach generally produced narrative-driven, community-specific descriptions of fire regimes focused on particular regions (e.g., Wright &

43

Satellites have been equipped with sensors to detect both active fires, from infrared radiation, and recent fires, based on contrasting colours between a fire scar and the surrounding vegetation. Randerson et al. (2012) is a good example.

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46  Ecology of Fire-Dependent Ecosystems

Bailey 1982, Pyne et al. 1996, Bradstock et al. 2002). Such fire regime descriptions allow local managers to predict how vegetation might respond to a particular fire management plan, or conversely, determine how to create a fire management plan that promotes a certain vegetation type.

44

Fire behaviour estimates are often intensity/severity, based on the amount of radiation or degree of contrast detected. When active fires are being monitored, rate of spread can sometimes be calculated as increase in burned area over a known time period.

Remotely sensed earth observation data from satellites allow analysis of information on fires at a global scale. The broad scale and the nature of earth observation tools make fire regime classifications from remotelysensed data different from previous ground-based classifications in two respects: Firstly, spatial and temporal parameters can be incorporated alongside estimations of fire behaviour, typically as burned area and seasonal duration of fire activity, respectively.44 Secondly, fire regime classifications are made independent of vegetation type and can thus be generalised worldwide. Associations between fire regimes and vegetation types can be made after the fact (e.g., Chuvieco et al. 2008; Table 3.2). Such fire regime descriptions are particularly useful for determining emissions from wildland fire. When combined with land cover data and emissions values for vegetation types, spatio-temporal patterns of fire emissions can be established (Schultz et al. 2008, van der Werf et al. 2010). Fire regime parameter

Table 3.2: Eight global fire regimes identified by a “3-D” model of fire activity on Earth derived from from remotely-sensed data, ordered by percent coverage of Earth’s terrestrial surface (Chuvieco et al. 2008). Description of parameters Each individual grid cell in the spatialised data is assigned to a category (high or low) for three parameters: Density = mean monthly fire activity, as counts of fires per cell per month. Duration = length of the fire season, as the duration of fire activity in the cell. Brief < 6 mo, Long > 6 mo. Deviation = variability in fire activity from year to year, as the standard deviation of mean annual fire density.

Area

Main vegetation types

Density

Duration

Deviation

High

Brief

High

24%

Boreal forests; tropical savanna fires in lessdeveloped regions

Low

Long

Low

21%

Agricultural fires in developed regions (temperate & tropical wet)

High

Long

High

20%

Savanna fires and & deforestation in dry tropics

Low

Brief

Low

19%

Various types of fires in developed regions

Low

Brief

High

6%

Boreal forest fires

High

Long

Low

6%

Periodic fires in tropical wet zones; fires in savannas & agricultural areas

High

Brief

Low

3%

Burning residual biomass

Low

Long

High

1%

Various fire types

Broad patterns Chuvieco et al.’s 3-D model shows most fire occurs in boreal forest and tropical savannas (Table 3.2), which is consistent with other summaries of fire’s global distribution (Dwyer et al. 2000, Rogers et al. 2015, Tansey et al. 2018). Brief duration and high deviation in boreal forests reflect the

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Fire regime categorisation

Fire physical characteristics

Dominant biomes (% of global area/pyrome)

Frequency

Intensity

Extent

FRI

FRP

BA

Duration

High

High

Large

3

473

414

4

Flooded grasslands (46%), Tropical grasslands & shrublands (29%)

High

Low

Small

1

197

25

3

Tropical grasslands & shrublands (33%), Tropical dry broadleaf forests (21%)

Low

High

Long

> 50

476

83

2

Boreal forests (46%), Xeric vegetation (31%), Temperate coniferous forests (26%), Mediterranean vegetation (25%)

Low

Low

Small

> 50

187

4

1

Boreal forests (47%), Temperate coniferous & mixed forests (45 & 41%), Mediterranean vegetation (36%), Montane grasslands (31%)

Moderate

Low

Small

12

224

9

3

Tropical forests (coniferous 59%; broadleaf, moist 56% & dry 53%), Temperate mixed forests (45%), Temperate grasslands & shrublands (37%), Flooded & montane grasslands (36% & 35%)

short snow-free season and long fire return intervals of northern regions (Oris et al. 2014). Burned area and fuel consumption in tropical savannas and woodlands are characterised by considerable variability depending on seasonality and source of ignitions (van Leeuwen et al. 2014). Chuvieco et al.’s 3-D model reflects this variability by associating these vegetation types with several fire regime categories (Table 3.2). More recently, Archibald et al. (2013) defined global pyromes using five characteristics from remotely sensed data (Table 3.3). While the delineations were made without regard to vegetation type and considered only physical fire characteristics, using five parameters generally achieved a better fit with vegetation than the 3-D model (Chuvieco et al. 2008). For example, the pyrome model distinguished extensive, high-intensity savanna fires in Australia from smaller, lower-intensity savanna fires in Africa (Archibald et al. 2013). However, the pyrome model was not able to resolve the two major boreal forest fire regimes—stand-replacing crown fires in North America vs. less-intense surface fires in Eurasia (Rogers et al. 2015; Fig. 3.3)—despite the underlying data capturing the different intensities.

Table 3.3: Five global pyromes—fire regime classifications based on five remotely-sensed physical characteristics of fire discriminated in a manner similar to floristic biomes (Archibald et al. 2013). Note that this table excludes one of the originally reported physical characteristics (mean burned area) because it had low variability and was not terribly illustrative of differences in fire regimes. Description of the four included characteristics: FRI = Fire return interval. Mean years between fire events. FRP = Fire radiative power. Maximum rate of energy release (MW), a remotely-sensed estimate of intensity. BA = Burned area. Maximum fire size (km2 ). Duration = Median length of fire season, in months.

Over 20% of global burned area in the 3-D model is associated with agricultural fires in developed regions (Table 3.2). This counters conventional wisdom that developed countries have largely abandoned fire as an agricultural practice in the post-Enlightenment industrial era (e.g., Pyne 2016). In fact, over 1.2 million hectares of cropland burn in the US alone, amounting to 43% of annual area burned in wildland fires (McCarty et al. 2009).

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48  Ecology of Fire-Dependent Ecosystems Figure 3.12: Fire scars in tree boles mark fire events. (L) Fire burns up a bole in the 2013 Rim Fire (CA, USA). Fire scars form without killing the tree. (C & R) Fire scars from Ponderosa pine trees cut in 1930. Decades, as determined from growth rings, are marked incrementally from the centre out; fire years are marked near their respective fire scar along the right. (C) Dates from 1680, with six fire scars; (R) dates, incredibly, from 1255, with 24 fire scars. These data were used to reconstruct climate cycles in eastern Oregon (Keen 1937).

L: M. McMillan, USFS; C & R: W.J. Buckhorn, USFS, 1937

Reconstructing past fire regimes

45

Our discussion of pre-historic fire regimes focuses on the Holocene (most recent 12,000 years; Marlon et al. 2013). For a longer perspective, we recommend Fire Phenomena and the Earth System, by Belcher (2013).

Considerable effort is put in to reconstruct global fire regimes throughout our planet’s history.45 Here we review the methods used to reconstruct past fire regimes and describe their insights.

Dendrochronology Woody plant biology makes trees inherent calendars, and these calendars can include the dates of past fires (McBride 1983). Each year, a tree expands its trunk, or bole, with a ring of tissue; the number of rings equates to the age of the tree. Furthermore, the width of each ring generally relates to the quality of growth conditions in that year. When many trees from an area overlap in the time periods through which they grew, scientists can cross-reference patterns in the fluctuating width of rings to match up years and estimate climate conditions several hundred years into the past.

50

Season of fire Early

Number of scars

Late Dormant Undetermined

25

0

1653−1829

1830−1890

1891−1940

1941−2011

Pre−settlement Settlement Development Suppression

Period

Data: Stambaugh et al. (2017)

Figure 3.13: Fire frequency and season from fire scars collected from Bastrop (Texas) 1653−1829 State Park, USA. Width 1830−1890 1891−1940of bars 1941−2011 scaled by duration of each period; actual years given below the bars. Numbers above the bars represent the4.3average number of fires per decade within each period. “Settlement” refers to European colonisation. 1653−1829 1830−1890 1891−1940 1941−2011

Many dendrochronological samples include evidence of fire, especially in ecosystems characterised by surface fire regimes. Surface fires can climb up the downwind or uphill side of a tree and burn through the outer bark, but without burning fully around the tree and girdling it, the tree will survive. The tree will compartmentalise the burned area, which can suffer disease and rot, by covering it with new tissue; this can happen repeatedly through the life of the tree (Smith & Sutherland 1999). When a cross section is obtained, fire scars can be cross-referenced with the dendrochronological record to determine when recorded fire events occurred (Fig. 3.12). Fire scar data can be rich in information. By specifying where in the trees’ annual ring development fire scars occurred, Stambaugh et al. (2017) described altered fire seasonality as a forest in Texas, USA transitioned from Native American inhabitation through three phases of European settlement (Fig. 3.13). More generally, fire scars record not only the number of fires over the course of a tree’s life—giving an average fire frequency for that period—they also record the return interval between each fire—allowing periods of different fire frequencies to be determined (Fig. 3.14).

2 0.6

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1830−1890

1891−1940

1941−2011

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Fire regimes past and present  49 Figure 3.14: Two examples of fire histories spanning the mid-17th to late 20th centuries reconstructed from tree fire scars. Flame emojis denote fire scars, grey bars span the sampled period of individual trees, and the “rug” of tick marks along the bottom summarises the number of fire scars per year across all sampled trees. Note period of frequent fire in Poland followed by fire exclusion resulting in a widespread fire event ca. 1875. Conversely, fire frequency in Oklahoma increased as the Cherokee Nation was forcibly relocated to the area.

Data: Poland, Niklasson et al. (2010); Oklahoma, Stambaugh et al. (2013)

Several studies have used fire scars to demonstrate how changes in human settlement patterns and management have altered fire regimes. European settlement in North America has been associated with increased fire frequency for a period along the Missouri River (Stambaugh et al. 2006) and decreased fire frequency in Southwestern forests (Touchan et al. 1995). Niklasson & Drakenberg (2001) associate changing fire frequencies with shifts in plant species composition in southern Sweden. Spatial patterns can also be inferred when enough trees in a landscape are sampled. By cross-referencing dendrochronological dates, fire scars can be connected to specific fire events, revealing the spatial extent and even severity of individual burns (Falk et al. 2011).

Sediments Sediment deposits from lakes and wetlands also trap charcoal, which provides an alternative record of fire regimes. While a fire scar can be precisely located in time and space, dates and distances for charcoal are fuzzy (Whitlock & Anderson 2003). Sediment records capture information on different scales. Spatially, charcoal derives from run-off within the same catchment, an indicator of local burning; or aerial deposits that fall out of smoke plumes from fires upwind of the water body. Temporally, primary charcoal is that carried into the water body immediately after a burn, while secondary charcoal accumulates from post-fire run-off events. Charcoal deposit records extend further into the past than fire scars—sometimes tens of thousands of years,46 while most dendrochronological records are limited to a maximum of 500–1000 years. Such data help connect long-term patterns in climate, plant community composition, and fire regimes. For example, Olsson et al. (2010) used charcoal from peat and lake sediment in Sweden to reconstruct a 10,500-year fire history that connected fire frequency to climate patterns in the first part of the Holocene, and patterns of human settlement since 750 BC.

46

Charcoal from a core of marine sediment off the coast of Namibia informed a 170,000-year fire record for southern Africa (Daniau et al. 2013).

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CHAPTER

4

The role of humans Prehistoric and indigenous burning; colonial and post-colonial fire management policies; human modifications of natural fire regimes; future fire regimes and novel ecosystems

Two points about fire, our planet, and our species are nearly incontrovertible: (1) Fire has long played a role in the Earth system, both biologically and geophysically; and (2) Humans Homo sapiens have had a close relationship with fire since our species evolved, first around the hearth, where we cooked food and socialised, and then on the landscape, where we intentionally burned to advance our goals. But the extent to which human fire use affected the global fire regime prior to the industrial era remains up for debate. However, it is well established that humans today are altering ecosystems and the global climate at an unprecedented rate. There is also ample evidence that humans drove fire regimes at regional scales around the world. Understanding how fire regimes responded to climatic changes through Earth’s history, and how humans increasingly modulate this relationship, helps manage fire under imminent global change scenarios.

A BRIEF REVIEW OF HUMANS AND FIRE Fire historian Stephen Pyne lists the discovery and adoption of fire by humans as one of three major events in the history of fire on Earth.47 The archaeological record suggests the “long and convoluted process” of fire adoption passed through three stages (Gowlett 2016): Firstly, humans sought to forage for resources in burned landscapes; secondly, humans domesticated fire in the hearth for protection and cooking; and thirdly, humans applied fire in technological processes for toolmaking and pottery. As human prowess with fire increased, many peoples used it to shape their landscapes, with ecological legacies that persist today.

47

The Three Fires of Pyne (2001): First Fire—natural burning emerges ca. 400 mya as plants evolve and oxygenate atmosphere; Second Fire—humans learned to use biomass burning to their advantage; Third Fire—industrialised fire, in furnaces, engines, and bombs.

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52  Ecology of Fire-Dependent Ecosystems

Indigenous burning

48

The cultural connections between fire and humans are elaborated upon in Ch. 10 and only briefly reviewed here.

Humans have used fire for nearly as long as they have existed, and with increasing complexity as cultures developed and populations expanded.48 Initial fire use could date to 1.5 million years ago in Africa and 400,000 years ago in Europe; fire was in wide use by 120,000 years ago (Gowlett 2016). Fire uses included cooking, tool-crafting, and smelting (Bentsen 2014, Attwell et al. 2015). Landscapes were burned not only for hunting and land-clearing, but also in the intentional management of plant-based resources (e.g., berries, basket materials) and swidden agriculture (Jones 1969, Anderson 1999, Rue et al. 2002, Nigh 2008, Coughlan 2015). While it is clear that pre-historic humans used fire, it is not clear that anthropogenic burning affected fire regimes at a global scale. Patterns in the paleo-fire record more closely align with climate patterns than what’s known about human populations and land-use (Marlon et al. 2013). For humans to alter fire regimes, anthropogenic burning must overcome the influence of natural controls on fire (Whitlock et al. 2010). The greatest increases in fire activity are in areas with infrequent natural ignitions and synchronous, fire-conducive fuel moisture and weather conditions. Fuel load must either be naturally adequate or human activities must increase flammability, such as shifting vegetation towards more herbaceous fuels. The influence of climate on pre-historic fire regimes was likely often equal ´ to, or greater than, anthropogenic impacts (Mendez et al. 2016, Marlon et al. 2008). Thus, pyrogeographers have scrutinised the conventional wisdom that indigenous burning had broad-scale effects on vegetation regardless of its cultural importance or frequency (e.g., Barrett et al. 2005). However, there are several examples of indigenous fire use altering local fire regimes, sometimes with substantial impacts on biodiversity: ¯ • When the Maori first settled New Zealand’s South Island nearly 1000 years ago, fire was rare in the indigenous forests. After the onset of the Initial Burning Period, it took only a few decades of anthropogenic burning to shift much of the island’s vegetation to the open grasslands that exist today (McWethy et al. 2009, Perry et al. 2012).

• In western Australia, the Martu people intentionally burned spatially49

Notably, patch-burning was apparently meant to boost success of women hunting burrowing animals, and contributed little to the hunting success of men (Bird et al. 2005).

discrete patches to facilitate hunting;49 cessation of the indigenous fire regime in the 1960s increased the spatial extent and variability of fires and threatened native small mammals (Bird et al. 2012).

• Mediterranean ecosystems in both California and the Mediterranean Basin have long been “cultural landscapes,” in which anthropogenic fire has been used for thousands of years to shape vegetation, often towards plant communities more favourable to livestock grazing (Timbrook et al. 1982, Naveh 2007, Keeley 2002, Marriner et al. 2019).

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The role of humans  53

Fire suppression—from the colonial era to Smokey Bear As European culture developed, the human relationship with fire changed. Pyne (2016) describes the arc from natural philosophy to reductionism.50 Plato and other ancient philosophers considered fire one of four elements, along with earth, air, and water. Enlightenment-era science reduced fire from a mystical, elemental phenomenon to a product of chemical reactions. A growing human population required efficient use of natural resources to produce food and drive industry. Once alternative sources of nitrogen fertiliser emerged—manure regimes, guano from caves and far-off islands—fire was no longer central to agriculture.

50

Readers interested in an extensive discussion of fire use and disuse by humans—what Scott et al. (2013) call the “pyric transition“—should check out their book Fire on Earth: An Introduction.

Fire was primitive, science was progress—such was the European attitude when met with ancestral forms of anthropogenic burning in the colonial era. From the outset, accounts of indigenous fire use were disdainful of what was perceived to be ignorant and wasteful burning; colonial authorities criticised destruction of soil and forest resources (Krebs et al. 2010). In fact, racist colonial missives introduced a term that has become a fun´ damental concept in ecology—regime. Regime was used by at least 11 observers in French colonies between 1828 and the 1930s (Krebs et al. 2010). The term likely entered English from French via some combination of inter-colony contact and the education of many British foresters in France.51 For example, Hearle (1888) retained the accent and italics when ´ describing indigenous pastoral burning in colonial India as a “regime of fires” that jeopardised the colonial authority’s forestry projects.

51

As well as at least one American, Gifford Pinchot, the first Chief of the US Forest Service.

Fire suppression was thus a pillar of natural resource management in Europe, her colonies, and eventually Westernised post-colonial governments. In colonial South Africa, repeated convictions for deliberate burning risked capital punishment (Botha 1924). In Australia, European settlement disrupted indigenous patch burning, and Aboriginal peoples were moved to settled areas under fire suppression policies (Edwards et al. 2008). Intensive livestock management replaced pastoralism, and agricultural burning declined (Burrows et al. 1995). In the US, the nascent Bureau of Forestry condemned fire as a destructive force (Foley 1903). Seeking legitimacy among congressional and industry detractors, the fledgling US Forest Service responded to the massive Western wildfires in 1910 by pledging to protect the nation’s timber resources via total fire suppression (Egan 2009). Fire suppression policies of the early 20th century left a strong legacy, both on landscapes and public perceptions of fire. Donovan & Brown (2007) summarised the issue in the title of their review Be careful what you wish for: the legacy of Smokey Bear—Decades of fire exclusion have allowed hazardous levels of fuels to accumulate, making subsequent fires increasingly difficult—and increasingly costly—to suppress. While this “fire deficit” could be reduced through managed burning, overcoming social and political barriers to wildland fire use is challenging (Kolden 2019).

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54  Ecology of Fire-Dependent Ecosystems

Vegetation Table 4.1: Ways in which humans alter the three sides of the Fire Regime Triangle (Fig. 3.1).

Primarily Fuel effects: • Build-up via fire exclusion • Reduction via grazing, harvest, or hazard fuel treatments • Altered structure from management, or lack thereof • Altered continuity and moisture due to invasive species

Ignitions

• Suppression of natural starts • Increased starts (arson, accident, land-clearing)

• Altered seasonality Climate Primarily Global change effects: • Altered periodicity and intensity of rainfall and drought • Enhanced temperature extremes

Anthropogenic alterations

52

Increasingly emotional media coverage of wildfire events might also contribute to perceptions of greater wildfire activity (Yell 2010).

Stuart Palley, USFS

Figure 4.1: The drip torch has become a ubiquitous symbol of sanctioned human ignitions, often used by wildland fire professionals in prescribed burns. Firing devices allow a high degree of precision in implementing ignition plans. This crew member conducts firing operations during the 2017 Thomas Fire in California, USA.

Public attention to wildfires might create a perception that humans increase fire activity,52 but global burned area has in fact recently declined (Andela et al. 2017). Development in fire-prone ecosystems exposes humans to wildfire, enhancing the perception of greater wildfire activity despite lower burned area worldwide; only as humans encroach on previously unpopulated areas does fire frequency increase (Knorr et al. 2014, 2016). In most wildfire-afflicted areas, humans reduce fire frequency, which increases the severity of individual fire events, feeding the conventional wisdom of a wildfire problem. Accounting for the discrepancy between increasing severity and decreasing burned area requires scrutiny of how human impacts on the fire environment alter the components of fire regimes. Humans have substantially altered the spatial and temporal distribution of fire. For example, in expanding the spatial and seasonal “fire niche” in the US, humans took over as the source of 84% of wildfires and 44% of total area burned (Balch et al. 2017). While changes in the spatial-temporal distribution of fire are complex, alterations can be attributed to specific modifications of the Fire Regime Triangle (Table 4.1). Humans alter the spatial extent of fire by modifying landscape configuration, fuel continuity, and the location and density of ignitions (Fig. 4.1). Humans alter the temporal distribution of fire by modifying fuel accumulation rates and the seasonality and frequency of ignitions. For example, diverging patterns of fire deficits in forested areas and surpluses in non-forested areas of the western US can be attributed to modification of ignitions (fire exclusion), and modification of fuelbeds (invasive annual grasses), respectively (Parks et al. 2015). Discussing anthropogenic wildland fire is complex. Peoples all over the world have different reasons to start or extinguish fires, and the evolution ´ of regime to “regime” makes clear the subjectivity in valuing fire as “good” or “bad”. One approach would be to divide fire into unintentional and intentional, in which the former sums up wildfire and the latter all fire that begins with a human purpose. This dichotomy fails to describe natural starts that are allowed to burn through wilderness areas under certain conditions, and deforestation burns that are conducted outside of any management system or ecologically minded fire regime. When necessary, we differentiate fire

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ID

From

I

Frequent, low severity

Infrequent, Fire exclusion allows fuel build-ups to high drive high-severity crown fires instead severity of frequent, low-severity surface fires.

• “Fire deficit” in western US forests1 • Wildfires in southern Europe2 • Juniperus invasion, US grasslands3

Frequent, low severity

Ignitions fully suppressed, prevented, or culturally/politically discouraged. Land use precludes fire spread, e.g., heavy livestock grazing on rangelands.

• Eastern US hardwood forests4 • Woody encroachment in grasslands

II

To

Very little to no fire

Description

Examples

and savannas worldwide5 • US Southwest, N. Great Plains6

Spatially discrete, III moderate severity

• Megafires, extreme wildfire events7 Ecosystems adapted to standExtensive, replacing but spatially limited fires now • Cheatgrass Bromus tectorum inhigh vasion of sagebrush steppe (Great burn extensively, suffer biophysical severity Basin, USA)8 degradation that impairs recovery.

Infrequent IV or rare natural fire

Highseverity burns

Ignition- or moisture-limited forests in wet tropical or boreal regions burned to clear land for farming, mining, or oil.

• Deforestation in Amazon Basin9 • Oil exploration in Siberia10 • A high-intensity wildfire caused a fire-

Low variability, low severity

“The failure of ‘safe’ prescribed burning9 ”. Strict prescriptions to ensure safe burns for management purposes, often with fixed seasonality under moderate conditions, preclude beneficial effects of high-severity patches.

V

High variability, mixed severity

dependent Protea to emerge after 30+ years in South Africa’s fynbos11 • Abundance of several bird species increase in forests recently burned at high severity in Montana, USA12

Table 4.2: Five classes of potential fire regime shifts after humans alter local fuels and ignitions. References: 1 Keane et al. (2002), Kolden (2019), and Margolis & Malevich (2016); 2 Moreira et al. (2011); 3 Margolis (2014) and Fuhlendorf et al. (1996); 4 Nowacki & Abrams (2008); 5 Silva et al. (2001), van Auken (2000), Stevens et al. (2017), and Matula et al. (2014); 6 Brown & Sieg (1999), Umbanhowar (1996), Touchan et al. (1995), and Savage & Swetnam (1990); 7 Tedim et al. (2018); 8 D’Antonio & Vitousek (1992); 9 Morton et al. (2008); 10 Mollicone et al. (2006); 11 van Wilgen (2013); 12 Hutto & Patterson (2016)

by whether or not an ignition occurs and fire spreads under the authority of some collective body—sanctioned vs. unsanctioned.53 We use the term “authority” loosely, such that it includes strict government regulations, traditional indigenous practices, and even informal cultures of land management. Inherent in this loose definition is the understanding that the values and standards of various levels of authority are often at odds. We’ve grouped potential anthropogenic fire regime shifts into five broad categories, which are presented in Table 4.2. Below we discuss some of the major human-caused mechanisms that drive these shifts.

53

In this coarse framework unsanctioned fire is synonymous with “wildfire” as colloquially used, naturally-ignited or otherwise. Sanctioned fire includes prescribed fire and other management burns set intentionally by, for example, pastoralists.

Exclusion: suppression vs. preclusion Fire exclusion results from suppressed unsanctioned ignitions and the preclusion of fire spread. Which mechanism excludes fire derives from landscape flammability—whether or not fuels are available for combustion.

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56  Ecology of Fire-Dependent Ecosystems

Suppression implies fuels will carry fire if ignited; thus, fire exclusion by suppression typically produces Regime Shift I (Table 4.2)—fuels build up until an unsanctioned ignition occurs and suppression is delayed. Meanwhile, fire spreads at high intensity or in different vegetation layers (e.g., shift from surface to crown fire) due to accumulated fuel. Fire preclusion depends on a reduced probability of fire spread (Regime Shift II, Table 4.2). In regions where anthropogenic ignitions drove the preEuropean fire regime, modifications can include demographic shifts, policies restricting the authority to burn, or practices that discourage fire use. Examples include cessation of indigenous burning and shifting management focus to livestock production (Brown & Sieg 1999, Haugo et al. 2019). Jurisdiction State ●

40

Federal

Northern California

Fire frequency

30

Ignitions

20 10 0 40

●●● ●● ●●●●●●● ●●●●● ●● ●●● ●●●● ●●● ●●●●●●

Southern California

30 20 10 0

●● ● ● ●●● ●●● ● ●● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ●● ●●●● ● ● ● ●●

1975

1995

Year

2015

Data: Keeley & Syphard (2018)

Figure 4.2: Power lines cause more ignitions on state-managed land in California, USA, than on land managed by the US Forest Service. A trend towards more starts in the south is shifting north. Frequency as fires/year/ million ha.

54

Other modifications include land-use or plant community changes that alter fuels and reduce the capacity for sanctioned fire. Spatially patchy herbivory can interrupt fuel connectivity (Kerby et al. 2007). Shifts in plant composition that increase fuel moisture or reduce fine fuel load also impede fire spread (Stevens & Beckage 2009, McGranahan et al. 2018a).

The contact area between wildlands and human development is called the wildland-urban interface, or WUI, which we discuss in Ch. 12. Chas-Amil et al. (2013) found that in a fire-prone area of Spain, twice as many ignitions occur in the WUI than other urban areas.

Humans alter the Fire Regime Triangle by introducing novel ignitions, both sanctioned and unsanctioned. Of course, these ignitions must occur simultaneously with favourable conditions in the other sides of the triangle—climate and vegetation. When they do, the results of unsanctioned ignitions can be disastrous. For example, recent autumns in California, USA, have brought massive wildfires driven by offshore winds—seasonal air currents that move downslope from mountains to the Pacific Ocean. But one such air pattern, the Santa Ana winds, rarely overlaps with lightning (Bendix & Hartnett 2018), suggesting that most, if not all, of these fires start from unsanctioned human ignitions. The severity and extent of these fires—limited only by the built environment, suppression effort, and ultimately the ocean—suggest Regime Shift III (Table 4.2). In California, unsanctioned ignitions go beyond arsonists and careless individuals—power lines are frequently to blame (Fig. 4.2). Indeed, while ignitions caused by sparks from steam engines was a major concern prior to diesel locomotives (e.g., Wallace 1923), today’s linear infrastructure of concern worldwide consists of roads and electrical lines (Mitchell 2013, Miller et al. 2017, Ricotta et al. 2018). Many patterns in human behaviour have increased unsanctioned ignitions. In some regions of Europe, ignition frequency has been linked to unemployment rates and transportation networks (Ganteaume et al. 2013). These issues are exacerbated as human populations expand into wildlands. Housing and development clusters apart from dense urban areas surrounded by natural vegetation or agriculture are known as peri-urban areas, and have high densities of ignitions (Chas-Amil et al. 2013).54 Other trends in anthropogenic ignitions reflect cultural and technological changes. For example, in the US, fewer ignitions have been attributed to cigarettes. Butry et al. (2014) found that advances aimed at preventing

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smouldering cigarettes from causing residential fires reduced wildland fire starts by 23% between 1980 and 2011. Nearly half of the decline in fires being caused by cigarettes, though, can be attributed to better fire investigation techniques that more accurately determine the actual sources of wildland fire ignitions. Interestingly, lower adult smoking rates in the US played only a minor role in the decline of cigarette-caused wildfire (9%). In many areas there is less nuance behind unsanctioned fires—large areas are deliberately burned by humans to clear forest for farming or extracting natural resources. For example, landscape fragmentation and agricultural intensification accelerate fire and deforestation in regions of the Amazon with little to no natural fire regime (Alencar et al. 2015, Morton et al. 2008), typifying Regime Shift IV in Table 4.2.55 Boreal regions also burn more extensively and frequently as humans encroach on unsettled areas (e.g., petroleum development in Siberia, Mollicone et al. 2006).

Management fires Societies worldwide conduct sanctioned burning to advance land management goals, from pastoralists and ranchers burning to maintain rangeland to government agencies burning to reduce hazardous fuel loads or conserve biodiversity. Management fires fit into a broad category of sanctioned burns characterised as wildland fire use. Most Western countries and Westernised post-colonial governments exert authority over burning by administering sanctioned fire within a policy framework. Such fire is often referred to as prescribed fire because fires are allowed to burn under authorised conditions—i.e., a prescription. Prescribed fires include both those intentionally set and natural ignitions closely monitored but not suppressed, often in wilderness areas. Two signatures of prescribed burns on fire regime include moderated severity and reduced variability, classifying many prescribed fire regimes as Regime Shift V (Table 4.2). A primary difference between wildfire and most prescribed burns is that prescriptions typically define a range of operating conditions—e.g., minimum relative humidity, maximum wind speed, which help keep fires containable.56 But ecologically, fire regimes that include only low–moderate intensity can deviate from natural fire regimes. Some fire-adapted species benefit from greater fire severity than is effected by most prescribed burns. Certainly, prescribed fire is valuable to the management of fire-dependent ecosystems, and is an obvious solution to the global fire deficit behind Regime Shifts I and II in Table 4.2. In general, well-designed prescribed fire programs can reduce hazardous fuel loads (Fernandes & Botelho 2003, Alcasena et al. 2018, McCaw 2013).57 But the narrow range of fire effects created by limited fire intensity precludes the life histories of many species adapted to high-severity fire. As Hutto (2008) put it, some like it hot; see examples given for Regime Shift V in Table 4.2. Mediterranean ecosystems are particularly sensitive to “the failure of ‘safe’ prescribed burning (van Wilgen 2013, p.e40)”. Mediterranean regions

55

Amazonian fires challenge the sanctioned vs. unsanctioned duality, which considers deforestation fires unsanctioned because they are once-off burns to shift natural land cover to agriculture (van der Werf et al. 2010). Fire introduced to these ecosystems is used as a land-clearing tool, not a management process. But the economic motivations behind forest conversion increase local acceptance, i.e., sanctioning, of burning, and different deforestation rates among recent Brasilian presidential administrations suggests at least passive authorisation at the federal level (Borunda 2019).

56

There are exceptions to the moderate condition mode. Notably, most examples pertain to maintaining balance between grass cover and woody plants in rangelands, which is difficult under moderate fire intensities (Twidwell et al. 2013a). Pastoralists in Ethiopia burn under very high fire weather indices, which ensures fire behaviour sufficient to control shrub encroachment (Johansson et al. 2012). In Texas, USA, “extreme prescribed fire” offers economical control of woody plants (van Liew 2012). 57

Whether fuel reduction with prescribed fire goes on to reduce wildfire area varies considerably among biomes, and is likely both effective and efficient in vegetation types with high fire activity (Price et al. 2015).

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58  Ecology of Fire-Dependent Ecosystems

58

In a piece with the straightforward title Don’t fight fire with fire, Whelan (2002) describes the irony of plants in Australia that require fire to initiate their life histories, but are susceptible to burning for their first 10 years.

59

For examples, see work by Kepe (2005), Eriksen (2007), Johansson et al. (2012), Walters (2012), and Ando et al. (2014).

worldwide have recently experienced dramatic wildfire events (Pausas & ´ ˜ 2012, San-Miguel-Ayanz et al. 2013), and low-severity Fernandez-Mu noz prescribed fires can reduce hazardous fuel accumulation (Fernandes et al. 2013). But evidence suggests biodiversity suffers when the fire regime shifts from infrequent, high-intensity fire to frequent, low-intensity burns (Bradshaw et al. 2018).58 Furthermore, it is unclear whether hazard reduction burn programs successfully reduce wildfire in Mediterranean vegetation. For example, fire spread in California chaparral is likely facilitated by drought and not dependent on fuel age (Keeley & Zedler 2009). Many management burns do not fit the “prescribed fire” construct. Fire is part of agricultural systems worldwide (Magi et al. 2012); even in the continental US, burning residual crop biomass makes up 43% of the total area burned in wildfires (McCarty et al. 2009). Fires set by “rednecks” benefit ecosystems in the Southeastern US (Putz 2003). Across Africa, locallysanctioned fires target forage resources, soil fertility, or pests.59 Seasonality is another way management fires alter fire regimes. Managers often burn outside of peak fire seasons, to enable containment and reduce fuel loads before weather increases wildfire risk (Bradshaw et al. 2018). Conventional, dormant-season burns in rangelands have been challenged by calls for growing-season fire to diversify productivity responses and ecological communities (Engle & Bidwell 2001, Vermeire et al. 2014, Towne & Craine 2016). Agricultural burning often occurs outside the non-agricultural fire season (Magi et al. 2012). Many of the African fire regimes cited above occur outside of “natural” fire seasons or those dictated by government.59

Climate change Evidence worldwide suggests global climate changes are causing longterm alterations of fire regimes, likely at accelerating rates. For example, since the 1970s, a climate-driven fire regime has replaced a fuels-limited regime in the Western Mediterranean Basin, increasing burned area 10´ ˜ 2012). Substantially greater wildfire acfold (Pausas & Fernandez-Mu noz tivity in the western US since the 1980s has been attributed to warmer air temperatures and earlier spring snowmelt (Westerling 2016). More extreme fire weather conditions are expected to make fire suppression more difficult in the boreal region (de Groot et al. 2013). The direction of alteration depends on how changes in temperature and precipitation affect fuels and fire behaviour (Table 4.1). For example, fire weather in Australia is predicted to shift towards more high fire danger days and fewer days that meet prescribed fire conditions (Clarke & Evans 2019, Clarke et al. 2019), likely increasing fire extent and severity (i.e., Regime Shift III, Table 4.2). Conversely, some regional climate changes produce cooler, wetter weather during conventional burn seasons (Yurkonis et al. 2019), likely driving fire preclusion (i.e., Regime Shift II, Table 4.2).

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FIRE, MANAGEMENT, AND CHANGE Modern use of the term fire regime developed along with the ecological approach to forestry and rangeland management. In the US, agency focus generally moved from resource extraction towards ecosystem management in the late 20th century (Christensen et al. 1996). Managers targeted reference conditions understood to represent community composition and ecosystem function prior to European settlement (e.g., Laughlin et al. 2004). Shifting from strict climate-determined models of plant succession (e.g., Clements 1916) towards dynamic models of disturbance in the 1960s not only invited an appreciation of the inherent role of wildland fire in natural ecosystems, but facilitated a distinction between natural wildland fire and burning used for deforestation, or arson (Krebs et al. 2010). Fire regime emerged as a means to describe variability in patterns of wildland fire among ecosystems. But there is variability within ecosystems, too. It is necessary to determine if alterations to fire regime, whether caused by direct human impact or global environmental change, are substantial enough to merit altering management approaches, as well. To do so, observed alterations to extant fire regimes must be understood within the context of patterns in pre-historic fire regimes.

Range of variability Managers must understand fire regime alterations within a range of variability. Most fire regime parameters are presented as numerical averages, but the amount of variability around these values is an inherent ecosystem property itself (Landres et al. 1999). Furthermore, even quantifiable parameters are subjective. Terms like frequent and severe are relative to a range of potential values that vary widely among ecosystems. One must discern differences in the core parameters of a fire regime that cause relatively minor changes within one vegetation state from differences that cause major shifts between vegetation states. For example, whether grassland burns annually or every few years can substantially alter the relative abundance of grass species (Fig. 4.3); such shifts are often reversible with low-severity fire. But when fire is infrequent or excluded altogether, trees encroach (Ratajczak et al. 2016); reversing such a shift requires highseverity fire (Twidwell et al. 2013a).

Historical Range of Variability (HRV) The historical range of variability describes how ecosystem properties varied over space and time in the past. The concept assumes extant ecological communities have necessarily adapted to the natural range of conditions the species have been subject to (Swanson et al. 1994). This approach expands the reference condition concept from a set of static ecosystem properties to one bounded by a natural range of variation, towards which ecosystem managers can develop management plans that ensure “future patterns are based on historical patterns to the degree feasible (Cissel et al. 1994, p. 30)”.

Figure 4.3: Annual or intermediate fire return intervals (FRI) in tallgrass prairie of the USA are within the historic range of variability (below the broken line), and maintain the native grassland state. But FRI beyond the historic range of variability leads to invasive species encroachment. This figure applies relative grass species abundance data from Kirkman et al. (2014) to conceptual model from Keane et al. (2009).

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60  Ecology of Fire-Dependent Ecosystems

Effectively estimating an ecosystem’s HRV requires a lot of data and must overcome several challenges. Long-term data on both fire regime and vegetation are necessary to reconstruct pre-settlement patterns; such information is often obtained through analysis of fire scars and sediments as described above (Keane et al. 2009). On one hand, the record must be long enough to account for substantial periods of climate variability—the longer the timeframe in the dataset, the better the model can account for pre-historic climate patterns (Millar & Woolfenden 1999). On the other hand, uncertainty in this record might have less impact given that present community composition reflects adaptation to filters created by previous conditions (Keane et al. 2009).

60

We are not aware of comparable systems outside of the US, although large-scale descriptions of variability in fire regime have been made elsewhere (e.g., Australia; Bradstock 2010, Williamson et al. 2016b).

LANDFIRE Ecosystem managers in the United States have a system to operationalise the historical range of variability at landscape levels.60 The Landscape Fire and Resource Management Planning Tools Project, or LANDFIRE, provides standardised data on vegetation, wildland fuels, and fire regimes through an open-source format accessible by many Geographic Information Systems (Rollins 2009). Vegetation layers can even be updated after a wildfire or management action to ensure models have realistic, up-to-date parameters (Ryan & Opperman 2013).

Future fire regimes Predicting future fire regimes is not rocket science; it is far more complicated than that. —Keeley & Syphard (2016) Clearly, global changes, including climate and land use, alter fire regimes. The interactions between factors of global change, and how they will affect ecosystem properties, are less clear. Here we describe how current models of fire regime integrate changes to the historic range of variability, and how new concepts can inform wildland fire science and management.

Departures from HRV Departure from the historical range of variability occurs when parameters such as fire intensity or frequency deviate substantially from the known bounds of the variability. For example, Fornwalt et al. (2016) determined the 2002 Hayman Fire in Colorado, USA, “burned with uncharacteristic severity” having caused high mortality to mature trees in areas that had burned with much lower severity before. Sources of departure include environmental change and invasive species. LANDFIRE simulations with predicted future climate scenarios deviate substantially from simulations under historical conditions (Keane et al. 2008). Invasive species can introduce qualitatively different fuel characteristics that alter fire regimes beyond the historical range of variability (Mack & D’Antonio 1998, Brooks et al. 2004). Fire Regime Condition Class (FRCC) is a LANDFIRE tool designed to describe departures from the historical range of variability (Barrett et al.

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2010).61 The FRCC tool compares current vegetation conditions to the reference conditions stored in the LANDFIRE geographic information system and calculates departure values for a mapped area (Rollins 2009). There are three categories of departure: FRCC I has departure values 0–33, indicating intact vegetation patterns and disturbance regimes relative to the reference condition; FRCC II has departure values 34–66, indicating moderate departure with declining ecological integrity; and FRCC III has departure values 64–100, indicating substantial departure from the historical range of variability and poor ecological integrity (Barrett et al. 2010).

61

Conceptually, fire regime condition classification is useful for describing departures from historical range of variability even for those who do not or cannot make use of LANDFIRE tools. The fire regime of any ecosystem can be critically assessed in terms of how patterns of fuel, fire behaviour, and fire effects differ from what is expected.

The novel ecosystem concept Some ecosystems are farther beyond the historic range of variability than LANDFIRE can handle. In some cases, managers might look to neighbouring regions with conditions similar to those forecast under models of global change (Fule´ 2008). But when vegetation shifts substantially alter fuels, a new framework might be required. For example, invasion of the annual cheatgrass Bromus tectorum in the Great Basin, USA, has increased fine fuel load well above that typical of semi-arid steppe. In some cases, fuel type-specific spatial data can improve FRCC performance (Menakis et al. 2003). But in other cases, similarity between local data and FRCC output can be as low as three percent (Provencher et al. 2009). Novel ecosystems are those “composed of new combinations of species under new abiotic conditions (Seastedt et al. 2008)”, which encapsulates both the invasive species and climate change aspects of altered fire regimes.62 To be clear, novel ecosystems are not simply altered ecosystems—in many cases, ecosystems comprised of exotic-dominated communities function ecologically, but are structured differently than the native communities they displace (e.g., Wilsey et al. 2009). Management of novel ecosystems must prioritise ecological functions, patterns, and processes over specific composition (Fuhlendorf et al. 2012, Stanturf et al. 2014).

62

This concept got a late start: Papers on “novel ecosystems” surged ca. 2005, after “biodiversity” and “ecosystem services” spiked ca. 1987 and ca. 1991, respectively (Evers et al. 2018).

Novel ecosystems are also the result of human activity (Hobbs et al. 2006). As such, management should explicitly incorporate social models as well as ecological models in coupled human-natural systems frameworks (Spies et al. 2014). Relevant social issues relate to the increasing cost of trying to implement suppression and control policies (Donovan & Brown 2007). Humans will need to reconsider fire suppression and consider adapting, adjusting, or even abandoning residence patterns where humans and novel fire regimes come into conflict (Gill et al. 2013).

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II Fire Effects

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CHAPTER

5

Fundamentals of wildland fire impacts and ecology Scale, biological organisation, and ecological sub-disciplines; evolution of fire-adaptive traits

This chapter introduces the subject of scale in fire science, reviews levels of hierarchical organisation important to understanding ecological effects, and discusses fire-related adaptations. We draw on the distinction between direct and indirect fire effects (Ch. 3):

• Direct effects—also called first-order effects —are the immediate impacts on organisms that result from direct exposure to heat, flame, or the chemicals in smoke (Reinhardt et al. 2001b). They usually occur during or immediately following fire, although mortality from fire damage might not be immediately apparent (e.g., it can take several days for topkilled trees to drop needles after the flaming front passes). Direct effects manifest at the level of individuals.

• Indirect effects—also called second-order effects63 —are post-fire

63

biophysical changes to the burned area. They are not directly caused by the heat or smoke from a fire (Reinhardt et al. 2001b). Rather, they are typically consequences of the direct impacts of smoke or fire that occur days, weeks, or even years after fire (Pyke et al. 2010). Indirect effects occur at the population, community, or landscape level.

THE IMPORTANCE OF SCALE Ecology follows a hierarchy of increasing inclusiveness in which each level contributes to the level above (Odum & Barrett 1971; Table 5.1). In this framework, researchers often focus on a level of organisation and its attendant processes and functions. As such, ecological sub-disciplines have formed within each level, developing unique theories, methodologies, and implications. A robust understanding of the impact of any disturbance requires integration across levels of organisation and ecological sub-disciplines (Pickett et al. 1989).

Another level of indirect effect has been described as third-order effects—the effects of fire on the human experience (Ryan et al. 2012, Lake 2007). These comprise the human response to fire and the related risks and opportunities, tangible and intangible (Ryan et al. 2012). Tangible effects include fire management activities, post-fire restoration efforts, and fire-induced changes in recreation and subsistence activities. Intangible third-order effects are human spiritual and emotional responses to direct and indirect fire effects.

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66  Ecology of Fire-Dependent Ecosystems

Level Description Individual organisms (microbes, fungi, plants, animals) Populations—Groups of organisms of the same species in a given location capable of interbreeding (Silvertown & Charlesworth 2009, p. 1) Communities—Populations of different species with overlapping distributions and potential interactions (Vellend 2010) Ecosystems*—All biotic communities and abiotic landscape elements within a given region Biomes—Broad community types that cover a large region with common climate (e.g., tundra, boreal forest, desert) Biosphere—Terrestrial, marine, and aquatic life zones, organisms therein, and the geologic and atmospheric layers in which they interact (Vernadsky 1945) Table 5.1: Levels of ecological organisation. * Alternatively, Lidicker (1988) places the landscape level above communities in the ecological hierarchy.

Relevant ecological sub-fields Fire can affect all levels of organisation, but fire effects and mechanisms can differ among levels (Rykiel 1985). We briefly describe relevant subdisciplines of ecology below. The upcoming chapters will elaborate on fire effects and responses at the different levels of ecological organisation and discuss the contributions of each subdiscipline to the field of fire ecology.

Population ecology Population ecology investigates the processes that influence the size and distribution of plant and animal populations (Begon et al. 2009, pp. 3–5) and explores how populations interact with biotic and abiotic factors in the environment. While the study of plant and animal populations developed independently, the underlying processes are the same—immigration, emigration, birth, and death. As such, we combine our discussion of plant and animal population responses to fire in Chapter 8. Managers must often protect or eliminate a particular species (e.g., endangered species or invasive species, respectively), and fire—along with other disturbances—can affect population dynamics (Maret & Wilson 2000, Hoffmann 1999). Since fire can cause mortality and alter reproduction, it has direct and indirect effects on both birth and death rates (Mott & Andrew 1985). Therefore, it is often necessary to study the impact of fire on reproductive individuals, specifically, to determine how fire affects viability of endangered populations or control of invasive species.

Community ecology Community ecology is the study of a group of potentially interacting species in a particular location (Morin 2009, pp. 1–8). The spatial extent of a community is somewhat subjective and thus typically defined by the question of interest. Population ecology and community ecology overlap to some degree, as no population exists in a vacuum. For example, predatorprey interactions could be investigated from either a population or community perspective. The interaction affects the population dynamics of each species, but together, the two species comprise a community, with the study of their co-existence falling under the purview of community ecology. Community ecology was initially descriptive, focused on identifying and enumerating all the species in a particular locale (Morin 2009). Repeated observations of patterns in community composition led to theories of community assembly and the drivers of biological diversity, which have become the foci of community ecology (Keddy 1992). Community ecologists study fire effects because fire is one of the ecological processes responsible for succession, or changes in community composition and structure over time and space (Mackey & Currie 2000, Petraitis et al. 1989). Many indirect fire effects also derive from shifts in composition or structure.

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USFS

Landscape ecology The problem of pattern and scale is the central problem in ecology. Applied challenges. . . require the interfacing of phenomena that occur on very different scales of space, time, and ecological organisation. Furthermore, there is no single natural scale at which ecological phenomena should be studied (Levin 1992). Landscape ecology is the study of the spatial configuration of ecological communities and environmental gradients across a broad scale—landscape—and the effect of that configuration on the ecological processes, communities, and populations contained within a given landscape. While it emerged as a discipline in the late 1980s and early 1990s (Turner 1989, Forman 1990), landscape ecology began to be explored in the 1960s as a combination of ideas prominent in human geography, ecology, and landscape architecture (Naveh & Lieberman 1994). Landscape ecology rose in prominence as issues of scale gained recognition; it provided an opportunity to account for environmental variability in a spatially explicit manner across broad scales (Wiens et al. 1993, Risser 1987).

Figure 5.1: Three scales of impact in the 2013 Rim Fire, California, USA. (L) Individual organisms must first escape or tolerate the heat and smoke of combustion (direct effects) and then survive in the post-fire landscape, in which environmental conditions and resource availability can be drastically altered (indirect effects). (C) At the scale of populations and communities, spatial variability in direct effects modulates succession through persistence and recovery. (R) The spatial extent of fire spread and completeness of burn within the final perimeter determine the connectivity of communities at the landscape scale. Pyrodiversity refers to the pattern of variability in direct and indirect effects, which affects the processes that support biodiversity in fire-prone landscapes. Substantial smoke emissions into the atmosphere can also affect global climate systems.

Landscape ecology, with its focus on explicit spatial context (Allen & Hoekstra 2015), is particularly important to understanding fire ecology. A common focus of landscape ecology is how disturbances both create and respond to the pattern of biophysical components of ecosystems on the landscape (Turner et al. 1994). The legacy of patterns created by past disturbances—landscape memory—and its effect on ecosystem processes and structure is often a focus in landscape ecology studies (Peterson 2002). Scale—the spatial extent and timeframe over which ecological processes operate—gained recognition in the late 1970s and early 1980s as critical to understanding ecological processes and their effects on biological populations and communities (Schneider 2001). Spatial scale describes the spatial extent of an ecological process or environmental feature (Fig. 5.1); temporal scale describes the timeframe over which processes occur (Levin

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Figure 5.2: Illustrating the effects of different grain and extent. The left panel shows the effects of increasing the extent of a study—landscape features that were not included in the smaller extent are encompassed by the larger extent. The right panel shows the effect of increasing the sampling grain—landscape features that were distinguishable at the smaller grain are averaged over a sample at the larger grain, leading to a loss of fine-scale detail.

I nc r eas i ngex t ent

I nc r eas i nggr ai n

Wiens & Milne (1989)

1992, Wiens & Milne 1989, Wiens et al. 1993). In addition to the spatial extent and temporal breadth of the investigation, scale also involves grain—the spatial extent of a sampling unit (Nally & Quinn 1998). Scale is an important consideration in designing ecological studies because a study cannot provide inference about a process operating beyond the extent of the study or at scales below the grain (Wiens & Milne 1989). Selecting the appropriate scale of inquiry—both grain and extent—requires understanding the scale at which the relevant ecological processes or organisms of interest operate (Wiens et al. 1993, Delcourt et al. 1982). For instance, studying highly mobile or large animals would likely require a larger extent than sessile plant populations. Fire creates environmental variability at multiple scales, and plants and animals interact with that variability on different scales depending on their body sizes and life histories. Results from a study conducted at an inappropriate scale might represent artefacts of scale rather than actual ecological dynamics (Levin 1992). Lest one conclude that a study should be designed at the largest extent possible, there are trade-offs to increasing the extent of inquiry. Logistically, increasing the extent usually requires increasing the grain, which can increase the likelihood of detecting broad-scale processes at the expense of overlooking fine-scale details (Fig. 5.2). Both are important in ecological systems because broad-scale processes typically constrain dynamics at finer scales, but the processes by which fine-scale dynamics scale-up can impose constraints on broad-scale patterns as well (Milne 1988). One common way of incorporating scale into ecological studies is to measure a response at multiple nested scales and compare the results. While it is impossible to look across all scales, selecting several based on one’s current understanding of the important ecological processes is a common approach helpful in determining scale dependencies (Wiens & Milne 1989).

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Fire effects and scale Scale is important to understanding fire effects (Lertzman et al. 1998). There is variability in severity both within individual fire events and between fires through time. Thus, describing a fire regime for a given locale necessarily simplifies fire as an ecosystems process—variability within and among the fires in a regime is averaged across a single scale (Falk et al. 2007). Spatial and temporal variability in fire severity, extent, and time between fires drives differences in indirect fire effects as organisms, populations, and communities respond to variability across disturbance events (Peters et al. 2004, Romme et al. 1998).

Scale-independent responses Direct effects can be scale-independent. The effect of combustion and its products on an individual is often the same whether a fire is small or large and whether it has been a long or short time since the previous fire. But variability in individual effects is often related to fire intensity, which can correlate with fire size and fire return interval. Mortality of resprouting plants is often related to fire frequency ` (Zammit 1988, Vila-Cabrera et al. 2008), as resprouting capacity depends on belowground reserves that can be depleted by frequent fire (Canadell ´ & Lopez-Soria 1998). Direct effects of fire on animals are often related to spatial components of fire severity as large, high-severity fires can be more difficult to escape than small fires or large fires with scattered refugia (Meddens et al. 2018, Robinson et al. 2013). We discuss direct effects of fires on individuals in Chapter 8.

Scale-dependent responses Scale-dependent responses include the spatial uniformity of burn severity, connectivity of burned areas, and the time between recurrent fires (Morgan et al. 2001). They also involve interactions between fire effects (both direct and indirect) and other disturbances (e.g., drought or insect outbreaks; Bebi et al. 2003, Bigler et al. 2005, Turner et al. 2013). Scale-dependent responses are typically higherorder fire effects and their study requires the inclusion of a spatial component and explicit consideration of spatial and temporal variability in fire effects. We discuss scale-dependent responses of populations and communities, as well as the role of landscapes in mediating those responses in more detail in the upcoming chapters.

Biogeophysical impacts Fire affects the Earth’s physical environment along both temporal and spatial dimensions: short-term vs. long-term, and from local to global scales. The physical impacts of fire are characterised by substantial variability—across ecosystems, among environmental conditions, and among different fire regimes. Variability in physical responses is likely because soil, water, and air cannot evolve or adapt as can the biota. Because variability in physical impacts of fire plays out in complex Earth

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systems, the outcomes of fire effects are diverse and sometimes counterintuitive. It is thus difficult to make useful generalisations about the impact of wildland fire on the physical environment. Chapter 6 discusses potential impacts of fire to soil, from physical and chemical properties to nutrient pools and cycling. Chapter 7 deals with fire effects on water and aquatic environments, as well as interactions between fire and Earth’s atmosphere.

Ecological function and ecosystem services Several definitions relevant to ecosystem ecology are presented in Table 5.2. Early ecosystem research showed the importance of understanding the interconnectedness of components within a biotic system. Lindeman (1942) showed that biotic and abiotic elements of aquatic systems exchanged both energy and matter; that exchange is now referred to by ecologists as ecosystem function. Examples of ecosystem function include nutrient cycling and biomass production (D´ıaz & Cabido 2001). Two components of ecosystem functioning include resource dynamics and ecosystem stability. Resource dynamics are mediated by the components of an ecosystem over relatively short temporal scales and often fluctuate over time. Ecosystem stability is modulated by the organisms and abiotic features comprising the ecosystem over relatively long timeframes. Ecosystem services are ecosystem functions that provide benefits to humans (Millennium Ecosystem Assessment 2005). The four categories of services delivered by ecosystems (Table 5.3) allow valuation of benefits obtained from natural systems and examination of trade-offs among the categories under different management scenarios (Wallace 2007). Research on plant functional traits can explore the impact of global change on ecosystem service delivery. Ecologists often group species into classes with similar traits to better understand ecosystem dynamics (Grime 1974, Table 5.2: Definitions of several terms relevant to ecosystem ecology.

Term

Definition

Ecosystem

a spatially explicit unit of the earth that includes all of the organisms, along with all components of the abiotic environment, within its boundaries (Lovett et al. 2005, p. 4).

Ecosystem function

flow of energy and materials through the arrangement of biotic and abiotic components of an ecosystem (D´ıaz & Cabido 2001).

Ecosystem stability

capacity of an ecosystem to persist in the same state or return to the same state following perturbation (D´ıaz & Cabido 2001).

Resource dynamics

magnitude and rate of inputs and outputs and the internal cycling of key resources such as water, carbon, and mineral nutrients (D´ıaz & Cabido 2001).

Functional traits

morphological, biochemical, physiological, structural, phenological, or behavioural characteristics that are expressed in phenotypes of individual organisms and are considered relevant to the response of such organisms to the environment and/or their effects on ecosystem properties (D´ıaz et al. 2013).

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Smith & Huston 1990). The functional composition of plant communities affect ecosystem processes and ecosystem service delivery (D´ıaz et al. 2007, 2006, Hooper et al. 2005). Thus, understanding impacts on plant functional traits helps predict the effects of environmental change on ecosystem service delivery (D´ıaz & Cabido 2001). The fire-adaptive traits discussed below are functional traits relevant to the plant’s response to fire, and therefore help determine the potential for altered ecosystem service delivery following fire or an alteration in fire regime.

CULTURAL RESOURCES Cultural resources are objects or places significant to understanding past human activity, ideas, and beliefs (Ryan et al. 2012). Cultural resources provide information on past cultures—prehistoric and historic—and are therefore valuable in interpreting current societies and environments through an historical lens. Cultural resources include rock art, prehistoric tools and wares, middens, prehistoric human remains, as well as historic buildings and battle sites. Table 5.4 lists direct effects on cultural resources.

Category—Examples Provisioning—Food, forage, timber, firewood, metals & minerals Regulating—Crop pollination, flood control Supporting —Biodiversity maintenance, wildlife habitat Cultural—Spiritual & aesthetic value Table 5.3: Four categories of services delivered by ecosystems as delineated by the Millennium Ecosystem Assessment (2005).

Direct effects Because many cultural resources are as yet undiscovered, or remain in place, they are susceptible to fire. For example, a fire damaged the Cedar Mesa Sandstone cliffs, a culturally significant place and natural feature in Canyonlands National Park, USA. The fire blackened the cliff surface and caused the outer surface of the sandstone cliffs to peel (Noxon & Marcus 1983). In Yellowstone National Park, USA, a post-fire archaeological investigation found an obsidian artefact altered by heating (Fig. 5.3). Similarly, a post-fire examination of cultural resources took place following the 1977 La Mesa Fire in Bandelier National Monument, USA. Archaeologists discovered 91 sites containing cultural resources, 54 of which were burned. Fire effects included colour change, breakage, crumbling, and attached residue on artefacts (Traylor et al. 1990).

A. Steffen, US NPS

Figure 5.3: Example of colour change on an obsidian artefact after fire in Yellowstone National Park, USA.

Indirect effects Fire can have indirect effects on cultural resources as well. These are most often associated with management operations and post-fire restoration activities (Ryan et al. 2012). In the archaeological investigation of the 1977 La Mesa Fire, archaeologists found 44 sites containing cultural resources that had been impacted by fire suppression tactics (Traylor et al. 1990). Artefacts can be inadvertently destroyed by heavy machinery or line construction, restoration tactics can lead to erosion of natural features of cultural significance, and cultural artefacts become easier to find when surrounding vegetation is consumed, increasing their vulnerability to vandalism or theft.

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Fire effect

Description

Affected cultural resources

Combustion

complete or partial consumption from burning

wooden structures; landscapes

Combustive residue

tar deposits formed from the by-products of combustion

prehistoric tools and vessels; human remains; ceramics

Colour change

darkening of materials as a result of high temperatures changing the character of the mineral substrate or from burnt sediment

stone artefacts, sandstone structures, exposed mineral landscape features

Paint oxidation

loss of, or change in pigment from heat exposure

ceramics

Crazing

fine surface cracking

pottery; ceramics; building materials

Spalling

exfoliation of the surface of a rock or structure from heat- or steam-induced pressure changes

sandstone structures and landscape features

Spall scars

depressions resulting from spalling

sandstone structures and landscape features

Potlid fractures

depression on surface of stone artefact created in the same manner as spalling, but with stone, the exfoliated piece remains whole and resembles a lid

lithic (i.e., stone) artefacts only

Fracturing

breaking into pieces or the presence of fissures

tools; pottery; mineral structures; exposed mineral landscape features

Table 5.4: Summaries of the most common fire effects on cultural resources adapted from Ryan et al. (2012).

Indirect effects can also follow changes in the site after fire. Cultural resources can be destroyed by post-fire tree fall, erosion, or landslides. Furthermore, heat-exposed building materials and landscape features are at a greater risk of weathering and can crack or fracture long after the fire damage occurs (Jones & Euler 1987, Ryan et al. 2012).

FIRE-ADAPTIVE TRAITS Many organisms in fire-dependent ecosystems have traits that allow them to survive and even thrive with fire. Most fire-adaptive traits allow organisms to cope with the immediate impacts of combustion and smoke on organisms, or direct effects. Regardless of whether these traits have been directly selected for by fire or were shaped by other selective forces, fireadaptive traits confer a fitness advantage in fire-prone environments under a specific fire regime. Fire regime alteration can reduce a species’ adaptive advantage despite its fire-adaptive traits. Organisms in fire-dependent ecosystems have traits that are adaptive in the face of both direct effects of combustion and indirect effects of fire on the individual’s habitat. Direct effects tend to be scale invariant, but indirect effects often depend on the scale of the fire. Robust fire science should explicitly consider scale to fully explore both indirect and direct fire effects.

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Evolution and natural selection Evolution is a change in the frequency of characteristics that can be passed down from one generation to the next within a population of organisms (Darwin 1859). Even within a single population, there is a great amount of variation among individuals. This variation arises largely from gene mutations and genetic recombination and is expressed as differences among individuals in morphological, physiological, and behavioural traits (Stearns & Hoekstra 2000). If these trait differences lead to increases in the rates of survival and reproduction (increased fitness) and they can be passed down from one generation to the next, their frequency in the population will increase over time (Lewontin 1970). Natural selection is considered the primary evolutionary process driving shifts in the frequency of traits in a population (Schluter 2000).64 Natural selection changes the frequency of genes in a population and produces adaptations—features that increase the reproductive success and survival, or fitness, of an individual in a particular environment (Lewontin 1974). The adaptation, or adaptive trait, becomes more prevalent over time as individuals with adaptive traits produce more offspring than those without. Natural selection acts on populations via biotic interactions such as competition, predation, and parasitism (Relyea 2005). Environmental stressors (e.g., temperature, acidity, salinity, or intense disturbance) can also drive natural selection (Bijlsma & Loeschcke 2005), although not all fluctuations in abiotic conditions cause sufficient stress to induce natural selection. Rather, natural selection results only when the intensity of the stress lowers survival or reproduction (Sibly & Calow 1989). Given sufficient time and variability in traits, organisms overcome environmental stress through phenotypic plasticity 65 or adaptation (Srensen et al. 2005). Abiotic and biotic factors can act in concert to drive adaptation. For instance, changes in biotic conditions can increase predation, or water stress can increase susceptibility to mortality from parasitism (Bijlsma & Loeschcke 2005). A classic example of adaptation to changing environmental conditions is the increased relative frequency of a dark (melanistic) version of the peppered moth Biston betularia during the industrial revolution (Fig. 5.4). The first sighting of the melanistic form was concurrent with industrialisation in England in 1848; by 1950, most of England had frequencies above 90%. Melanistic individuals had greater fitness as trees became blackened by soot from coal combustion—it was more difficult for predators to detect

64

Other evolutionary forces include genetic drift, and sexual selection—sometimes considered a component of natural selection (although Darwin (1896) considered the two as distinct evolutionary forces).

65

Pigliucci et al. (2006) define phenotypic plasticity as the ability of individual genotypes to produce different phenotypes when exposed to different environmental conditions, which means an organism can express different traits under different environmental conditions.

Figure 5.4: Two forms of the peppered moth Biston betularia. (A) The melanistic form, which became more frequent as trees darkened from soot and lichens died (making it more difficult to detect). (B) The light form, which is more frequent in unpolluted forests. Olaf Leillinger CC BY-SA 2.5

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when resting against the darkened trees (Kettlewell 1973). Frequency of the melanistic form decreased as coal use declined and effective pollution control legislation was implemented (Fig. 5.5).

General considerations

Data: Cook (2003)

Figure 5.5: Change in the frequency of the melanistic form of the peppered moth Biston betularia after the industrial revolution in several areas of England.

A suite of organismal traits are associated with fire-dependent ecosystems (Lamont & He 2017). Such traits allow organisms to survive fire and even provide fitness advantages under a particular fire regime (Keeley et al. 2011, Paula et al. 2009). Many traits are associated with a particular strategy to survive fire (Table 5.5). Strategies reflect the tolerance of a species to fire, often a particular fire type or other component of the characteristic fire regime within its range. Two temporal scales relevant to fire as a selective force include the direct effects of heat and smoke exposure during combustion, and the indirect effects of altered resources, habitat, and environmental conditions once the flame front passes. During a fire, the primary purpose of fire-adaptive traits is to avoid cellular damage due to exposure to the products of combustion. Injury and death are a function of the dose—the magnitude and duration of exposure to heat or smoke—and cell state—whether cells are active, dormant, or dead. Some organisms can tolerate brief exposures to intense heat or smoke, while others succumb to sustained exposure to even moderate intensities. In plants, dormant cells generally have lower moisture content than active cells, and some species have evolved to retain dead tissue as insulation. In animals, a whole organism can be dormant, as in torpor, or as with plants, dead cells can provide insulation, such as hair, scales, or spines.

Fire-intolerant species Some organisms are killed by even moderate-intensity fires. These species are typically rare in fire-prone ecosystems or depend on refugia—areas within a fire perimeter that remain unburned due to conditions unconducive to fire spread. Where such species do occur in fire-prone ecosystems, they often have reproductive traits that allow for their replacement after fire.

CSIRO CC BY 3.0

Figure 5.6: Banksia spp. have hard, woody fruits that only open after fire (He et al. 2011).

Post-fire obligate seeding is common among fire-intolerant plant species in fire-prone ecosystems. Heavy seed release follows immediately after fire (Keeley et al. 2011), when conditions are suitable for germination. Many species produce serotinous cones—cones with an external cuticle that opens at an environmental cue typically associated with favourable conditions for germination and establishment (Nathan et al. 1999). Serotiny is a general term for environmentally-cued seed release; fire-specific serotiny is called pyriscence (Lamont et al. 1991). Pyriscence is common in the Australian genus Banksia and occurs in some conifers as well (Fig. 5.6). Conifers of high-latitude forests in North America have thin bark and retain low branches, which increase exposure to infrequent but severe crown

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Strategy

Tactics

Examples

Endure—Organisms remain in place and rely on adaptations to tolerate heat and smoke. • Thick bark insulates tree boles at the base (low-intensity Insulation—Organisms retain dead regimes) or the full length (crown-fire regimes)1 or dormant exterior tissue to protect • Scabrous tissue, dead leaves, cones, and woody fruits proinner tissues. tect plant reproductive tissues2 Torpor—Reduce metabolic demand during and immediately after fire.

• Echidnas Tachyglossus aculeatus use torpor to survive within burn perimeters3

Avoid—Organisms remain in place, having adapted to keep critical tissues away from exposure. • Belowground growth points (bud banks) in plants4 Hide—Soil insulates tissues from • Burrowing animals provide refuges for themselves and other exposure. species5 Stretch—Keep sensitive tissues out of reach of heat exposure. Wait—Delay life history events until risk of damage passes or post-fire conditions are better.

• Many pines shed branches, have heat-dissipating canopies preventing surface fires from harming buds6

• Heat from fire and chemicals in smoke prompt seed release and germination, respectively7

Move—Organisms themselves move to reduce exposure, or rely on propagules to re-establish. Climb—Move to vegetation layers • Some arboreal mammals sense smoke, climb away from with less heat exposure. surface fires8

• Refugia, areas that do not burn or burn at low intensity, enScoot—Move within fire perimeter towards areas of low fuels.

hance survival9 • Even tortoises can survive by scooting to areas with low fuels10

Flee—Move beyond the fire perimeter.

• Chimpanzees identify areas of lower fuel and await lower

Drift—Propagules quickly come into area post-burn.

• Fireweed Epilobium angustifolium relies on long-distance

intensity to cross oncoming flame fronts11 and high-volume seed dispersal12

Table 5.5: Three general strategies for the survival of plants and animals exposed to heat and smoke from combustion in the wildland fire environment, and specific tactics and examples. References: 1 Pausas (2015); 2 Bond (1983), He et al. (2011), and Huss et al. (2019); 3 Nowack et al. (2016b); 4 Pausas et al. (2018); 5 Dawson et al. (2019); 6 Fernandes et al. (2008); 7 Lamont et al. (1991) and C ¸ atav et al. (2018); 8 Nowack et al. (2016a); 9 Robinson et al. (2013); 10 Wright (1988); 11 Pruetz & LaDuke (2010); 12 Archibold (1980) and Solbreck & Andersson (1987).

fires. But the trees retain serotinous cones in the canopy. Fire releases copious amounts of seeds and thus provides a mechanism for retaining postfire dominance despite high mortality (Schwilk & Ackerly 2001). Even some species without an impermeable outer cuticle germinate following fire. In these species, germination is not triggered by heat, but rather by smoke (Pierce et al. 1995, Keeley & Zedler 1998). Smoke-induced germination is widespread in fire-prone ecosystems, but can also be found in non-flammable ecosystems (Bradshaw et al. 2011).

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While most animals are fire intolerant—heat exposure and smoke inhalation induce severe injury and mortality—many species rely on behavioural traits to avoid direct contact with flame and smoke. The most ubiquitous is avoidance, in which animals either flee or hide in burrows; soil is a good insulator, but burrows must be well ventilated to prevent suffocation from smoke (Bendell 1974). As the primary behavioural fire-adaptive trait, avoidance success depends largely on individual mobility and fire behaviour (e.g., spatial extent and spread rate; Wright & Bailey 1982, Griffiths & Brook 2014). Unburned or low-severity areas also provide refugia. Small animals typically hide, while larger animals and birds flee to unburned or previously burned areas (examples in Wright & Bailey 1982, pp. 39–80). A notable fire-adaptive animal trait includes site selection of bowers—structures built by male Bowerbirds for courtship. Great Bowerbirds Chlamydera nuchalis expend considerable energy to build elaborate bowers out of highly-flammable dead twigs. A burned bower means males might not mate, and must exert additional effort to rebuild. Mikami et al. (2010) followed 23 active bowers in an area where fires spread across a substantial portion. Nine of the bowers were within a burn perimeter. Six of those 9 bowers survived the fires, and a comparison of unburned areas around the bowers to adjacent burned areas determined escape from damage was not likely due to chance. The bowers were often built under full canopy, where grass fuel was scarce, and the Bowerbirds cleared flammable litter away from the bower or covered it with rocks or decorations (Fig. 5.7), which decreased the likelihood of flames reaching the bower.

Graeme Churchard CC BY 2.0

Figure 5.7: Bowerbirds remove litter immediately around their nests and build in areas that are less likely to burn.

Not all animals flee fire—some are attracted to flames or smoke, and many are attracted to recently burned areas (Komarek 1969, Bird et al. 2005, Shaffer & Laudenslayer 2006; Fig. 5.8). Burning and recently burned areas can provide unique resources; taking advantage of these resources can be considered a fire-adaptive trait if it increases fitness of the animal. For instance, Australian “fire beetles” Acanthocnemus nigricans and Merimna atrata lay eggs in trees weakened by fire. The beetles have an infrared receptor in their abdomens that guides them to wood heated by combustion (Schmitz et al. 2002, Schmitz & Trenner 2003).

Fire-tolerant species Some organisms can survive fire damage. These organisms have traits that allow them to persist despite loss of tissue to combustion. Plants comprise most fire-tolerant species. They are able to protect sensitive tissue and remobilize resources to regrow following fire. Torre Hovick, with permission

Figure 5.8: A short-eared owl Asio flammeus explores a recently burned area of the Tallgrass Prairie Preserve, Oklahoma, USA.

Resprouting is the most common fire-adaptive trait in fire-tolerant plant species. Many woody plants can resprout from stems and roots (Pausas 2004). In fire-prone ecosystems, many species have underground or basal storage organs that provide resources necessary for resprouting after fire. These species can produce new stems and shoots even after complete consumption of above-ground tissue (Keeley 2012). Resprouting plants have an array of structures with meristematic tissues

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a

b

d

c

Figure 5.9: Several examples of meristematic tissue locations in resprouting plants: (a) basal resprouting of topkilled shrub; (b) epicormic resprouting of a burned Eucalpt; (c) lignotuber of kiepersol Cussonia paniculata; (d) rhizome in bracken fern Pteridium aquilinum.

a: Carissa Wonkka; b: CSIRO, CC BY 3.0; c: Genet at German Wikipedia, CC BY-SA 3.0; d: Emmy LYF, CC BY-SA 4.0

(Table 5.6). Roots and root crowns containing meristematic tissue are common, having arose early in the history of plants (Pausas & Parr 2018). Other resprouting species are epicormic —sprout from the stem. Many Eucalyptus species are epicormic resprouters, with meristematic tissue located deep in their thick bark and therefore protected from high-intensity fire despite having above-ground meristem (Waters et al. 2010, Burrows 2002, Burrows 2008). Epicormic resprouting in eucalypts likely evolved early as well, in the Cenozoic (Crisp et al. 2011). Another group of evolutionarily old but less widespread structures are nonwoody rhizomes and other belowground swellings on herbaceous plant roots (Pausas & Parr 2018). These are largely associated with monocots and ferns.66 Woody rhizomes, xylopodia, and lignotubers are also lesscommon meristematic structures found mostly in areas with a history of frequent fire (Paula et al. 2009, Paula et al. 2016; Fig. 5.9).

66

Most perennial grasses have both above- and below-ground meristematic tissue (Klimeˇsova´ & Klimeˇs 2007). But most new growth in grasslands is from below-ground meristematic tissues. Despite being able to reproduce from seed as well, Benson & Hartnett (2006) found that 99% of new perennial grass growth following fire in North American tallgrass prairie was regrowth from below-ground meristematic tissue located on rhizomes.

Fire-resistant species Some organisms incur little damage despite high heat exposure. A number of adaptive traits confer fire resistance to such species. These traits are generally either structural or developmental. For instance, North American conifers in ecosystems that experience surface fires tend to have thick bark to protect xylem and phloem. They also shed lower branches (self-prune). This reduces the risk that fire will be carried into the canopy. These traits allow conifers to exist in fire-prone ecosystems and suffer little damage from fire under the prevalent fire regime (Schwilk & Ackerly 2001). Thick bark is a key trait for woody fireresistant species (Lawes et al. 2011, Lawes & Clarke 2011). Grasstrees—palm-like arborescent plants, family Xanthorrhoeaceae—are also fire-resistant (Fig. 5.10). Typically less than 5m tall, they retain highlyflammable dead leaves but are seldom killed by fire (Lamont & Downes 1979). The thick dead leaves burn but insulate the stem, protecting the

Paul Asman & Jill Lenoble CC BY 2.0

Figure 5.10: Grasstrees Xanthorrhoea spp., although very flammable, are highly tolerant of fire. The lower leaves are retained after senescence. These dead leaves are burned all the way to the leaf base and form an insulating sheath around the trunk, protecting it from future fire (Lamont et al. 2004).

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apical meristem from exposure to high heat. The leaves do not burn completely and remain as a blackened covering on the trunk. The dead basal leaves can reach >1000◦ C, but the apical meristem stays under 60◦ C (Lamont et al. 2004). And because fire stimulates flowering (Baird 1977), grasstree regeneration is low in long-unburned areas. In South Africa, the tree aloe Aloe ferox retains dead leaves for insulation, as well (Bond 1983). Some animals have adapted behavioural and physiological traits that allow them to resist heating without leaving the burn area. In South African fynbos, angulate tortoises Chersina angulata are capable of scooting to nearby areas of lower fuel within the burn perimeter (Wright 1988). Some mammals enter torpor—a temporary, near-dormant state characterised by lowered body temperature and metabolic rate—which helps reduce stresses from elevated ambient air temperatures during fire, and harsh post-fire environments (Stawski et al. 2015, 2016; Fig. 5.11). Smoke and charcoal cues can induce torpor in some mammals (Stawski et al. 2017), which has been associated with perceived food scarcity (Nowack et al. 2018). Conversely, smoke cues can also interrupt deeper states of hibernation and allow animals to escape oncoming fires (Nowack et al. 2016a).

Unburned Burned

Figure 5.11: Echidnas Tachyglossus aculeatus reduce metabolic rate via torpor and endure wildland fire without fleeing. (L) Michael Williams, US Forest Service, found an echnida that survived during Australia’s 2019-2020 bush fire season. (R) Echidnas exposed to heat and the post-fire environment had lower body temperature, were less active, and spent more time in torpor than counterparts in unburned areas. Graph shows means and 95% confidence intervals for differences before and after the prescribed fire event.

Mean body temp (°C) ●

● −5

−4

−3

−2

−1

0

Total activity (min)

Unburned Burned

●

●

−200

0

200

Total torpor (min)

Unburned Burned

● 0

● 100 200 300 400

Post−fire vs. pre−fire change

Photo: M. Williams, USFS; Data: Nowack et al. (2016b)

The origin of fire-adaptive traits While it is widely accepted that certain traits confer greater fitness under particular fire regimes, the origin of fire-adaptive traits is debated (Simon & Pennington 2012). They could be fire adaptations—traits directly selected for under specific fire regimes (Pausas & Keeley 2009, Keeley & Fotheringham 1998, Keeley et al. 2011). Or the traits are exaptations—those evolved in response to other selective forces (Bradshaw et al. 2011, Hopper 2009), which happen also to be beneficial in fire-prone ecosystems (Gould & Vrba 1982). Exaptations occur when a trait is an adaptive response to a selective force similar to fire (e.g., herbivory and fire both remove aboveground tissue in grasses) such that the trait provides similar fitness benefits when burned as under the original selective force (Lamont & He 2017).

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Structure

Description

Functional Groups

Adaptiveness

Root buds

Buds on roots growing near the soil surface can produce new stems

Many angiosperms; several ferns; several conifers

Vegetative regrowth adaptive under many stressors

Root Crowns

Buds in transitional area between roots and shoots

Most common structure for tree resprouting; common in perennial herbs; common in shrubs; common in grasses

Fairly small bud bank and poorly insulated; more adaptive to nonfire disturbance or infrequent lowintensity surface fire

Stems

Buds in the bark of stems and branches

Common in trees, especially oaks and eucalypts, but also found in other spp.

Typically insulated by location deep within thick bark in fireprone systems, but are also adaptive in cases of wind or insect damage

Lignotubers

Rounded woody structure made of stem tissue; found between root crown and stem base

Mallees (eucalypt); woody shrubs

Protection from large size of burl and thick tissue makes them especially adaptive under high intensity or frequent fire (only occur in fire-prone areas)

Xylopodia

Woody basal burls located at the top of the main root

Small shrubs; woody annuals

Fewer buds and closer to surface of structure; adaptive under frequent, low-intensity fires

Rhizomes

Subsurface stems with shoots or adventitious roots

Woody and non-woody forbs; grasses

Mainly for vegetative spread; adaptive under any disturbance that causes shoot loss

Fleshy structures

Bulbs or tubers below the soil

Perennial herbaceous plants

Associated with carbohydrate storage; adaptive under any shoot-removing disturbance

Caudex

Single undivided trunk that can persist underground for years

Palms; grasstrees (species with undivided stems flanked by dead leaves)

Well insulated (up to 30 cm. below soil surface) and some can spend up to 60 yrs. below ground; adaptive for high intensity or frequent fires (Lamont & Downes 1979)

Table 5.6: Plant structures known to contain meristematic tissue necessary for resprouting following loss of or damage to aboveground tissues. Properties and descriptions summarised from Pausas & Parr (2018). While all traits are fire-adaptive, some provide a clear advantage under a specific fire regime.

Two arguments for exaptation are that some adaptations occur in both fireprone and non-fire prone ecosystems, or traits are also adaptive to other disturbances (Bradshaw et al. 2011). For instance, resprouting occurs frequently and is adaptive to any topkilling disturbance; in fact it is often argued that resprouting in woody plants evolved as a drought response. Similarly, many disturbances can trigger seed release in serotinous species (Lamont 1991), including drought, nutrient limitation, and seed predation

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Figure 5.12: (L) Seed roundness decreased as fire frequency increased in Helenium aromaticum—a fire-stimulated germinating plant in the Chilean matorral. (R) Seed roundness in Helenium aromaticum progeny correlated with seed roundness in maternal plants.

´ ´ et al. (2011) Data: Gomez-Gonz alez

(Hopper 2009, Bradshaw et al. 2011). Serotiny allows for a mass dispersal of seeds, which increases fitness under poor growing conditions, where many propagules will not survive (e.g., drought or poor soil fertility). In addition, mass dispersal saturates the environment with seeds. Since there are more seeds than seed predators can consume, some survive to germinate when they are all triggered to release simultaneously (Kelly 1994). To determine whether a trait is an adaptation to fire, it is common to assess trait divergence within a genus or species across a fire gradient or among different selective forces (Keeley et al. 2011). If members of a genus in fireprone ecosystems have fire-adaptive traits and congenerics in non-fire prone ecosystems do not, it is likely that fire exerted a selective force on species in the fire-prone environment. And if species perform better following fire because of a particular trait than they do under another selective pressure, that is evidence that fire selection drove the adaptation. ´ ´ Gomez-Gonz alez et al. (2011) studied whether fire-stimulated germination in Helenium aromaticum in the Chilean matorral is a fire adaptation. They tested for (1) differences in seed traits across a gradient of fire frequency, (2) whether those differences were heritable, and (3) whether traits increased individual fitness during fire. The study compared the shape, hairiness (i.e., pubescence), and seed coat thickness from populations in areas with different historical fire frequencies—such traits are often associated with fire-stimulated germination. They also experimentally exposed seeds from one of these populations to fire and measured the probability of germination following fire for seeds with different values of these traits. They examined the heritability of seed traits by comparing the expression of the traits between maternal plants and their offspring. They found that seed coat thickness, pubescence, and seed roundness (Fig. 5.12) are positively associated with fire frequency and seed coat thickness and pubescence increase fire-stimulated germination rates. Furthermore, pubescence and shape are heritable (Fig. 5.12). This provides strong evidence that fire is a selective force for fire-stimulated germination in Helenium aromaticum. 67

As described above, the origin of serotiny has been debated—some feel it is an adaptation to fire, while others feel it evolved in response to drought.

Causley et al. (2016) studied whether the serotinous cones of Australian Banksia spp. are fire adaptations.67 The study compared seed release, seed germination, and seedling recruitment in plants subjected to drought or fire and found that the rate of seed release from burned branches ver-

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sus those on branches that experienced simulated drought death varied among species. However, they found higher recruitment and seedling survival in burned plots than unburned plots for the 5 species that germinated, suggesting that serotiny increased plant fitness in post-fire habitats and is therefore likely an adaptation to fire in these species. Molecularly dated68 phylogenetic studies and advances in accurately determining fire regimes in prehistoric ecosystems have increased the ability to assess whether fire adaptative traits are true adaptations to fire or exaptations that evolved in response to other stressors (Simon et al. 2009, Lamont & He 2017). Ecosystem-specific time-calibrated phylogenetic trees (e.g., Fig. 5.13) depict changes in biota over time, showing when modern characteristics first became prevalent in the modern biota (Pennington et al. 2004, Becerra 2005, Linder 2005, Linder 2008). When coupled with knowledge of the timeframe when certain selective forces became prevalent in the species’ environments, phylogenetic trees can provide insight into the factors driving the development of particular characteristics or suites of characteristics. To establish that fire was the selective force behind an adaptation, it is necessary to determine whether fire was prevalent in an ecosystem when the trait evolved. Fire was present in many ecosystems during the early evolution of flowering plants during the Paleozoic and Mesozoic eras.69 Simon et al. (2009) used time-calibrated phylogenies to determine whether fire-adaptive traits in the Brazilian cerrado arose in response to fire. The cerrado is dominated by C4 grasses and typically burns in the winter, several times per decade. It is a global biodiversity hotspot with over 10,000 species, including many endemics (Myers et al. 2000). The study included lineages with both cerrado-endemic species and those absent or rare in the cerrado. Their phylogenetic tree identified 15 cerrado lineages, with the majority arising < 4 million years ago—around the time of the expansion of C4 grasses and the onset of frequent fires in this biome (Cerling et al. 1997). The majority of species in the cerrado lineages were species with fire-adaptive characteristics. Furthermore, Simon et al. (2009) found that these lineages arose long after the development of the climatic regime and acidic, nutrient-poor soils70 that others suggest drove epicormic resprouting and herbaceous vegetation with xylopodia in the cerrado. Diverse fire-adaptive traits were found in cerrado-endemic species, but such traits were rare in related lineages in nearby tropical forest, which historically had very infrequent fires (Simon et al. 2009). These include shrubs with many underground shoots, herbaceous perennials with xylopodia, shrubs with thick corky bark, and pachycaul trees—those with few branches and thick trunks (and typically thick bark) relative to their height.

68

Molecular dating is measuring the time since the divergence of two species by comparing their molecular sequences. Fewer differences occur between species that have diverged more recently (Sauquet 2013).

Figure 5.13: An example of a phylogenetic tree, a diagram that shows the evolutionary relationships among different taxa. It resembles a tree with different species representing branches of the tree. More closely related species share a common branch point more recently in history. 69

See work by Scott (2000), Falcon-Lang (2000), Pausas & Keeley (2009), Scott & Glasspool (2006), Bond & Scott (2010), and Glasspool & Scott (2010).

70

Simon et al. (2009) also note that the rainforest immediately adjacent to the cerrado is similarly nutrient poor yet there are no species with xylopodia or corky bark found there.

Mediterranean-type ecosystems are often the go-to systems for studying fire-adaptive traits and their origins. These systems include the Mediterranean Basin pine forests in Europe, Chilean matorral, South African fynbos, Western Australian heathland, and the chaparral of California, USA (Mooney et al. 1974). These regions are characterized by hot dry summers and mild wet winters where precipitation is greater than evapotranspira-

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Figure 5.14: A crown fire runs up a hill through chaparral during the 2017 Thomas Fire in California, USA.

USFS

tion, resulting in prolific plant growth during the winter. Because of high biomass accumulation in the winter and hot dry summers, Mediterraneantype ecosystems are highly flammable and typically experience frequent high-intensity crown fires (Keeley 2012; Fig. 5.14).

71

Convergence—or convergent evolution—occurs when organisms that are not closely related evolve similar traits because they are confronted with similar selective pressures.

The vegetation of Mediterranean-type ecosystems is very similar, dominated by evergreen shrubs with sclerophyllous leaves—thick, leathery leaves, which are highly flammable because of high concentrations of secondary compounds. The convergence71 in the vegetation in these systems has long been considered climate-driven (Mooney et al. 1974, Dunn et al. 1976), but because of the tight coupling of climate and fire regimes, similar fire regimes could also play a role in the convergence of plant traits in these regions especially for fire-adaptive traits. The shrubs and trees of Mediterranean-type ecosystems all exhibit fireadaptive traits. Many are post-fire seeders where seed recruitment occurs as a single pulse following fire (Keeley & Fotheringham 1997). Others are resprouters, often resprouting from lignotubers (Canadell & Zedler 1995)—a structure that is unique to fire-prone ecosystems. Some post-fire seeders are facultative seeders—they can also resprout. Others are obligate seeders—post-fire seeding is the only mode of reproduction for these species and they require a trigger (either heat or chemical) from fire for germination (Lamont 1991, Ne’eman et al. 2004). Post-fire seeding is prominent in all Mediterranean-type ecosystems except for Chilean matorral. This is considered strong evidence for the role of fire in the evolution of Mediterranean-type vegetation. The Chilean matorral had a very similar geological and climatological history to other Mediterranean-type ecosystems during the Tertiary resulting in the evolution of vegetation resrouting from lignotubers across all Mediterraneantype ecosystems (Montenegro et al. 1983). However, Andean uplift in the

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late Miocene in the Chilean matorral made summer storms and subsequently lightening ignitions less frequent. This loss of frequent fire in the Chilean matorral likely selected against post-fire seeding, as it is most adaptive in areas of frequent crown fires (Keeley et al. 2011). The other Mediterranean-type ecosystems all had frequent crown fires throughout their histories. Molecularly-dated phylogenetic studies have also been conducted in Mediterranean-type ecosystems. In a review of dated phylogenetic studies, Lamont & He (2017) found that fire-adaptive traits arose in South African fynbos and western Australian heathlands either concurrently or slightly after fire became prevalent in those systems, but before drought occurred there, suggesting that fire, not drought, selected for fire-adapted species. As there is little debate that even exapted traits can enhance fitness in fireprone ecosystems, debate over the origin of fire-adaptive traits is largely academic. However, it has been posited that plants with exapted traits will not perform as well as plants with fire-adapted traits under high-frequency fire regimes, which prompts concern that management planned under the assumption that fire-adaptive traits have been directly selected for by fire will lead to local extinctions and ecosystem degradation (Bradshaw et al. 2011, Hopper 2003). Some fear that managers might be too cavalier about the potential for frequent fire to damage plants in fire-prone areas because of the assumption that fire is an important selective force,72 with which plants in those regions have evolved (Bradshaw et al. 2011). Such a debate might not be necessary. Plants are subject to myriad stressors throughout their evolutionary history, many of which occur contemporaneously. This means that no single selective force is likely to have led directly to the evolution of a particular trait. Rather, a fire-adaptive trait has been shaped by the interaction of stressors—fire, nutrient stress, water stress, and even biotic competition interact to drive adaptation. It is often futile to try to disentangle the relative importance of these interacting forces (Keeley et al. 2011). Regardless of the origin of the trait, fire acts as a filter to plant communities, and those without fire-adaptive traits will not become established in fire-prone ecosystems. However, plants are adapted to a fire regime, not fire generally, so any change to the fire regime can be detrimental to population persistence even for species with fire-adaptive traits (Enright et al. 1998, Keeley et al. 2011).

72

Bradshaw et al. (2011) suggest that viewing fire-adaptive traits as adaptations creates a mindset in managers that management fires of any intensity applied with any frequency will not harm plants; viewing fire-adaptive traits as exaptations will lead to a more conservative approach to fire management, because exapted traits are seen as conferring a tolerance to fire (as opposed to the dependence upon fire that adaptation implies).

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6

CHAPTER

Soil properties Physical and chemical effects of heating on soil, soil nutrients, and soil organisms

Soil is a layered, natural body composed of weathered minerals, organic material, air, and water, formed from the geologic parent material and shaped over time by climate, topography, and organisms (FAO 2019). The function of soil goes beyond being the natural medium for plant growth—soil is habitat for organisms that facilitate the growth of those plants and decompose dead biomass. Soil is also an important hydrological regulator and major global carbon storage pool.

FIRE, HEATING, AND SOIL PROPERTIES Variability in fire effects on soil properties can be attributed to three sources: Variability in environmental conditions, during and after fire; the nature of heating, as it relates to fire regime (fire type and intensity); and time—the duration of heating, and the amount of recovery time since fire. Environmental conditions at the time of the burn are important because soil itself is a poor conductor of energy, including heat. Air and organic material provide substantial insulation such that wildland fire effects are generally limited to the top 2–3 cm of the soil surface (Giovannini & Lucchesi 1997; Figs. 6.2 and 6.1). These top centimetres, however, are critical to soil’s function in the ecosystem, and thus the implications of heating in this thin layer extend from organisms to landscapes (Mataix-Solera et al. 2011).

73

We recommend that readers interested in a more in-depth treatment of wildland fire and soil check out the recent text by Pereira et al. (2019), Fire Effects on Soil Properties.

Temperature (°C)

As a natural system that supports the growth of the vegetation that fuels wildland fire, soil has a number of complex interactions with fire.73 Understanding the mechanisms and outcomes of these interactions is key to the sustainable management of fire regimes, and here we provide an overview of fire effects on major soil properties and processes.

160 120 80 40

● ●● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ●

0

2

4

6

8

Soil depth (cm)

10

Data: Bradstock & Auld (1995)

Figure 6.1: Soil heating during wildland fire is often limited to the soil surface, as temperature declines rapidly with depth.

Soil moisture generally limits heat penetration. Dry soils heat faster than wet soils (Fig. 6.3), although heating rate under both conditions slows considerably around 90–95◦ C, as moisture in wet soil, and in the airspaces of dry soil, absorbs energy to evaporate (Campbell et al. 1995). This effect is 85

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USFS

Figure 6.2: The 2013 Rim Fire in California, USA, burned at high intensity on many forested slopes, with high severity to vegetation and soil. Burned Area Emergency Response teams evaluated burn severity to make recommendations on erosion mitigation. Despite the intensity and high severity to soil surface, heating effects penetrated no more than a few centimetres into the soil profile.

74

Temperature (°C)

Major exceptions are fires in soils with high levels of organic matter, e.g., peat. Depth (mm) 0 5

600

15

400

25

200

35 65 95

0 0

20

40

60

due to water’s high specific heat capacity—i.e., water requires a lot of heat energy to raise its temperature, especially through the phase change at 100◦ C. Thus, water absorbs a substantial amount of a fire’s heat, preventing it from transferring through the soil profile (Bad´ıa et al. 2017). Therefore, substantial heating in wet soils requires a high-intensity fire or a sustained heat source, such as a large smouldering log. Two processes cause direct fire effects on soil: Combustion of soil constituents themselves, and soil heating—the penetration of heat energy through the soil medium. Because combustion is generally limited to organic matter in the soil, it is not a major direct contributor to fire effects on soil properties in most soils.74 These are known as ground fires and are discussed more below. In most ecosystems, soil organic matter loss can exacerbate heating impacts. Heating drives physical changes in soil constituents, such as making minerals and soil aggregates brittle, or volatilising chemical compounds. Overall, the primary regulators of heating effects on soil are fire intensity and soil moisture, although some aspects of soil structure and texture can increase heating depth (Mataix-Solera et al. 2011). Table 6.1 summarises various physical and chemical effects of soil heating from wildland fires.

Fire effects on soil physical properties

Heat exposure (min) Soil condition Dry

Wet

Data: Campbell et al. (1995)

Figure 6.3: Wet soil heats more slowly than dry soil. As depth increases, rate of heating and maximum temperature decline, even after an hour of heat exposure.

Two perspectives on soil effects include (1) soil for its own sake, as a medium for plant growth; and (2) hydrology, which is the study of the movement and distribution of water in natural systems. As the two perspectives are interconnected—plants need water to grow, and vegetation affects hydrology—we do not distinguish between them, and use evidence from both. The hydrological perspective helps tell the story about fire and physical soil properties. Scaling fire impacts on a few centimetres’ worth of soil to the entire landscape is made easier by describing how water moves and where

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Category Property

Description

Summary of fire effects

Infiltration rate

How quickly water moves down into soil from the surface.

Porosity

Airspace—volume of pores in soil, divided by bulk density.

Structure

Aggregate stability, soil’s ability to maintain physical structure when exposed to external forces; Bulk density, amount (mass) of soil in a given volume. Thin, impermeable layer at, or just below, soil surface. Limitation on infiltration rate.

Decreases if surface resists water absorption or soil restricts hydraulic conductivity. Generally declines as soil particles destabilise and bulk density increases. Combustion of organic matter can destabilise particles, cause smaller particles to pack more closely together and increase bulk density. Depends heavily on pre-fire soil conditions and fire intensity, but often increases under moderatehigh severity burns.

Physical

Water repellency

Chemical Cation exchange capacity pH

Capacity of soil particles to retain cations for exchange via soil water. Measure of hydrogen ion activity. Neutral=7, acidic7.

it ends up (or doesn’t). When fire alters landscape hydrology, recovery depends on how quickly various characteristics of that landscape themselves recover—vegetation and ground cover, water repellency, soil organic matter, soil structure, and porosity (Ebel & Martin 2017). Many of these occur in Table 6.1, although recovery rates can vary widely (Fig. 6.4). Infiltration rate is a measure of how long it takes for water to soak into soil. This is an important property for soil moisture levels because the longer it takes for soil to absorb water, the more opportunity there is for the water to run downslope or evaporate. Thus, soil on steep slopes with low infiltration rates are at greater risk of insufficient moisture to support vegetation. Many factors that can decrease infiltration are discussed in this section as physical consequences of fire in their own right (Ebel & Moody 2017). Firedriven physical processes that reduce infiltration broadly fit two categories: barriers that block water at or near the surface, and alterations to soil structure such that water flows less easily between soil particles. Barriers at or near the soil surface can reduce infiltration. Barriers include ash crusts (Balfour et al. 2014, Bod´ı et al. 2014) and soil water repellency—also known as hydrophobicity. Water repellency occurs as a layer of soil that is resistant to water absorption (DeBano 2000; Fig. 6.5). The water-repellent layer develops when combustion at the soil surface volatilises organic compounds, which then move down the soil profile (DeBano et al. 1970). But since soil is a poor conductor of heat, volatilised compounds rapidly condense, accumulate, and bind with soil particles

Declines in proportion to organic matter loss. Typically increases temporarily after cations are released by fire.

Table 6.1: Soil properties known to be affected by wildland fire from DeBano et al. (1998) and Certini (2005, Table 2). Many effects are characterised by a high degree of variability across ecosystems, fire regimes, and especially over time.

Organic matter Structure Inverts Vegetation Water repellency 0

5

10

15

20

Recovery time (yrs)

Data: Ebel & Martin (2017)

Figure 6.4: Post-fire recovery rates of various soil responses. Bubble thickness depicts the number of studies at each value. Structure includes aggregate stability and bulk density; Inverts refers to soil fauna and bioturbidation; Vegetation includes plants, fine root growth, and ground cover.

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to create an impenetrable layer just below the soil surface. DeBano et al. (1976) showed that steeper temperature gradients are associated with more pronounced water repellency in laboratory trials, leading to their recommendation that prescribed fires in areas with repellency-prone soils be conducted under high-moisture conditions. In corroboration, Bad´ıa et al. (2017) also found that wet soils resisted water repellency under laboratory conditions, and field data by Huffman et al. (2001) show no soil water repellency when soil moisture levels were above 12-25%. Christoph Langhans, with permission

Figure 6.5: A thick water-repellent layer on the surface of severely-burned soils under Eucalyptus forest in south-eastern Australia. Infiltration was limited to flow through soil macropores.

Laboratory work suggests soil water repellency follows lower-intensity fires rather than high-intensity fires, presumably because high-intensity fires drive volatilised compounds out of the soil before they can move downward (Robichaud & Hungerford 2000, DeBano & Krammes 1966). But data from the field often show greater water repellency as burn severity increases (Cawson et al. 2016, Huffman et al. 2001); these studies suggest relationships between heating, soil water repellency, and infiltration rate vary with the spatial scale of measurement. Consider also that fire is not the only reason soils might have poor infiltration. Even unburned forest soils can demonstrate a high degree of soil water repellency, especially when ´ they are dry (Fernandez et al. 2019, Robichaud & Hungerford 2000). In fact, Williams et al. (2016) found that pre-fire soil water repellency was a stronger determinant of post-fire hydrology than heating during the fire. Infiltration can also be restricted by loss of porosity—the airspace, or pores, between soil particles. Soil heating can alter soil structure and reduce the amount or connectivity of pores through which water could otherwise flow. But how heating affects porosity can depend on soil type: Giovannini et al. (1988) found that heating reduced porosity in a sandy loam soil, but increased porosity in a silty clay until about 460◦ C, after which it also showed reduced pore space. Thus, the variability in heating effects on soil porosity seems most variable at lower severity (less-intense fire, or deeper soil). Although fine ash particles have been implicated as a means to reduce infiltration by clogging pores (e.g., Mallik et al. 1984), recent work suggests this process is actually unlikely to occur (Stoof et al. 2016). Persistence of limited infiltration in post-fire soils varies substantially, particularly among different fire intensities, although soil water repellency generally diminishes within five years post-burn (Fig. 6.4). Recovery times vary from within a season after a severe wildfire (Larsen et al. 2009) up to a ´ year after a prescribed fire (Jimenez-Pinilla et al. 2016), or as long as five years (Robichaud et al. 2013). Among various fire intensities in Ponderosa pine stands, Huffman et al. (2001) reported weakened soil water repellency in some burns as early as 3 months, but persistence as long as 22 months. Structure consists of aggregate stability—ability of soil to maintain physical structure when exposed to external forces (Mataix-Solera et al. 2011)—and bulk density, which refers to the mass of soil in a given volume. As aggregates destabilise and break down, bulk density increases as pores fill with microparticles (Certini 2005). While fine particles might be more susceptible to erosion, one of the main consequences of soil structure loss is the capacity for air, water, and roots to move easily through soil as a burned site recovers. Heating can also cause an increase in coarse particles

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Fire effects on soil chemical properties Cation exchange capacity refers to the availability of sites on soil particles to hold cations—positively-charged molecules—for exchange in the soilwater solution. Because these sites are primarily on soil organic matter, cation exchange capacity decreases as burn severity increases organic matter combustion (Fig. 6.6). These exchangeable cations comprise most of the plant-available nutrients that are discussed below. But fire-reduced cation exchange capacity—which affects a small volume of soil—does not necessarily limit nutrient availability, although it might increase the probability of transport (Ulery et al. 2017). Fire converts many of these cations to immediately available mineralised forms (Ulery et al. 1993).

25

CEC (cmol/kg)

when smaller clay particles fuse together (Bad´ıa & Mart´ı 2003). The extent of structural alteration varies between fires and along heating gradients ´ et al. 2011), and soil structure can have one of (Varela et al. 2015, Jordan the longest recovery times among fire-affected soil properties (Fig. 6.4).

20 15 10 5

●

Unburned

Low

High

Burn severity

Data: Ulery et al. (2017)

Figure 6.6: Cation exchange capacity (CEC) decreases as burn severity increases, due to greater combustion of soil organic matter.

pH is a measure of hydrogen ion activity in a solution and refers to how acidic (pH < 7) or alkaline (pH > 7) the solution is. Increased pH immediately after fire is among the most uniform responses to wildland fire (Certini 2005). pH can increase as much as 3 units (Ulery et al. 1993), although the magnitude of both the change and its effects on the ecosystem depend on the initial pH before the burn. Such sharp increases in soil pH are usually temporary and often do not persist after the first post-burn rain event, but pH can remain slightly above unburned levels for longer time periods. Ash75 is the main source of fire-driven increases in soil pH. As the chemical composition of ash varies with fire intensity,75 more severe burns tend to produce the highest soil pH by generating “white ash” at high temperatures,76 which leaves behind nutrients such as potassium and sodium in mineralised forms (Pereira et al. 2014, Ulery et al. 1993). These minerals are water-soluble and thus susceptible to transport, either via run-off or infiltration and percolation through the soil profile, which accounts for the ephemeral spikes in soil pH. Slightly higher soil pH can persist for years after a burn due to residual calcite, which is much less water-soluble than mineralised nutrients (Ulery et al. 1993).

75 Bod´ı et al. (2014) define ash as the particulate residue remaining, or deposited on the ground, from the burning of wildland fuels and consisting of mineral materials and charred organic components. Differences in plant species, life stage and nutrient status, and local geology contribute to differences in ash chemistry. 76

Residual organic carbon leaves blackened ash, thus whiter ash indicates more complete combustion (Goforth et al. 2005).

NUTRIENT POOLS Wildland fire effects on soil nutrients are highly variable (Table 6.2). In addition to previously-identified sources of variability—environmental conditions at the time of the burn, differences in fire type and intensity—variability in soil nutrient responses is further increased by the diversity of forms in which nutrients exist in the soil, and interactions with plant and microbial communities as they themselves respond directly and indirectly to fire. Fuelbed characteristics such as fuel load, density, and moisture content also modulate fire effects on nutrients, with losses generally decreasing as fuel density and moisture content increase (Gillon et al. 1995).

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Element

Relevant forms

Summary of fire effects

Total C

Generally tends to increase. Immediate effects include redistribution lower in soil profile, especially after wildfire. Long-term increases follow indirect effects to post-fire organisms. Surface litter and duff often combusted, reducing C stocks. Soil organic matter combusts around 220o C. Immediate increases at low–moderate heating, declines after 3+ mo. Effects greatest after wildfire. General long-term increases as post-combustion particles move down soil profile and soil microbes and roots grow, die, and decay. Increases after fire. Subject to vertical and horizontal transport. Resists decay longer than non-charred carbon. Turnover time depends on fire intensity and what soils the eroded charcoal gets deposited in.

Carbon (C)

Organic matter (OM, SOM) Soluble carbon (Extractable C) Soil organic carbon (SOC) Charcoal (black C, PyC) Nitrogen (N) Total N Organic N Ammonium (NH+ 4) Nitrate (NO− 2)

Little change. Generally considered stable to approx. 200o C. Loss to volatilisation—frequent in organic layers—can occur in mineral soil under very high intensity. Substantial increases immediately after fire. Return to pre-fire levels within a year. Effects increase with fire intensity. Modest increases after fire followed by peak 6–12 mo after fire. Effects increase with fire intensity.

Phosphorous (P) Total and available

Table 6.2: Effects of fire on major soil nutrients. Fire effects summarised from meta-analyses by Wan et al. (2001), Wang et al. (2012), and Butler et al. (2018) and are limited to results from mineral soil layers.

Increases in most ecosystems except coniferous forest. Effects increase with fire intensity.

Considerations for soil nutrient research Absolute vs. relative amounts. The amount of a nutrient in the soil is often referred to as the pool and is often expressed as either the actual amount of a material in the system (often by mass), or the proportion of a volume that is comprised of a material (often as a proportion or concentration). Whether fire effects are expressed as absolute change in the overall pool size, or change in the concentration of the pool in the soil medium, can substantially alter the interpretation of the effect, especially when fire alters the overall volume and affects other constituents disproportionately. For example, fire can substantially reduce nutrient pool sizes in the forest floor, but changes in concentrations are less dramatic when the main fire effect is overall reduction of organic matter via combustion (Nave et al. 2011, Wan et al. 2001, Belillas & Feller 1998). Organic vs. mineral soil. In ecosystems with a layer of undecomposed litter, such as temperate forests, distinctions must be made between nutrients in the “forest floor” and the actual mineral soil (Nave et al. 2011). The forest floor is typically high in organic matter, much of which is directly consumed by combustion. Thus, fire can substantially reduce nutrient pools in

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organic layers but not affect the mineral soil pools (Klopatek et al. 1990). While combustion can consume soil organic matter, soil heating causes most effects to mineral soil, which are therefore limited to the top few centimetres except under extremely intense fire (Stoof 2019, Wan et al. 2001). Totals vs. types. Soil nutrients occur in organic and mineral forms. Soil nutrients are often sampled and reported as a total—e.g., “total N” or “total P”—but such data do not differentiate between the different forms. Generally speaking, organisms can use only the charged, mineralised forms. Mineralised forms are often referred to as “available cations” or “extractable” nutrients because these mineral cations are soluble, and extractable from soil samples via organic extractions. Soil heating from wildland fires can facilitate mineralisation, increasing the proportion of soluble cation forms in the total nutrient pool (St. John & Rundel 1976). Very generally, mineralised forms appear most abundant when soil heating reaches 100◦ C but does not exceed 200–300◦ C (e.g., White et al. 1973). Pools vs. rates. This distinction is a foundation of ecology: Odum (1968) delineated ecosystem structure and function, which included quantities and distributions, and cycling rates, respectively. Even when soil nutrient information differentiates between the different forms of an element, sampling the pool is only a snapshot of nutrient availability. High levels of labile forms could be assimilated quickly or leach out of the system, reducing the stability of the pool. Determining how fire affects mineralisation rates offers insight into how amounts and proportions of nutrient pools will change in the time since the fire, an essential component of ecosystem recovery.

Fire effects on nutrient pools Carbon Over the long term, mineral soil carbon pools generally increase due to wildland fire, although reductions have been observed after high-frequency burning (Pellegrini et al. 2017). Thus the main component of fire regime affecting soil carbon pools appears to be fire return interval. Burn season can also modulate long-term fire effects on soil carbon, as fire and biological decomposers compete for plant biomass (Fynn et al. 2003).

CSIRO CC BY 3.0

Fire releases nutrients bound in plant material. The Kapalga Fire Experiment in Australia’s Kakadu National Park monitored nutrient losses and erosion in Eucalyptus woodlands from 1990–1994 (Andersen et al. 1998). Prescribed burning is used in commercial forestry worldwide to recycle nutrients and reduce wildfire risk (e.g., in South African pine plantations; de Ronde & Stock 1994).

Most carbon in the Earth system is bound up in geologic deposits and oceans, but the atmosphere and terrestrial ecosystems each hold a substantial amount of carbon (Falkowski et al. 2000). Plants are a link between the atmospheric and terrestrial carbon pools—they assimilate atmospheric carbon via photosynthesis, and return it to the soil via exudates and the decay of plant tissue. Terrestrial carbon is roughly split between living and dead biomass, and can be further divided into aboveground biomass, belowground biomass, and the soil itself. Terrestrial carbon pools cycle at different rates: live biomass transitions to dead biomass at timescales from days to centuries, depending on organismal lifespan; dead biomass not carried downstream into aquatic ecosystems is decomposed and added to

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the soil; soil carbon is divided into several pools that turn over at different rates ranging from years, centuries, and millenia (Prentice et al. 2001). Carbon sequestration is an important concept in climate change science. Biological processes transfer carbon from the atmosphere—where carbonbased gases like carbon dioxide (CO2 ) and methane (CH4 ) contribute to the greenhouse effect—into the soil. But due to substantial differences in carbon cycling rates, simply increasing soil carbon does little for long-term atmospheric carbon balances if only labile (short-term) pools increase. Long-term carbon sequestration on the scale of hundreds and thousands of years require recalcitrant soil carbon that resists remobilisation. Wildland fire has direct and indirect effects on the terrestrial carbon cycle.77 Directly, by converting carbon to different forms, fire is a decomposition process itself—recall from Ch. 2 that combustion essentially undoes photosynthesis (Eqs. 2.2 and 2.1). Fire also slows biological decomposition by charring organic matter and reducing it to fine particles that move down the soil profile (Metz et al. 1961). Indirectly, fire can alter factors that modulate ecosystem productivity, with contrasting effects over multiple temporal scales. For example, even if fire reduces carbon stocks by consuming soil organic matter, post-fire conditions can limit subsequent decomposition and increase organic matter accumulation (Semenova-Nelsen et al. 2019). Fire often stimulates root production, which contributes to an increase in soil organic matter as fine roots die and decay.

77

Strong convection during intense fires can also carry fine carbon particles into the atmosphere. Such carbon forms the particulate matter that can harm human respiratory health in the region around a fire, and affect global climate when borne into the upper atmosphere. Both consequences are discussed in the last section of this chapter.

Organic matter is a raw form of carbon in the plant-soil system. It is also labile, turning over as quickly as decomposition allows. Organic matter above the soil surface—e.g., standing vegetation and litter, such as the forest floor—is susceptible to combustion. Carbon from organic matter combustion either escapes to the atmosphere as gases (e.g., CO2 & CO) or remains on the soil surface.77 Organic matter in mineral soil layers is also susceptible to direct effects of soil heating—Giovannini et al. (1988) reported soil organic matter combustion at 220◦ C in the lab, and reviewed field studies in which surface soil temperatures ranged between 330–850◦ C. Ground fires are extreme cases of soil organic matter combustion. In a ground fire, combustion occurs in soil layers with high levels of organic matter, such as forest duff and peat (Fig. 6.7). Ground fires consist of smouldering combustion that can occur deep in the organic layer, making them difficult to control; without flames, ground fires can be difficult to detect (Rein et al. 2008). Furthermore, ground fires can persist for weeks or months. In a laboratory, Huang & Rein (2019) described peat burning as a two-stage process: when ignited at depth, combustion occurs as the oxidation of peat material (at about 300◦ C) and the smouldering front moves upward, weakening—but not collapsing—the structure of the organic layer. In the second stage, the smouldering front moves back down, burning up the previously charred peat. As the fuel is drier, the second stage smoulders hotter (600◦ C) and consumes the organic layer. Peat fires occur around the world and are a major source of carbon emis-

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US FWS

sions to the atmosphere. Page & Hooijer (2016) reviewed peatlands and peat fires worldwide: Globally, peatlands comprise just 3% of the Earth’s land area but hold 32–46% of the total soil carbon pool. Peatlands are mostly associated with northern temperate environments—Canada has 10 times more carbon in peat than in managed forests—but peat occurs and burns in places as varied as Andean cloud forests and Botswana’s Oka´ vango Delta (Roman-Cuesta et al. 2011, Gumbricht et al. 2002). Southeast Asia has significant peatlands that make disproportionate contributions to Earth’s carbon budget (Page & Hooijer 2016). The 1997 fire season in Indonesia alone produced carbon emissions equal to 13–40% of that year’s global fossil fuel emissions (Field et al. 2016).

Charcoal is a unique form of organic carbon produced by fire. Managers have long been aware that charcoal alters several soil properties, including soil moisture and nutrient pools (Tryon 1948). More recently, attention has turned to charcoal—known alternatively as black carbon or pyrogenic carbon (a.k.a. PyC)—as an important pool in the global carbon budget (Reisser et al. 2016). Post-fire charcoal can constitute as much as 11% of the carbon emitted by fire (Krishnaraj et al. 2016). Charcoal can take hundreds to thousands of years longer to decay than other forms of soil organic carbon, with potential for long-term carbon sequestration (Fig. 6.8). This recalcitrance is due specifically to strong double bonds between carbon atoms and low nitrogen content in particles; the formation process is accelerated by fire (Pingree & DeLuca 2017). Charcoal also promotes mineralisation of nutrients in the soil by providing microsites for soil microbes (DeLuca et al. 2002), creating a “charosphere” of habitat and resources for microbial communities around these particles (Quilliam et al. 2013). However, it is unlikely that a single charcoal particle can provide all of these benefits simultaneously—not all pieces of charcoal are created equally.78 Higher heating creates increasingly complex carbon molecules in the resulting charcoal (McBeath et al. 2011). Greater complexity of charcoal

Figure 6.7: In 2011, the Lateral West Fire at the Great Dismal Swamp National Wildlife Refuge in Virginia, USA, burned peatland. Combustion proceeded as a ground fire, releasing smoke but little flame as it burned deep into the organic layer, leaving a reddish residue on the mineral surface. Trees toppled as the organic matter supporting them combusted, and downed trees themselves caught fire and smouldered.

pixabay.com

Figure 6.8: Charred wood decomposes much more slowly than uncharred wood. In addition to providing binding sites for plant-available nutrients, charcoal particles in the soil can contribute to long-term carbon sequestration.

78

On the other hand, heterogeneity in fire intensity likely ensures the full range of charcoal types are created if maximum intensities occur in a patch distribution (Matosziuk et al. 2019).

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molecules increases resistance to decomposition by reducing microbial activity (Jenkins et al. 2014). And while some charcoal becomes soluble ´ and percolates down the soil profile (Soucemarianadin et al. 2019), much is transported laterally by run-off (Cotrufo et al. 2016). Charcoal deposited in soils at lower landscape positions tends to decompose faster, especially if it was created at lower temperatures (Abney et al. 2019). These factors limit the actual effect of charcoal in fire-prone landscapes. Carbon also occurs as a soluble form available to microbes but susceptible to transport. Soluble carbon pools increase with low to moderate heating in the lab and immediately after fire in the field, but tend to decline as biological processes consume it, or it is lost to run-off or leaching (Santos et al. ´ 2016, Lombao et al. 2015, Prieto-Fernandez et al. 1998).

Nitrogen

Kaibab National Forest, USFS

Figure 6.9: Fire intensity is an important factor determining the magnitude and duration of fire effects on nutrients. Top: Low-intensity surface fires, such as those that creep through grass fuels in a Ponderosa pine savanna, volatilise organic nitrogen from plant tissues but have little effect on soil beyond depositing other elements released from plant material. Bottom: In addition to releasing organic nutrients from plant biomass, high-intensity fires such as those that burn through Juniperus crowns can alter soil nitrogen and increase the availability of inorganic nutrients.

Soil nitrogen pools generally increase following wildland fire, and the most important component of the fire regime appears to be fire intensity (Fig. 6.9). Several meta-analyses demonstrate an overall net increase, or ˜ et al. 2018, Nave et no change, in soil nitrogen across ecosystems (Alcaniz al. 2011, Wan et al. 2001). Much of the nitrogen in plant biomass—in both forest litter and grassland fuels—is volatilised and released to the atmosphere (Caldwell et al. 2002, Hobbs et al. 1991, DeBell & Ralston 1970). In the mineral soil, the magnitude of direct effects, and the duration of indirect effects, depend on the severity of soil heating. Nitrogen is the most abundant element in the Earth’s atmosphere, but most atmospheric nitrogen occurs in an inert gas form (N2 ) that is unavailable to biological processes. Plants require “fixed” nitrogen, produced by microbes that convert N2 into ammonia (NH3 ). The mineral cation form—also plantavailable—is ammonium (NH+ 4 ). The relative proportion of ammonia and ammonium depends on soil pH: at low pH, hydrogen ions (H+ ) more easily react with NH3 to form NH+ 4 . Soil bacteria then convert ammonium to nitrite − (NO2 ) and finally to nitrate (NO− 3 ), which can be easily taken up by plants. Nitrogen bound up in the biomass of plants, animals, and bateria—living and dead—constitutes the organic nitrogen pool. Total N is the combination of all mineral forms and organic nitrogen. Fire has substantial effects on almost all forms of nitrogen in the plant-soil system. In a review of fire effects on nitrogen, Smithwick et al. (2005) summarise mechanisms that occur during the fire event (volatilisation, pyrolysis, ash deposition, translocation of nitrogen from organic soil to mineral soil, and destruction of root and microbial cell walls) and after fire passes. Indirect, post-fire effects include shifts in microbial activity due to altered soil abiotic properties (e.g., pH, temperature, and moisture); altered availability and quality of microbial substrates, and altered microbial community abundance and composition. At longer time scales, nitrogen dynamics are affected by post-fire vegetation, which can interact with microbial communities to affect nitrogen input and uptake (Hart et al. 2005). Soil heating can cause the physiochemical degradation of organic matter

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Ammonium

100 75 50

Nutrient level (mg/Kg soil)

and increase available nitrogen. A frequent pattern in post-fire nitrogen dynamics is a short-term increase in ammonium right after fire, and a lagged increase in nitrate (Smithwick et al. 2005). Between 100–230◦ C, proteins in soil organic matter break down into ammonia, which in the presence of charged clay particles, is retainable in soil as ammonium; but at 400◦ C, volatile nitrogenous compounds are released (Russell et al. 1974). This effect likely accounts for higher ammonium levels immediately after burns, and increased nitrate levels shortly after fire, as microbial nitrification is stimulated by the available ammonium (Kovacic et al. 1986). A general trend is for the magnitude of the ammonium increase to scale up under higher-intensity fires (Fig. 6.10), and subsequent increases in nitrification can persist for decades under severe fires (Kurth et al. 2014).

25

Nitrate 30 20 10 0

50

Many nutrients are infrequently reported in the fire effects literature, making generalisations difficult. Reviewing prescribed fire effects on soil nu˜ et al. (2018) concluded that fire generally increases availtrients, Alcaniz able cations, but that these increases are typically ephemeral. Also understudied are heavy metals, which can be toxic to humans and harmful to ecosystems. Abraham et al. (2017) review how fire mobilises heavy metals, especially in industrial areas where pollutants accumulate in plant tissue and become volatilised or concentrated in ash.

150

200

Severity (Increasing fuel load) Unburned

Other elements

100

Days since fire

Low

Moderate

High

Data: Kendawang et al. (2005)

Figure 6.10: Ammonium and nitrate levels depend on time-since-fire and severity in a shifting cultivation system in Malaysia. In these plots, severity was increased by adding vegetation biomass (100, 200, and 300 Mg/ha, respectively). Note how high-severity fires immediately increased ammonium, but nitrate responses lagged.

Phosphorous Total and available phosphorous pools generally show fire-driven increases in the mineral soil of ecosystems around the world, with the exception of coniferous forests (Butler et al. 2018). These effects increase with fire intensity and appear strongest when burns occur later in the growing season, although winter and spring burns show moderate increases as well. In the top 2 cm of soil in an Australian eucalyptus forest, Tomkins et al. (1991) found that both total phosphorous and available phosphorous increased after fire, with greater increases in plots burned at higher intensity, and values for both were even higher after 100 mm of rain. Fire immediately increased available phosphorous in the mineral soil of high-intensity, slash-burn plots relative to undisturbed forest soils in Mexico (Giardina et al. 2000). And in a semi-arid grassland in China, extractable phosphorous levels in the top 10 cm were 44% higher than adjacent unburned areas one year after a wildfire (Liu et al. 2018).

Potassium appears to increase after fire, but availability in mineral soil layers might depend on fire intensity and parent material. In an Australian eucalyptus forest, exchangeable potassium pools increased after fire, with spikes after 100 mm of rain, and increases greater at higher fire intensities (Tomkins et al. 1991). In the Chinese grassland reported above, available potassium in the top 10 cm was no different from unburned soils a year after a wildfire (Liu et al. 2018). In a laboratory burning experiment on soils from under Ponderosa pine stands in western South Dakota, USA, White

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et al. (1973) found that heating to 500◦ C released water-soluble potassium from shale-derived soils; lower temperatures and sandstones produced no increase in water-soluble potassium.

SOIL ORGANISMS AND MINERALISATION Soil microbes Organisms of all sorts spend their entire life histories in the soil, and are critical to ecosystem processes. Collectively, “soil microbes” refer to the fungi and bacteria that perform essential functions including decomposition and nutrient cycling. Some microbes, like mycorrhizal and endophytic fungi, can support host plants by increasing nutrient uptake efficiency. Fire affects soil organisms directly through soil heating, and indirectly through the modification of the soil physical and chemical environment, as well as potential alterations to host plant communities (Mataix-Solera et al. 2009). Soil microbes are difficult to study, and the unpredictability and variability of fires leaves the responses of these taxa understudied and poorly reported (Zaitsev et al. 2016). Although tools to study microbes have advanced substantially in recent decades, consensus on the response of soil organisms to fire and soil heating has been relatively slow to develop. An early study on “microfauna” in longleaf pine stands in the Southeastern US was limited to organisms from sieved soil observable under a 15-power microscope, and concluded that frequent burning was deleterious (Heyward & Tissot 1936). But molecular techniques allow investigation of taxa previously too difficult to study, and in the same longleaf pine ecosystem, Hansen et al. (2019) found that recurrent fire did not affect the abundance of soil fungi (despite fungi being considered one of the most sensitive soil microbes to fire; Pressler et al. 2019). Conventional wisdom suggests fire has negative effects on soil microbes, although variability complicates generalisations. Several meta-analyses show overall reductions in soil microbeal abundance, total biomass, and diversity (Pressler et al. 2019, Dove & Hart 2017, Dooley & Treseder 2012), with little evidence of recovery over time. A meta-analysis by Wang et al. (2012) found fire reduced measures of overall soil microbial biomass; the greatest reductions occurred within three months of fire and post-fire recovery took about three years.

79

Fire effects on decomposition are not consistent across ecosystems. For example, contrast slower decomposition in conifers of the Southeastern US (Semenova-Nelsen et al. 2019) with faster decomposition of wood in northern coniferous forests of Montana, USA (Page-Dumroese et al. 2019).

Fire effects on soil organisms can affect nutrient cycling, but the connection between direct effects on organisms and indirect ecosystem responses is complex. In the longleaf pine ecosystem, a restructured, post-fire fungal community decomposes surface fuels more slowly, which reinforces the fire regime by allowing fuels to build up (Semenova-Nelsen et al. 2019).79 After a wildfire in southwestern USA reduced the overall abundance of nitrogenfixing and nitrite-forming soil bacteria in a coniferous forest, ammonia and nitrate levels recovered to, or exceeded, levels in unburned soils about a year after the fire (Yeager et al. 2005). Substantial differences in the community composition of burned and unburned areas suggest some genetic

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groups can maintain nutrient cycling after fire even at overall lower abundances.80 Thus, organismal impacts are better viewed in an ecological context—how fire affects overall ecosystem function (Hart et al. 2005). Two important trends emerge from the literature on fire effects on soil organisms: Fire severity81 is an important source of variability in fire effects on soil organisms, and within low- to moderate-severity burns, assemblages of soil organisms might change in response to burns but overall abundance—and overall activity—either remain stable or recover quickly. Several examples from the primary literature supports these conclusions (Smith et al. 2017, Shen et al. 2016, Scharenbroch et al. 2012). The boreal forest demonstrates variability in fire effects on soil organisms. In boreal forests, organic matter accumulates in upper soil layers and on the soil surface.82 These layers provide niche diversity for soil microbes and afford a range of variability in fire severity in terms of the degree of soil organic matter combustion. A broad suite of studies have examined fire effects on soil organisms by sampling across fire types and from multiple soil strata; several have also used long chronosequences with 100-plus years since the last fire. Most responses either show recovery within sev¨ eral years (Cutler et al. 2017) or over several decades (Koster et al. 2014).

80

Sun et al. (2015, 2016) make this point explicitly in discussing the function of distinct phylogenetic groups after fire in boreal forests for both fungi and bacteria. 81

Another meta-analysis by Wang et al. (2012) found less dramatic impacts on soil organism abundance and function from prescribed fires vs. wildfires, although the authors lament the paucity of specific information in primary literature on fire severity.

82

Holden et al. (2016) distinguish between two organic horizons—fibric, a top layer with recognisable plant parts, and humic, a second layer of highly decomposed organic matter—above the mineral soil.

Belowground communities undergo succession as boreal ecosystems ¨ recover from fire, and recovery time varies with fire severity (Malmstrom 2010). Both fungal and bacterial community composition changes with time, as post-fire conditions that favor certain groups recover to more closely reflect pre-fire conditions (Sun et al. 2015, 2016, Cutler et al. 2017). Although overall microbial biomass can be depressed for decades after a ¨ boreal forest burns (Koster et al. 2016), taxonomic diversity and richness is often unaffected by fire (Sun et al. 2015, 2016, Tas¸ et al. 2014). Microbial abundance rebounds as soil organic matter accumulates in the years and decades after a fire; by removing more organic matter and affecting deeper layers, high-severity burns require more time to recover. Larger soil organisms such as arthropods can survive lower-intensity fires in lower soil layers and more rapidly restore populations throughout the soil profile post-fire; after higher-intensity burns, soil organisms recolonise more slowly from surrounding unburnt areas (Gongalsky & Persson 2013). Meanwhile, boreal mycorrhizal fungi are sensitive to even low-intensity fire, especially in upper layers with high organic matter and susceptibility to combustion (Holden et al. 2016). Despite reducing organic carbon pools, high-severity boreal fires might slow carbon turnover by altering microbial communities. High-intensity fires reduce the abundance of saprophitic fungi capable of breaking down recalcitrant carbon (Holden et al. 2016). Thus, for decades after a boreal forest burns severely, soil carbon pools increase as organic matter accumulates, which potentially offsets carbon lost to the atmosphere via combustion (Holden et al. 2016, Tas¸ et al. 2014). However, increasing air (and thus soil) temperatures as a result of global change appears to elevate soil respiration even after high-severity fires (Bergner et al. 2004).

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98  Ecology of Fire-Dependent Ecosystems

Mineralisation rates

µJ1JGU\VRLO

 

1LWUDWH

 

$PPRQLXP









,QFXEDWLRQSHULRGGD\V 7UHDWPHQW %XUQHG

8QEXUQHG Data: Carreira et al. (1994)

Figure 6.11: Fire increased ammonium pools and nitrification rates in soil of a Spanish Mediterranean shrubland.

Ecosystem properties can be divided into states and rates (Odum 1968); here, the size of various nutrient pools and how rapidly nutrients are converted between their various forms, respectively. Soil microbes are clearly important to nutrient dynamics, are generally affected by fire, and often show contrasting responses to fire at different times since the event as other environmental conditions change. Fire has long been recognised as a mineralising agent in wildland ecosystems (e.g., St. John & Rundel 1976). Thus, a robust understanding of fire effects on an ecosystem includes not only how quantities have been altered (pool size), but also how quickly nutrients move among pool types (rates). A key ecosystem process is nitrogen mineralisation—the conversion of soil nitrogen into available forms by soil microbes. Fire generally increases nitrogen mineralisation (Fig. 6.11), at least in the short term: After a 70% increase in the first three months after fire, nitrogen mineralisation rate declines with time-since-fire (meta-analysis by Wang et al. 2012). Mineralisation rates are often studied by incubating soil samples in a laboratory, and measuring how much of various nutrients are produced over time (frequently, ammonium and nitrate; e.g., Fig. 6.11). Soils collected from burned areas produce more mineralised nitrogen in several such incubations (Carreira et al. 1994, Ando et al. 2014, Zhang et al. 2018). Zhang et al. (2018) found that certain bacterial genetic functional groups were associated with greater mineralisation potential, and Smithwick et al. (2012) showed that nitrogen mineralisation rates were greatest when the microbial community had more bacteria than fungi. Nitrification, specifically, was greater when ammonium pool sizes were larger and pH higher. While Kurth et al. (2014) found that higher ammonium elevated nitrification rates for decades after severe fires in southwestern USA Ponderosa pine stands, they also note that these nitrification rates are similar to those under stands that have been restored to a pre-European settlement condition. Thus, the novel state might in fact be the low nitrification rates observed prior to fire.

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CHAPTER

7

Water and the atmosphere Erosion, run-off, and impacts to water resources; feedbacks between the atmosphere, vegetation, and fire

EROSION, AQUATIC IMPACTS, AND WATER RESOURCES Wildland fires impact water resources. Most impacts occur after the fire passes, and relate to the severity of the fire in the catchment—e.g., the extent to which forest canopy cover is reduced, and how much combustion reduced surface fuels, litter, and soil organic matter. Post-fire runoff can carry away ash and cause soil erosion, increasing nutrient and sediment loads of downslope water bodies. Greater turbidity and reduced bank vegetation can increase water temperatures, nutrients can increase productivity of aquatic ecosystems, and sedimentation can alter channel morphology and habitat availability for aquatic organisms. All of these alterations can affect the quality and availability of downstream human water supplies.

Elk stand in the Bitterroot River, Montana, USA, as the water reflects fire from the mountainside behind them. Wildland fires can have substantial impacts on surface waterways.

Post-fire run-off and erosion Severe burns remove aboveground organic matter, exposing bare soil to the effects of water and gravity at spatial scales ranging from individual raindrops to entire slopes. After fire, nutrients bound up in ash deposits, mineral sediment, and coarse woody debris are available for transport by subsequent wind, rainfall, or snowmelt. General types of water-driven erosion include sheet, rill, and mass wasting events (Roose 1996).83 Sheet erosion is rainfall-driven and occurs across broad soil surfaces. The Universal Soil Loss Equation (USLE, Roose 1996) has been used worldwide to predict potential soil loss to sheet erosion (mass/area, A):

A = R · K · LS · C · P

John McColgan, USFS

83

To be clear, erosion doesn’t depend on water. Dry ravel refers to gravity-driven transport—rolling, bouncing, sliding—that can be initiated when fire burns away vegetation that was holding particles onto a slope (Gabet 2003).

(7.1)

in which relevant factors for post-fire erosion include R, a rainfall factor for

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100  Ecology of Fire-Dependent Ecosystems

Christoph Langhans, with permission

Figure 7.1: Two views of erosion channels created by hillslope debris flow in southeastern Australia. (L) Deep channel incision on severely-burned slopes. (R) Multiple, long channels incised into severely burned slopes under Eucalyptus forest. 84

C and P represent crop cover and mitigation practices, respectively, reflecting the agricultural context for which the USLE was developed.

85

While sheet erosion typically does not remove the entire topsoil layer and rills are fairly isolated, the widespread loss of topsoil caused by mass wasting is a major impediment to post-fire recovery of soil organisms and vegetation.

the kinetic energy of falling precipitation; K , the soil erodibility factor; and LS , the slope length factor.84 Uninterrupted water accelerates downslope. Post-fire erosion is the product of fire regime, precipitation regime, and geologic-hydrological context of the burned area (see comprehensive review by Moody et al. 2013). Once fire passes, precipitation, R, is the primary driver of erosion. Raindrops themselves have sufficient energy to detach particles and make them available for transport (Salles et al. 2000). The geologic-hydrological context is defined by slope (S ), soil hydraulic properties, soil and sediment erodibility (K ), and sediment supply (Eq. 7.1). Generally, the effect of slope is driven by gravity, which contributes to whether water infiltrates into—or runs over the top of—the soil surface, and affects the size of sediments that detach and how far they move. More specifically, slope effects depend on soil erodibility (K ), which is determined by soil texture and organic matter content; thus, fire effects on these physical properties scale up to slope stability. Rills are narrow surface channels caused by linear water flow; channels that exceed 50 cm deep are called gullies (Roose 1996). Mass-wasting events are extreme forms of erosion in which broad areas erode deeply beyond the soil surface.85 Mass-wasting can include landslides and hillside debris flows, in which water and sediment push lobes of rocks, logs, and other coarse particles (Langhans et al. 2017; Fig. 7.1). Parise & Cannon (2012) summarise three possible wildfire-related effects that can trigger landslides: (1) Soil moisture build-up due to reduced interception and transpiration after canopy cover burns; (2) Reduced soil cohesion after wildfire consumes or weakens roots that otherwise held soil on steep slopes; and (3) Stream bank failure as banks erode due to greater post-fire streamflow. Erosion risk is neither necessarily greatest immediately after a burn, nor an inevitable outcome of wildland fire. Ash can absorb both energy from raindrops and a substantial amount of precipitation, deferring erosion risk until after the ash layer has disintegrated (Woods & Balfour 2008). The loss of soil cohesion from root breakage can be delayed for years until dead roots decompose, or it might not occur at all—grassland soils in South Africa

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demonstrated greater shear strength and resistance to erosion after fire (Bijker et al. 2001). Elsewhere in South Africa, native fynbos vegetation resisted run-off, while run-off from forestry plantations increased substantially (Scott 1993). Addressing concerns about erosion and hazardous fuel management, Harrison et al. (2016) report that patchy prescribed burning can effectively reduce fuel loads and mitigate erosion in steep mountain forests.

Fire effects on aquatic ecosystems Fire effects on aquatic ecosystems can be long-lasting. Sedimentation and elevated nutrient loads are due to the post-fire erosion and run-off discussed above, the magnitude and nature of which vary among responses and fire severity. Rhoades et al. (2019) described elevated nutrient levels in streams after a severe wildfire in Colorado, USA, that persisted for 14 years. But in that time, responses varied: nitrogen increases were associated with severely burned catchments, while carbon remained elevated in catchments that burned to lesser extents (Rhoades et al. 2019). Elevated stream temperatures can follow wildland fire. Whether combustion directly heats water is unclear,86 but streams that drain burned catchments can be 2–6◦ C warmer after fire (e.g., Sestrich et al. 2011). Stream temperatures can remain elevated for several years after a catchment burns, likely returning to pre-fire levels only once the vegetation canopy recovers. In western North America, daily maximum stream temperatures remained elevated seven years after catchments burned (Mahlum et al. 2011, Wagner et al. 2014). Elevated post-fire stream temperatures are primarily attributed to increased solar radiation in the absence of shade from bank vegetation (Royer & Minshall 1997, Williams Subiza & Brand 2018). Warmer temperatures appear localised to reaches within burned areas. Mahlum et al. (2011) found no difference in stream temperature fluctuations between unburned sites and samples taken 2 km downstream of burned areas. Analysis of long-term data from the Pacific Northwest, USA, found no extreme increases in daily maximum or mean temperatures across all monitored streams; instead, wildfire increased the frequency of relatively warm days over cool days (Koontz et al. 2018). The duration and intensity of these effects might suggest potentially devastating impacts to aquatic biodiversity. But in fact, several studies describe resilient populations throughout aquatic food webs. Streams in Patagonia showed no substantial changes in aquatic invertebrates despite higher sediment loads and reduced canopy shading after a wildfire (Williams Subiza & Brand 2018). Silins et al. (2014) report elevated phosphorous levels as long as five years after a wildfire in western Canada, to which they attribute sustained increases in ecosystem productivity (Fig. 7.2). A model created for wildfire impacts on stream habitat in the Northwestern US suggested a net positive impact on spring Chinook salmon, and current distributions of the fish support the model’s conclusions (Flitcroft et al. 2016). Native trout populations recovered so rapidly after a wildfire in western Montana that researchers suggested a positive effect of wildfire

86

Take, for example, two studies based on data from Montana, USA: While Hitt (2003) found an immediate increase of nearly 8◦ C during a single wildfire event, Mahlum et al. (2011) did not record immediate spikes while wildfires burned. Conversely, in northern California, maximum daily water temperatures were nearly 1◦ C cooler when smoke blocked solar radiation (David et al. 2018).

&KORURSK\OODµJ FP









,QYHUWHEUDWHVFRXQW P







7URXWZHLJKWJ







7UHDWPHQW 8QEXUQHG

%XUQHG Data: Silins et al. (2014)

Figure 7.2: Aquatic ecosystem productivity increased in catchments burned by wildfire in the Rocky Mountains of Alberta, Canada, relative to unburned catchments.

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102  Ecology of Fire-Dependent Ecosystems

on native fishes; meanwhile, an exotic species of management concern did not respond well to post-fire conditions (Sestrich et al. 2011). The effects of wildfire on aquatic macroinvertebrates vary with the severity and spatial extent of burns upstream. Negative post-fire effects are unlikely for larger streams, while smaller streams (first–fourth order) typically recover in a few years; those draining catchments in which greater than 50% burned with high severity might require 5–10 years (Minshall 2003). During the recovery period, the composition of macroinvertebrate communities might change, but overall species richness and abundance are typically unaffected by fire in the catchment (Minshall 2003).

Fire effects on water quality and supply Many urban areas draw their water from forested catchments, specifically, to take advantage of ecosystem services that provide clean, reliable water supplies. As such, there is concern about the safety and reliability of water supplies when source catchments burn (Bladon et al. 2014, Hallema et al. 2018). Elevated nutrient levels can persist downstream and put greater demands on water treatment prior to distribution for as long as 15 years after the fire (Emelko et al. 2011, Murphy et al. 2015, Hohner et al. 2019). Reviewing studies on potential contaminants in drinking water supplies carried downstream after wildfires, Smith et al. (2011) found high variability in transport loads—sediment loads were 1–1459 times above unburned levels, and nutrient loads ranged from 0.3–431 times greater—and concluded that the highest levels posed meaningful threats to drinking water quality.

A. Larson, US NPS

Figure 7.3: Small soil slumps and a trickle of water-borne silt were the result of thaws in the permafrost after the 2018 Andrew Creek Fire in the Yukon-Charley Rivers National Preserve, Alaska, USA.

87

Fort Collins, Colorado, USA, temporarily drew water from a reservoir above the city after post-fire run-off compromised water quality of the river providing their primary source (Writer et al. 2014). Of course, such a strategy requires more than one source of drinking water, which not every municipality has.

Variability in sediment and nutrient loads can be explained by precipitation patterns. In a study tracking run-off over the first year after a fire, Lane et al. (2006) attributed 76% and 79% of total suspended solids in two Australian creeks to single rainfall events. This association underscores the importance of rainfall and run-off in determining outcomes of fire events, as how post-fire erosion control can mitigate fire effects (Fig. 7.3). However, over longer time scales, post-fire run-off might make less of a contribution than other geomorphological processes—elsewhere in Australia, Tomkins et al. (2007) attributed just 5% of sediment load over 40 years to wildfire. Water availability is also a concern. Impacts on water supplies span multiple time scales (Martin 2016, Table 1). During fire events, water supplies can be compromised by damaged or inaccessible infrastructure. In the short-term, degraded water quality might require municipalities to temporarily avoid water from fire-affected sources, especially after pulses of high rainfall.87 And in the long-term, sustained sedimentation after repeated fires and run-off events can reduce reservoir capacity. Wildland fire could also stabilise water supply. Burned catchments delivered 1.2–2.0 times more water than unburned catchments in five years after fires in Alberta, Canada (Mahat et al. 2016). In the western US, the effect of fire might account for as much as 20% of stream yield from burned catchments (Wine et al. 2018). Such evidence suggests that maintaining fire regimes could mitigate negative impacts to water supplies anticipated

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by some climate change scenarios. Likewise, a study from Italy demonstrated higher water table levels after a pine plantation burned and a native scrub vegetation type established (Giambastiani et al. 2018).88

AIR, WEATHER, AND CLIMATE In considering fire effects on the atmosphere, we focus here on short-term impacts such as altered surface albedo and emissions of airborne chemicals and particles (aerosols, greenhouse gases, and particulate matter). These are the effects that are realised on human time scales and interact with human management and how humans use ecosystems. We begin with short-term, local impacts—those that immediately affect air quality and weather patterns—and scale up to global impacts of wildland fire.

88

This is, of course, a single case that might better serve the debate over woody plants and groundwater depletion (e.g., Acharya et al. 2018). At the same time, it suggests that prescribed burning to control afforestation might benefit water supply if erosion can be mitigated (e.g., pre-burn fuel treatments; Harrison et al. 2016).

The effect of wildland fire on the atmosphere is controlled by the mechanisms that comprise Earth’s energy balance. All incoming solar radiation, or insolation, can be accounted for as outgoing radiation (Fig. 7.4). How and where energy is returned to space determines how that energy affects the Earth’s system. For example, 25% of incoming energy is reflected by clouds in the upper atmosphere, and thus contributes little to warming. But energy that is absorbed by the surface and held in the lower atmosphere by clouds contributes to warming (e.g., the greenhouse effect).

One must distinguish between the nature of energy coming in to the Earth system versus that going out because they travel at different wavelengths. Most solar energy comes in at wavelengths in the visible spectrum, while outgoing radiation (that which has been absorbed by the surface, and not reflected back as sunlight) typically has longer wavelengths in the infrared range. Airborne particles—e.g., water droplets in clouds, and particulate matter in smoke—often react differently to energy at different wavelengths. For example, clouds can be opaque to energy that comes in as visible light; thus, 25% of insolation is reflected back into space before it has the

Figure 7.4: Earth’s energy balance, redrawn from Schneider (1987). All insolation, or incoming solar radiation, is matched by outgoing radiation: In the figure, 100 units of solar energy comes in, and 100 units go out. Arrows and numerals in parentheses indicate the direction and proportion of each mechanism.

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104  Ecology of Fire-Dependent Ecosystems

chance to heat the atmosphere or the surface (Fig. 7.4). At the same time, clouds are transparent to infrared radiation, which allows heat to escape and causes nighttime cooling at the Earth’s surface. Albedo and radiative forcing are important to Earth’s energy balance. Albedo is reflectivity of a surface, measured by how much radiation that strikes the surface is reflected back: 0 = total absorption, 1 = total reflectance. Earth’s albedo averages about 0.3, meaning 30% of incoming solar radiation is reflected back into space and does not transfer energy to the Earth system (Fig. 7.4). Radiative forcing measures the difference between absorbed and reflected radiation. Increased radiative forcing—e.g., lower albedo—means the Earth absorbs more solar energy.

24−hr PM2.5 levels (µg/m3)

Fire effects on air quality Perhaps the most immediate and certainly most apparent atmospheric impact of fire is degraded air quality due to smoke. Smoke from wildland fires is comprised of a number of chemicals—chiefly, carbon monoxide (CO), carbon dioxide (CO2 ), methane (CH4 ), as well as other hydrocarbons—in addition to particulate matter (Ward & Hardy 1991, Naeher et al. 2007).

80 60 40 20 Yes

No

Fire event?

Data: Liu et al. (2015)

Figure 7.5: Wildland fires increase airborne PM2.5 concentrations above days without fire events, and above US Environmental Protection Agency guidelines for safe air quality (broken line). Mean values and 95% confidence intervals calculated from 14 studies reviewed by Liu et al. (2015).

From a human health standpoint, the primary concern in wildland fire smoke is particulate matter (Reisen & Brown 2009, Horsley et al. 2018). The size class known as PM2.5 —particulate matter less than 2.5 microns in diameter—are particularly hazardous. PM2.5 particles are small enough to travel deep into human airways, where they remain lodged: 96% of retained particulate matter in human lungs is PM2.5 (Churg & Brauer 1997). Airborne PM2.5 concentrations are widely associated with unhealthy levels in areas proximate to wildland fires (Fig. 7.5), including populated areas where agricultural fields are burned (Ryu et al. 2007). Wildland fire smoke is unhealthy for humans and contributes disproportionately to deaths in less-developed tropical countries with greater annual biomass burning. Leading impacts to human health from smoke are primarily respiratory conditions, and to a lesser extent followed by cardiovascular conditions. In a review of physical health impacts from wildfire smoke exposure, Liu et al. (2014) found that 43 of 45 investigations of respiratory morbidity connected adverse health conditions to smoke, and 6 of 14 studies on cardiovascular morbidity associated health impairments to smoke. Worldwide, Johnston et al. (2012) attribute an average of 339,000 deaths annually to poor air pollution from the smoke of landscape-level fires, a category separate from cooking fires and waste disposal. Two major points stand out from their analysis: Firstly, citizens of tropical countries are at greater risk due to higher levels of open biomass burning. Secondly, the ˜ and La Nina ˜ years, number of deaths varies substantially between El Nino with the former driving up to 532,000 human deaths per year due to generally drier conditions and greater area burned in the tropics.

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The air in and around urban areas often becomes polluted when wildlands burn nearby.89 In Brisbane, Australia, air pollutant levels increased 20%–430% during periods of active burning in the region (He et al. 2016). In Sydney, Australia, local wildland fires were the cause of 94% of periods with very high air pollution for which a cause was determined between 1994 and 2007 (Johnston et al. 2011). Sydney’s pollution events have been associated with human fatalities (Horsley et al. 2018).

89

Air quality also declines substantially in the proximity of fires, where human impacts are primarily confined to fire fighters. Wildland firefighters are exposed to particulate matter from both the fire itself and internal combustion engines used in fire-fighting operations (Naeher et al. 2007).

There is debate about pollution differences between wildfires and prescribed burns. On one hand, prescribed burns do make meaningful contributions to local air pollution (Reisen et al. 2013, Haikerwal et al. 2015). For example, He et al. (2016) found that 70% of high-pollution events in Brisbane were caused by prescribed burns. On the other hand, there is evidence that smoke from prescribed burns contains less of the constituents most problematic for human health, especially PM2.5 (Prunicki et al. 2019). Smoke regimes of prescribed burns and wildfires are inherently different (Williamson et al. 2016a). One one hand, prescribed burns can be conducted under favourable atmospheric conditions—principally, when convection and wind direction carry smoke away from sensitive areas (Di Virgilio et al. 2018). On the other hand, prescribed burns rarely reach wildfire intensity (e.g., Williamson et al. 2013), which limits smoke transport into the upper atmosphere for dilution and dispersal (Fig. 7.6). The ability to plan prescribed burns presents opportunity to inform nearby residents, allowing them opportunity to avoid exposure to smoke. Simply staying indoors during the peak hours of pollution can be sufficient to reduce smoke exposure risk (Reisen et al. 2013), which some sensitive sub-populations—such as ¨ those with asthma—are already likely to do (Kunzli et al. 2006). Reduced visibility is another concern with wildland smoke. This issue is especially problematic in the Southeastern US, where atmospheric humidity is prone to reacting with smoke from wildland fires to create superfog with liquid water densities as much as 17 times greater than smoke-free fog, which can reduce visibility to as low as 1–3 m (Achtemeier 2003, 2008). Conditions favourable to superfog can occur very late or very early in the day—outside of operational periods for wildland fire managers—from residual smoke trapped in low landscape features (Achtemeier et al. 1998). Superfog events over roadways have caused a substantial number of crashes and fatalities, one of the most notorious resulting in a 70-vehicle collision with five deaths in Florida (Collins et al. 2009).

Eric Neitzel, CC-BY-SA-3.0

Figure 7.6: The plume from the 2004 Willow Fire, Arizona, USA, lifted combustion products so high into the atmosphere as to create a pyrocumulus cloud.

Fire effects on weather Wildland fire can affect weather. Although the timeframes are not usually long—on the scale of days, weeks, and sometimes seasons—the magnitude of changes can be large. Most of these effects are driven by smoke in the atmosphere; as such, the effects can reach beyond the area burning.

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106  Ecology of Fire-Dependent Ecosystems

Effect

Cause

Mechanism

Surface cooling

Smoke in low–mid atmosphere

Suspended particles block light from reaching surface, essentially providing shade. Cooler temperatures reduce decomposition rates. Diffuse light under moderate smoke optimises photosynthesis, but heavy smoke blocks too much light. Aerosols absorb radiation and warm upper air layers. Stable atmosphere inhibits convection. Aerosols and other smoke-derived particles provide cloud condensation nuclei. Warm upper air layers stabilise atmosphere, inhibit convection, and reduce cloud formation. Aerosols borne aloft into the upper atmosphere scatter light and increase reflectivity of high-level clouds. Ecosystems with light soils (e.g., tropical sandy soils) can be more reflective after fire removes vegetation. In boreal forest, establishment of deciduous trees increases post-fire albedo over pre-burn coniferous forest. Black carbon particles in atmosphere and as cloud nuclei absorb solar radiation. Blackened post-fire land surfaces absorb solar radiation. Black carbon deposition on snow fields and sea ice.

C accumulation Altered productivity Fewer clouds

Smoke in cloudy sky

More clouds

Smoke in cloud-free sky

Less precipitation

Stable atmosphere

Global cooling

Increased cloud albedo Increased surface albedo

Global warming

Increased radiative forcing Reduced surface albedo

Table 7.1: Potential impacts of wildland fire on weather and climate summarised from Ward et al. (2012), Langmann et al. (2009), and Jacobson (2014).

90

Qu et al. (2018) describe a similar phenomenon in China, where airborne aerosols due to pollution stabilise the atmosphere and prevent mixing; further accumulation of pollutants in the lower atmosphere creates a positive feedback.

91

Smoke density is measured as aerosol optical depth, which is a ratio expressing how much radiation is transmitted through the atmosphere relative to the incidental radiation received.

Surface air temperatures Perhaps counter-intuitively, wildland fire can reduce local air temperatures. For example, while a fire burned in northern California, a nearby valley experienced daytime temperatures 15◦ C below normal for a week, and 5◦ C below normal for three weeks (Robock 1988). The lower air temperatures were attributed to an inversion that was strengthened by the accumulated smoke.90 Interestingly, night-time temperatures were not affected, consistent with the solar-terrestrial energy balance: smoke particles prevent incoming visible light from reaching the surface, but allow infrared radiation to pass from the surface and into space, resulting in net surface cooling. Surface cooling by smoke is not always localised. Weeks-long belownormal daytime surface air temperatures in the eastern and central US have been attributed to forest fires in Northwestern US and Quebec, Canada, respectively (Robock 1991, Pahlow et al. 2005). Our literature review found no evidence of wildland fire increasing local air temperature. Smoke can also alter plant community energy budgets by altering temperature and light availability. In some regions, cooling from fire-emitted aerosols can increase soil carbon stocks by slowing decomposition (Landry et al. 2017). During a forest fire in British Columbia, Canada, photosynthesis increased under the diffuse light of moderate smoke, but declined under heavy smoke (McKendry et al. 2019).91

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Cloud formation Wildland fire can affect weather by altering cloud formation through the injection of aerosols into the atmosphere. Cloud formation begins with cloud condensation nuclei, airborne particles around which water droplets condense, grow, and when heavy enough to overcome convective uplift, fall as precipitation. Wildland fire emissions borne aloft—aerosols and black carbon particles—can also serve as substrate for water droplet and ice particle formation (Kireeva et al. 2009, Koren et al. 2008). Fire-generated aerosols can either facilitate or inhibit cloud formation depending on whether clouds are absent or already present. When clouds have yet to form, aerosols can act as cloud condensation nuclei; when clouds are present, aerosols can absorb solar radiation and stabilise the atmosphere, preventing the convection that would otherwise transport moisture into the atmosphere (Koren et al. 2008). Cloud reduction by smoke from widespread vegetation combustion—referred to in the atmospheric literature as “biomass burning”—has been documented in the Amazon Basin (Fig. 7.7). Similar processes can modulate lightning activity, which depends on convective clouds (Altaratz et al. 2010).

Smoke heating? Without

40 30 20 10 0

40

Cloud cover (%)

Height (x100 m)

With

30 20 10 0

0

2

4

6

Heating rate (K/day)

0.0

0.5

1.0

1.5

Smoke density (AOD)

Figure 7.7: Aerosols from biomass burning can inhibit cloud formation. These data are from studies in the Amazon. (L) When aerosols absorb energy at the top of the boundary layer—the lowest part of the atmosphere in contact with the Earth’s surface (Stull 1988)—they warm and stabilise the air column, preventing convection (model results from Feingold et al. 2005). (R) Cloud coverage declines with greater smoke density (measured by satellites as aerosol optical depth; Koren 2004). Black line is mean cloud fraction; grey lines denote error.

Data from L: Feingold et al. (2005); R: Koren (2004)

Climate-fire interactions By altering cloud formation and atmospheric moisture, aerosols released to the atmosphere by wildland fires can affect precipitation. Reduced precipitation is attributed to inhibited cloud formation in the tropics (Thornhill et al. 2018); conversely, aerosols have also been shown to increase the intensity of individual rainfall events in many climate regions (Koren et al. 2012). Rosenfeld (1999) used satellite data to show that “convective tropical clouds infected by heavy smoke from forest fires” needed to build high enough for moisture to freeze before precipitation would fall, while nearby clouds in cleaner air precipitated without freezing. In the Amazon Basin, aerosols from vegetation combustion are associated with more severe

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drought during dry seasons (Bevan et al. 2009). In Southern Africa, dry season rainfall has decreased 25–30% since 1950, which Hodnebrog et al. (2016) attribute to biomass burning in the region. Aerosols from biomass burning have also been shown to inhibit snowfall in the North American Rocky Mountains and the Himalayas (Borys et al. 2003, Bali et al. 2017).

500 hPa anomaly (dam)

Hectares burned (millions)

4

1.5

0

1.0 0.5

−4 t t hes wes Hig Lo

Fire activity

0.0

e e itiv ativ Pos Neg

Anomaly sign Data: Skinner et al. (1999)

Figure 7.8: Whether atmospheric anomalies are positive or negative correlates with how much some regions of Canada burn. (L) Positive anomalies occurred during seasons with the highest burned areas, while seasons with negative anomalies had the lowest burned areas. (R) Over all fire seasons, positive anomalies didn’t mean large areas always burned, but large areas never burned under negative anomalies.

92

Biomass burning also affects long-term climate dynamics by releasing substantial amounts of carbon dioxide, an important greenhouse gas. In terms of long-term soil-plant-atmosphere carbon pools, one must distinguish grassland/savanna fires, agricultural burning, and “temporary” forest burning from “permanent” forest burning (land clearing and deforestation) because carbon losses from permanent burning will not be offset by subsequent regrowth (Jacobson 2004).

Other atmospheric processes drive regional and seasonal patterns in wildland fire. Upper atmospheric flow affects moisture distribution, and periodic latitudinal shifts in these air streams over the oceans, known as oscillations, bring rain in some years to some regions, and drought in others. Tropical air is generally warm and wet; polar air is cool and dry. Mid-latitude differences in atmospheric pressure affect how far from the equator tropical air streams oscillate, and thus determine whether different land regions ˜ phase receive wet or dry conditions. For example, in Indonesia, the El Nino ˜ Southern Oscillation brings drought and increases wildfire of the El Nino ˜ phase tends activity (Field et al. 2016), while in Florida, USA, the El Nino ˜ to have higher rainfall, less lightning, and overall less fire; it is the La Nina phase that brings drought and fire to the Everglades (Beckage et al. 2003). Atmospheric pressure can also affect fire activity at continental scales. Atmospheric pressure conditions are referred to as anomalies, which index how much the condition deviates from the long-term average. Atmospheric scientists describe pressure anomalies as the height above the Earth’s surface they observe a specific pressure measurement (hectopascals, hPa, or millibars, mb). For example, under a high pressure ridge, the 500 hPa layer will be pushed closer to the Earth’s surface (below average, or negative anomaly), while under a low pressure trough, the 500 hPa layer will rise higher in the atmosphere (above average, or positive anomaly). In a comparison of atmospheric pressure anomalies and wildfires in Canada between 1953 and 1995, Skinner et al. (1999) found that most years with the highest fire activity also had a high-pressure ridge (positive anomaly) in northwest Canada, and up to a million more hectares per year burned under the influence of the high-pressure ridge (Fig. 7.8). Wildland fires burning at regional and seasonal scales can affect the global climate for decades. Fire affects global climate through radiative forcing—fire effects that alter the energy balance of the Earth system.92 For example, emissions from fire can affect whether the atmosphere reflects or absorbs incoming solar radiation, and the post-fire landscape can change albedo—whether the burned area reflects or absorbs incoming solar radiation that reaches the Earth’s surface (Langmann et al. 2009). Each response has different cooling or warming effects. Accounting for wildland fire in global change models is difficult because effects on radiative forcing vary among ecosystems and can change or even reverse over time. But at a global, long-term scale, the net effects of biomass burning likely generate warmer temperatures, because cloud absorption effects and the effects of aerosols on cloud formation (discussed above) outweigh the potential cooling effects of aerosols (Jacobson 2014). Below, we discuss the various effects of wildland fire on radiative forcing.

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Sub−Saharan Africa

0.02

0.010

0.01

0.005

0.00

Severity −0.01

High

Albedo change

Albedo change

Boreal Canada

Low

−0.02 1

2

3

4

5

6

Years since fire

7

0.000

Season

−0.005

Wet Dry

−0.010 8

0

1mo

6mo 1yr

2yr

Time since fire

5yr

Data from L: Jin et al. (2012); R: Saha et al. (2019)

Black carbon, or soot, carried into the atmosphere by smoke increases radiative forcing by absorbing energy in the top of the atmosphere (Ramanathan & Carmichael 2008).93 In a laboratory experiment, an air-black carbon mix absorbed nearly twice the radiation than clean air (Wang et al. 2018). Ramanathan & Carmichael (2008) describe two other pathways by which black carbon increases radiative forcing: Reducing cloud albedo by increasing energy absorption of water droplets that form around black carbon particles, and reducing surface albedo when airborne black carbon particles are deposited on snow and sea ice (Ward et al. 2012). Some aerosols reduce radiative forcing by increasing cloud albedo, and thus have a cooling effect. Precise estimates of wildland fire-derived aerosol effects are elusive, though, because different types of fires—i.e., flaming vs. smouldering combustion, fine herbaceous vs. coarse woody fuels—produce different ratios of gaseous emissions (Andreae & Merlet 2001). There is also considerable spatial variability, and global air circulation is critical to determining local effects of global biomass burning. For example, smoke observed to increase radiative forcing over one continent can originate from another continent (Landry et al. 2017).

Figure 7.9: Changes in surface albedo as ecosystems recover from fire. (L) In Canadian boreal forests, higher-severity fires cause greater albedo reductions immediately after fire, but eventually cause greater albedo increases as deciduous trees establish (Jin et al. 2012). (R) Surface albedo changes in sub-Saharan Africa above (broken lines) and below (solid lines) the equator during wet and dry seasons (Saha et al. 2019). 93

Not all soot is the same. Most black carbon in the northern hemisphere comes from fossil fuels, and from biomass in the southern hemisphere (Ramanathan & Carmichael 2008). Also, soot production varies by fire type, with nearly 10 times more black carbon being emitted from flaming combustion vs. smouldering fires (Conny & Slater 2002).

Finally, wildland fire affects radiative forcing by altering the albedo of the Earth’s surface, although the effects are sometimes counter-intuitive. In many instances, wildland fires decrease the albedo of burned areas, as the charred, ash-covered, and denuded interior of burn scars takes on a dark colour and absorbs incoming solar radiation (Fig. 7.9 L). But in other cases, fire can increase the albedo of burned areas. For example, in the Kalahari region of Southern Africa, bare sands reflect more light than the vegetation that covered them before fire (Saha et al. 2019; Fig. 7.9 R). Albedo responses in boreal forest are especially dynamic, varying over time and among fire severity and seasons. Temporal changes come in the shift in community composition following fire—early successional stages are characterised by deciduous trees (e.g., Populus spp.), and later stages

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110  Ecology of Fire-Dependent Ecosystems

Summer Shortwave albedo

Figure 7.10: Fire’s effect on albedo varies with burn severity and time-since-fire in Alaska’s boreal forest. High-severity fires increase albedo more than lower-severity burns, due to greater deciduous tree cover after burns.

Winter

0.55 0.50

0.14

0.45

0.13

0.40

0.12 10

20

30

40

50

10

Stand age (Years)

Burn severity

High

20

30

40

50

Low Data: Beck et al. (2011)

are dominated by conifers like spruce (Picea spp.) and fir (Abies spp.). During the growing season, deciduous leaves tend to reflect more light than spruce needles; in the winter, deciduous leaves fall away and expose highly-reflective snow surfaces otherwise shaded by conifers. Thus, despite initial reductions after fire burns through organic matter, springtime albedo can exceed pre-fire levels after just 5–7 years due to the establishment of deciduous trees that favour mineral soil (Jin et al. 2012). High-severity fires kill more conifers and burn more of the organic layer, exposing mineral soil to the benefit of deciduous trees. Thus, high-severity fires increase surface albedo for decades after fire (Fig. 7.10). But deciduous trees have greater transpiration rates, which might increase water vapor to the point that subsequently greater radiative forcing in the atmosphere offset the forcing reductions of greater albedo (Swann et al. 2010).

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8

Individuals and populations Direct and indirect impacts of fire on plants and animals; fire effects on population dynamics; variability due to fire regime components and robust approaches to population ecology

Fire affects organisms in many ways. Effects can be negative (e.g., mortality) or positive (e.g., fire-stimulated germination), depending on the characteristics of the fire and of the organism. Impacts that can affect an individual include the acute effects of heat and smoke from combustion as well as changes in the post-fire environment relative to pre-fire conditions. Because changes to survival, growth, and reproductive capacity of individuals mediate population dynamics, fire affects populations as well. Population-level effects often depend on the local fire regime. While a single fire can cause a population to fluctuate, long-term patterns in fire type, recurrence, and seasonality are more determinative of long-term population trends. However, substantial departures from historical ranges of variability can immediately affect species adapted to a particular fire regime. Here, we discuss how fire impacts individual plants and animals and how those impacts scale up to the population level. We identify variables relevant to population dynamics in the face of fire and introduce models appropriate for studying fire impacts at both individual and population levels.

DIRECT EFFECTS ON INDIVIDUALS Recall that direct fire effects are the immediate impacts on organisms that result from exposure to heat, flame, or smoke (Reinhardt et al. 2001b; Ch. 3). Direct effects differ across species and even among individuals within a species; these differences have important implications for population dynamics (Hoffmann 1999, Maret & Wilson 2000, Bond et al. 2012) and community assembly (e.g., Johnson & Abrahamson 1990, Jacobson et al. 1991, Gabrey et al. 2001, Taylor & Fox 2001).

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General considerations Thermal tolerance An organism’s ability to survive heat exposure depends upon several factors, beginning with the magnitude and duration of heat exposure (Stephan et al. 2010), or heat dose. Other factors include the amount of insulation the organism has, its body size, behaviour, and the extent to which injury to given tissues or organs results in the organism’s death.

94

See work by Doerr et al. (1970), Erwin & Stasiak (1979), Singer et al. (1989), Southgate & Masters (1996), Letnic (2003), Letnic et al. (2005), and Banks et al. (2011b). 95

Trout can withstand maximum water temperatures between 23 and 25◦ C (Cherry et al. 1977).

Plants, for example, can endure extensive heating and even survive the loss of above-ground tissues by regenerating from below-ground buds. While animals must avoid bodily injury, many hide or move to avoid exposure (Simons 1991). Some mammals use torpor to reduce metabolic demands both during and after fire (Matthews et al. 2017). Thus, direct mortality is often minimal for mobile organisms.94 Aquatic organisms also have low susceptibility to heat-related mortality. Recall from Chapter 7 that stream impacts are typically localised in burned reaches. For example, peak stream temperature during a fire was 17.2◦ C versus 7.8◦ C in unburned areas (Hitt 2003), which did not exceed the thermal tolerance for fishes in the watershed.95 Meanwhile, Mahlum et al. (2011) found no immediate temperature change in streams during a severe wildfire in the northern Rocky Mountains, USA.

Smoke effects Toxic gasses in smoke can be detrimental to wildlife, although few studies have quantified these effects. Bornean orangutans Pongo pygmaeus wurmbii are harmed by wildfire smoke (Erb et al. 2018), which can also affect aquatic organisms. Water ammonium levels during a fire up to 40 times greater than before the fire were attributed to water uptake of smoke-borne gasses (Spencer & Hauer 1991). High ammonium levels can cause fish and amphibian mortality (Cushing & Olson 1963, Pilliod et al. 2003). Smoke effects are not all negative. Smoke induces germination in many plant species (Keeley & Zedler 1998, Keeley et al. 1985, Nelson et al. 2012, Downes et al. 2010, Wicklow 1977). A compound, Karrikinolide, is the main germination stimulant found in smoke (Flematti et al. 2004). Karrikins are deposited on soil from smoke and, together with triggers such as litter reduction and increased light, stimulate germination (Dixon et al. 2009). Some species do not respond to karrikins alone (Downes et al. 2010) and require another compound, cyanohydrin glyceronitrile to germinate (Flematti et al. 2011). Karrikins have also been found to result from biomass decomposition and plant metabolism and, therefore, are not unique to smoke (Chiwocha et al. 2009). Many plants, including many not found in fire-prone environments respond to karrikins.

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Plant-specific responses The effect of fire on a plant depends on the characteristics of both the fire and the plant. Generally, at least some above-ground plant tissue is scorched or consumed by a flame front. The extent of damage depends on fire intensity and heating duration, the plant’s resprouting capacity (Fig. 8.1), and how well-protected meristematic and vascular tissue are from heat exposure (Stephan et al. 2010, Reinhardt & Dickinson 2010).

Grasses and forbs Typically, the entire above-ground portion of grasses and forbs are consumed by a flaming front, but is replaced by regrowth from below-ground meristematic tissues that are insulated by soil (Bradstock et al. 1995, Klimeˇsova´ & Klimeˇs 2007). For grasses, up to 99% of above-ground postfire regrowth is attributable to below-ground buds, and only 1% from seed (Benson & Hartnett 2006; Fig. 8.2). Some fire-induced mortality does occur. Sensitivity to fire is species´ et al. 1996, Gosz & Gosz 1996) and differences have been specific (Boo attributed to amount and location of meristematic tissue (Benson et al. 2004, Russell et al. 2015), activation pattern of dormant meristematic tissue (Lehtila¨ 2000, Russell et al. 2015),96 and amount and distribution of ´ et al. 1996).97 senescent material (Boo Heat exposure drives mortality (Cable 1965, Gosz & Gosz 1996), and depends on both biotic and abiotic conditions. Among six grasses in an Argentinian grassland, mortality was highest for those with the most standing dead biomass, suggesting that flammable dead biomass around plant ´ et al. 1996). Mortality also bases increases heat exposure to buds (Boo varied with season, ranging from 12 to 86% after a dormant season fire, but 1g, with lower mortality rates for larger seeds. Larger seeds contain more resources for rapid taproot development (Hoffmann 2000; Fig. 8.3). Deep taproots increase access to water and nutrients and enhance carbohydrate storage.

Growth and damage Fire typically consumes or damages at least some plant tissues. The nature of damage depends on factors such as the intensity and duration of heat exposure, plant type and size,98 and the degree of protection the plant has from heat (Stephan et al. 2010). Post-fire growth is also modulated by changes in soil texture and bulk

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density (Kennard & Gholz 2001), porosity and infiltration (Ulery & Graham 1993), and nutrient availability (DeBano et al. 1977, Kennard & Gholz 2001, Valor et al. 2013). Trees often experience reduction in growth immediately after fire if the fire is severe and scorch is high (Botelho et al. 1998, Busse et al. 2000, Valor et al. 2013), but no change in growth rate for lowseverity fire (Lilieholm & Hu 1987, Valor et al. 2013). However, an increase in growth rate relative to unburnt forests can result over the long term from decreases in competition from other trees (Valor et al. 2013). Plant growth responses to fire are species- and site-specific. Contrary to the patterns above, Kennard & Gholz (2001) found greater tree seedling growth following high intensity fire in a tropical dry forest in Bolivia. They attributed this to greater nutrient availability post-fire, as they did not observe the trend in low-severity burns with lower post-fire nutrient loads.

Interactions Interactions between fire and other stressors can modulate fire effects on individual organisms. Fire can interact with drought to increase plant mortality rates above those observed for either disturbance alone (Bigler et al. 2005, Moser et al. 2010, Brando et al. 2014, Harvey et al. 2016, Twidwell et al. 2016b). Fire interacts with other stressors as well. Fire can also increase susceptibility of trees to insects (Geiszler et al. 1984). Bark beetles, specifically, are attracted to fire and readily locate burnt stands (Muona & Rutanen 1994, Hart 1998). Thus, more bark beetles occurred after low-intensity prescribed fire in old growth forest in Minnesota, USA, than at unburned sites (Santoro et al. 2001). Even trees with no crown scorch were attacked by beetles at their scorched bases. Within a year, half of the beetle-attacked trees died and the rest sustained damage and bark loss, making them more susceptible to future fire (Fig. 8.4).

C. Bell, US NPS

Figure 8.4: Bark beetles can partially girdle trees and damage bark, increasing tree vulnerability to fire.

Animal-specific responses Animals can be killed or injured from direct contact with flames or exposure to high temperature or smoke. While many studies have reported some animal kill during fire (e.g., Tevis 1956, Howard et al. 1959, Bingham 1965, Erwin & Stasiak 1979), the severity of these impacts depends on the size, mobility, and life history of the species. Highly mobile species like birds and large mammals typically do not experience high mortality rates. For instance, after the 1988 Yellowstone fires, bears, elk, moose, and other large mammals were found dead, but dead animals only constituted 1% of the total pre-fire abundance (Singer et al. 1989). Similarly, a systematic search post-wildfire in Alberta, Canada turned up no dead ruffed grouse (Doerr et al. 1970). Less mobile species have higher rates of mortality from fire. Insect abundance (especially for species with limited flight distances) is often much lower immediately after fire (Rice 1932, Bulan & Barrett 1971, Morris 1975, Hansen 1986, Anderson et al. 1989, Paquin & Coderre 1997, Siemann et al. 1997). Animals are particularly vulnerable to fire during less mobile life

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stages; take, for example, high mortality of ground-nesting bird nestlings and fledglings after prescribed fire in North Dakota, USA (Kruse & Piehl 1984). However, 69% of nests survived the fires because of patchy fire spread, suggesting the amount of unburnt refugia within a fire perimeter is an important determinant of mortality for less mobile species. 99

In an interesting case of animals surviving fire via refugia, Brennan et al. (2011) found that highly flammable grass trees actually served as refugia for insects. While high temperatures at the base of the grass tree killed many insects, those in the crown experienced little mortality because the maximum temperatures there were far lower (25◦ C vs. 225◦ C).

Animals use other types of refugia, as well.99 Many small mammals avoid injury by retreating to burrows (e.g., Hedlund & Rickard 1981, Geluso & Bragg 1986). Amphibians and reptiles avoid a large amount of direct fireinduced mortality because many inhabit wet areas which can serve as a refuge during fire (Komarek 1969, Vogl 1973) and their small size means they can burrow or go into burrows of other species to avoid fire (Babbitt & Babbitt 1951, Folk & Bales 1982). Amphibians and reptiles in arid areas might be more at risk. Some can scoot to areas of lower fuels within the fire perimeter, but surviving unscathed is not guaranteed (Fig. 8.5).100

POPULATION-LEVEL IMPACTS

Abu Shawka CC SA 3.0

Figure 8.5: An angulate tortoise Chersina angulata damaged by a fynbos fire. Angulate tortoises are capable of scooting to nearby areas of lower fuel within burn perimeters (Table 5.5), but can still sustain damage.

Fire affects populations by altering population vital rates—the relative frequencies of the events that mediate population size and structure (Caswell 1986), such as establishment, growth, and reproduction. This occurs when fire changes something about the individuals in a population (e.g., causes mortality or stimulates flowering). It also occurs when the fire impacts another population that those individuals interact with (e.g., decreases prey abundance or attracts predators to the area; Fox 1982, Green & Sanecki 2006) or affects abiotic components of their environment (e.g., causes sedimentation of streams or increases solar radiation; Bozek & Young 1994, Burton 2005). Fire-induced mortality occurs in both plants and animals. However, whether individual losses to mortality alter population dynamics depends on the life history stage of those lost, and whether the rate of replacing individuals at affected life history stages is altered, as well.

100

In desert areas of Arizona and Utah, USA, inspection of dead desert tortoises, snakes, and lizards following wildfires revealed that many were killed by heat and flame exposure (Esque et al. 2003).

Plant population dynamics Population dynamics are determined by dispersal, mortality, growth, reproduction, and establishment (Henriques & Hay 2002). Changes in the overall population size over time results from changes in these vital rates at the population level (Caswell 1986). Furthermore, fire effects can either be additive or mitigating (Warchola et al. 2018).

Mortality Although fire causes mortality of adult serotinous trees, fire is necessary for reproduction in pyriscent species (Fig. 8.6) and enhances seedling establishment by providing suitable habitat for seedling germination and

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growth (Causley et al. 2016, Perry & Lotan 1979). Thus, the negative effect—the loss of mature individuals—is offset by a large influx of seedlings, and the population is stable over time under certain fire regimes. Similarly, even though fire increased grass mortality rates by 42% in a Namibian semi-arid savanna (Zimmermann et al. 2010), the reduction in population size (and attendant competition) increased individual survival rate and productivity during subsequent years, which offset fire-induced mortality.

Dormancy is a common strategy to reduce mortality risk during fire. In plants, photosynthetically active cells contain more water than dormant cells and are thus more susceptible to ruptured cell walls as water expands due to freezing or rapid heating. For example, high mortality in prickly pear cactus Opuntia phaeacantha follows burns during the growing season, when cells are photosynthetically-active and full of water (Fig. 8.7).

Reproduction While plants demonstrate a variety of reproductive strategies—seeding vs. vegetative, asexual vs. sexual—fire effects on reproductive rates depend heavily on whether reproduction consists of dispersal away from the parent or resprouting from the roots. In the latter case, new stems grow from meristematic tissues located in lateral roots (in woody plants, these are often called root suckers; Moreira 2000). Seedlings produced through sexual reproduction and root suckers often have different responses to fire because root suckers are not entirely dependent upon their own capacity to store carbohydrates, nutrients, and water (Hoffmann 1998). Often, offspring produced through sexual reproduction grow more slowly than vegetatively produced individuals (Abrahamson 1980, Hoffmann & Moreira 2002). The large size root suckers are able to attain from faster growth relative to sexually produced seedlings confers increased tolerance to future fire as well (Hoffmann 1998).

Josef Tal CC BY-SA 3.0

Figure 8.6: Pyriscent cones will not germinate without fire, and adults are fire-sensitive, so adult mortality is necessary for regeneration. The image shows the serotinous cone of Pinus halepensis, a conifer from the Mediterranean region of Europe.

100 Summer

Mortality (%)

The loss of aboveground tissue is considered negative growth—as growth is measured as an increase in size during a specified interval of time, a decrease in size constitutes negative growth (Hoffmann & Moreira 2002). This has important consequences not only for average population growth rates, but also survival in the face of subsequent stressors and reproduction. Within a species, fecundity—the number of seeds produced by an individual—increases with increasing plant size (Aarssen & Taylor 1992).

75 50

Winter

25 0

0

1

Years since burn

3

Data: Ansley & Castellano (2007)

Figure 8.7: Prickly pear cactus Opuntia phaeacantha mortality is greater after summer vs. winter burns (solid vs. broken lines, respectively). Shading denotes spatial extent of prickly pear clusters; darker lines are smaller clusters that burned with higher intensity due to more herbaceous fuels, while lighter lines denote larger clusters with less fuel.

Fire reduces the success of sexual reproduction for many plants. It reduces the size of adults, often leading to lower reproductive output; reproductive organs and seeds can also be destroyed by fire (Hoffmann 1998, Moreira 2000). However, fire-adaptive reproductive traits can mitigate these losses. The extent of fire effects on reproduction also depends on the fire regime. For instance, high fire frequencies in some fire-prone ecosystems limit plant sexual reproduction. For sexual reproduction to be successful in areas with short fire return intervals, plants must either be capable of resprouting or they must flower and produce seeds that germinate and either themselves reach reproductive age or at least reach an age class sufficient

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to survive the next fire before it comes. Plants in areas with frequent fires that do not resprout (obligate seeders) typically have fire-protected seeds (that often require fire to germinate; Causley et al. 2016). Others ensure rapid reproduction following fire through smoke-stimulated germination (Keeley & Zedler 1998) or fire-stimulated flowering (Coutinho 1990). Resprouting allows plants to avoid the risks associated with sexual reproduction in fire-prone environments. While fire return interval determines the size and structure of resprouting plants, they typically survive fire even where fire occurs fairly frequently (Higgins et al. 2007). Fire will consume above-ground biomass, but plants will grow to their previous stature with time. This cycle of top-kill and resprouting is called the fire trap (Waldrop et al. 1992). Resprouters remain in the fire trap until a fire-free period allows them to grow beyond the reach of flame.

Establishment Seedling establishment is a critical phase in plant life cycles (Silvertown et al. 1993). Fire can promote establishment by enhancing conditions for seedling growth. Fire lowers competition for water, light, and nutrients by reducing adult density (Christensen & Muller 1975, Hulbert 1988, Swank & Oechel 1991) and removing litter, which can inhibit germination and growth (Hulbert 1988, Bosy & Reader 1995, Foster 1999, Vellend et al. 2000, Jensen & Gutekunst 2003). But the full effect of fire on seedling establishment depends on the fire regime, the pre- and post-fire conditions, and the anatomical and physiological characteristics of the plant. Fire frequency plays an extremely important role in successful seedling establishment for obligate seeders because they are dependent on developing a mature and viable crop of seeds between fires. Obligate seeders in areas with high fire frequencies risk local extinction.

101

Pinus attanuata forms even-aged stands because all recruitment occurs during the previous fire, which kills all adults.

102

The risk that fire frequency will be too high for an obligate seeder to develop a sufficient cone crop between subsequent fires is termed immaturity risk.

For instance, fire killed all adults in a stand of knobcone pine Pinus attanuata, a pyriscent tree (Keeley et al. 1999). All were 8 years old at the time of the fire.101 Post-fire, seedlings grew rapidly and produced mature cones at age 4. Larger trees had more cones. While this represents successful reproduction despite frequent fires (2 fires in < 10 years), the authors note that the density of Pinus attanuata seedlings was substantially lower after the fire. Therefore, if fire frequency remains that high, the population could become locally extinct because regeneration does not keep pace with fireinduced mortality.102 More time between fires would allow trees to grow larger and produce larger crops of seedlings. Local extinction resulted from high fire frequency for another obligate seeder, Ceanothus oliganthus (Zedler et al. 1983). C. oliganthus was the second most abundant species in an area burned by wildfire in 1979; while adults were killed, seedlings emerged in high densities following the fire. Another fire burned part of the area affected by the 1979 fire in 1980. Seedling densities remained high in the area that only burned in 1979, but almost no seedlings remained in areas impacted by both fires (Zedler et al. 1983; Fig. 8.8). A few seedlings located in areas with little grass cover

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survived because the fire did not spread through those patches, but these were likely too few to avoid local extinction.

Resprout vigour is modulated by plant size when topkilled—large plants have higher growth rates than small plants (Canadell et al. 1991, Pausas 1997, Vesk 2006, Robertson & Hmielowski 2014). Resprouting plant growth rates follow a resprout curve: growth rates increase in the years following germination, but decrease over time because of changes in resource allocation from below- to above-ground and a reduction in the amount of meristematic tissue (Clark & Liming 1953, Vesk et al. 2004, Malanson & Trabaud 1988, Drewa et al. 2002). Since the size resprouting plants reach between fires depends on fire frequency, and the growth rate of resprouting plants depends on size but decreases over time, the biomass of resprouting plants tends to cycle around a certain maximum depending on the fire return interval (Hoffmann & Solbrig 2003, Grady & Hoffmann 2012).

5

●

ic

rn

ed

Tw

O nc

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●

Bu rn ed

Be

fo r

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Bu

Count per m2

Fire frequency plays a role in resprout establishment as well. Resprouting can deplete carbohydrate storage (Bowen & Pate 1993, Schutz et al. 2009). Although plants replenish depleted stores through photosynthesis, frequent fires provide less opportunity to replenish carbohydrates needed for subsequent resprouting. This can cause resprouter biomass to decrease over time (Bowen & Pate 1993, Wildy & Pate 2002). This reduction in biomass is often because of reduction in plant sizes, not decreases in density. In Kruger National Park, South Africa, large experimental units were treated with different fire frequencies for 40 years. Fire frequency did not affect the density of woody resprouting plants in these plots; increasing fire frequency was correlated with decreases in biomass and average size of individuals (Higgins et al. 2007).

●

10

Data: Zedler et al. (1983)

Figure 8.8: Density of adult Ceanothus oliganthus (black) before a wildfire and seedlings (grey) in areas burned by a single wildfire and those burned by two wildfires a year apart (1979 & 1980). Error bars represent 1 standard deviation.

Fire intensity mediates resprouting vigour. Flames > 2m long produced more resprouting woody plant top-kill and greater mortality than shorter flames (< 2m) in the Brazilian cerrado (Hoffmann & Solbrig 2003). Sim´ ilarly, Lloret & Lopez-Soria (1993) found a correlation between fire temperature (ranging from 50 to 750◦ C) and both mortality and resprouting biomass in a laboratory experiment. Variation in fire intensity within a fire due to local variation in fuels, topography, and microclimatic conditions leads to variation in post-fire resprouting vigour as well (Noble 1984; Fig. 8.9). Fire season also impacts resprouting. Top-kill in dormant, dry seasons (temperate and tropical regions, respectively) increases resprouting rates and resprout growth rates over top-kill during growing/wet seasons (Buell 1940, Clark & Liming 1953, Wenger 1953, Trapnell 1959, Geldenhuys 1977). This is likely related to carbohydrate availability, as plants shift allocation during the year. In temperate regions, plants allocate more to above-ground growth during the growing season and more to carbohydrate reserves during the dormant season (Kays & Canham 1991, Pelc et al. 2011). A similar pattern holds in the tropics: resources are allocated to above-ground growth during the wet season and below-ground during the dry season (Latt et al. 2001, Newell et al. 2002).

Carissa Wonkka

Figure 8.9: Resprouting shrubs burned during an experimental fire in Texas, USA show variation in resprouting vigour despite being of similar sizes. Temperature and flame-length recorded during the fires showed wide variation within burns (Twidwell et al. 2016b).

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103

Root reserves might have been lower earlier in the dry season because of allocation to above-ground biomass during the wet season.

Fire season and fire intensity are often related, making it difficult to distinguish the effects of higher intensities from those of physiological differences related to season (e.g., lower root reserves in dormant season). For instance, in a Northern Australian tropical savanna, both whole plant survival and resprout biomass were greater when resprouting woody plants burned in the early dry season versus the late dry season (Williams et al. 1999). But since the early fires were less intense than later ones (2200 vs. 7700 KWm-1 ), it is uncertain whether decreases in resprouting and survival were related to increased intensity, or lower root reserves.103 Pre- and post-fire environmental conditions affect seedling establishment. In South Africa’s Kruger National Park, the decreases in plant biomass and size associated with increasing fire frequency were modulated by site productivity. Less productive sites saw a steeper decline in biomass with increasing fire frequency (Higgins et al. 2007). Drought can also affect post-fire seedling establishment. For example, for most species in a subalpine forest in the Rocky Mountains, USA, seedling establishment declined with drought severity (Harvey et al. 2016). This relationship was not evident for all species though. Seedling establishment of several species was unrelated to drought severity. For the most part, these were known to be more drought tolerant (e.g., Pinus contorta was not af˜ & Sala 2000). fected by drought and it is extremely drought tolerant; Pinol Other plant characteristics can modulate post-fire drought effects on seedling establishment. Large-seeded Banksia spp. were less likely to perish during post-fire drought because the seedlings of large-seeded species were able to grow more quickly and develop larger root systems and could therefore access more soil moisture than other species (Lamont & Witkowski 1995).

Indirect effects on animal populations Similar to plant populations, animal population dynamics are driven by birth (fecundity), death (mortality), immigration, and emigration (Jackson 1939). Fire can affect these vital rates differently for different species, which depends on the characteristics of the species and the fire regime. We discuss some important indirect fire effects on different taxa below.

Aquatic animals While aquatic animal mortality from the direct effects of fire has been observed (Minshall et al. 1989, Cushing & Olson 1963), it does not necessarily impact population dynamics. Long-term vital rates often recover or even improve. For example, three years after fire caused population crashes of redband trout Oncorhynchus mykiss in burnt sections of the Boise River, USA, the fish were found in greater densities in those stretches of river than sections that had not burned (Rieman & McIntyre 1995). Similarly,

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post-fire declines in macroinvertebrate abundance rebounded after seven years, although responses were taxa-specific (Minshall et al. 2001). Fire effects on hydrological processes can impact aquatic animal populations (Bozek & Young 1994, Lyon & O’Connor 2008). Less vegetation in burnt areas can increase stream flow and sedimentation (Bozek & Young 1994) and potentially alter stream channels (Zelt & Wohl 2004, Dunham et al. 2007). Loss of vegetation along the banks of rivers and streams can temporarily increase water temperature (Amaranthus et al. 1989, Mahlum et al. 2011). Fire in forested areas alters inputs and transport of woody debris in streams and rivers (Young 1994, Zelt & Wohl 2004; Fig. 8.10). Indirect effects of fire on aquatic animals differs among taxa and species. Aquatic invertebrates tend to increase in burnt areas for the most part (Mellon et al. 2008, Andersen et al. 2005, Silins et al. 2014), although Minshall et al. (2001) found a decrease in macroinvertebrate abundance in burnt areas that rebounded within 7 years. Silins et al. (2014) attributed observed increases in macroinvertebrates to increases in algal biomass, which resulted when sediment increased phosphorus inputs in streams.

US NPS

Figure 8.10: Fire increased coarse woody debris in a stream in Rocky Mountain National Park, USA.

Most studies report changes in the relative abundance of invertebrates, with increases in species that have high dispersal rates, high fecundity, and short lifespans (Malison & Baxter 2010, Mihuc & Minshall 1995, Vieira et al. 2004, Mellon et al. 2008). The magnitude and duration of changes in aquatic invertebrate abundance depends on the stream size and vegetation recovery time (Mellon et al. 2008). Fish species also showed species-specific responses to fire. Most studies of fish response to fire show immediate post-fire declines (e.g., Rieman et al. 1997, Howell 2006, Burton 2005, Lyon & O’Connor 2008), but in many cases fish populations bounced back shortly after the initial population decline (Rieman et al. 1997, Howell 2006, Burton 2005). In all of these cases, habitat alteration was evident, but the effects on populations were ephemeral. For instance, Dunham et al. (2007) found an increase in maximum stream temperatures for up to 10 years following a large wildfire, but identified no associated effects on fish populations at that time. In other cases, fire had negative effects on fish populations. Sedimentation in Australian alpine streams led to 95–100% loss in fish abundance, and abundances of many species remained low for up to 36 months after the fire, although a few species showed some recovery by 24 months after fire (Lyon & O’Connor 2008). Fire return interval might play a role in recovery of fish populations. Fore instance, Whitney et al. (2015) found that 6 of 7 species of fish in the southwestern USA experienced population declines after two consecutive fires burned the same area. In addition, stream connectedness and post-fire conditions modulate fish response to fire-induced habitat alteration. Isolated populations of fish are more at risk than those that are connected to potential sources of immigration (Rieman et al. 1997, Propst et al. 1992). Flooding in areas that have been previously burned can cause additional die-offs as sediment is again carried into streams. This can lead to additional losses for populations, slowing their ability to rebound from initial die-offs (Bozek & Young 1994).

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Herpetofauna As discussed above, amphibians and reptiles avoid a large amount of direct fire-induced mortality (Komarek 1969, Vogl 1973, Folk & Bales 1982, Babbitt & Babbitt 1951). Direct, negative fire impacts to individuals can be offset by long-term indirect benefits from an open habitat (Wharton 1966, Cutter & Guyette 1994, Greenberg et al. 1994).

Jean and Fred CC BY 2.0

Figure 8.11: A lizard in John Forrest National Park, Australia, contrasts against a charred tree bole.

Reptile and amphibian populations often respond most to indirect fire effects, especially habitat alteration (Fox 1982). Immediately after fire, herpetofauna habitats often have higher temperatures and less moisture as a result of greater insolation—fire reduces canopy and litter, allowing more sunlight to reach the ground. The duration and extent of habitat alteration depends on the vegetation type and fire severity (Fig. 8.11). Herpetofaunal responses to fire-induced changes in habitat differ among species, depending on the physiology of the species and its habitat requirements (Greenberg & Waldrop 2008, Webb & Shine 2008, Fox 1982, Ferreira et al. 2016). Fire effects on reptiles and amphibian abundances range from no effect (e.g., Hossack et al. 2013, Hossack & Corn 2007), to increases (e.g., Rochester et al. 2010, Kahn 1960, Lillywhite & North 1974, Wilgers & Horne 2006, Hossack & Corn 2007, Kirkland et al. 1996) and declines (e.g., Hossack et al. 2013, Rochester et al. 2010, O’Donnell et al. 2015). Among species that initially decline, some rebound quickly (O’Donnell et al. 2015). Negative effects were most often observed in moist microhabitats, while increases in abundance were most often observed in populations of generalists and open-habitat specialists (Rochester et al. 2010). Habitat alteration can affect predation rates of reptiles and amphibians as well. Often, the open environment created by fire facilitates predators. For instance, Webb & Shine (2008) found differences in the survival of two snake species following fire. One species declined, while the other was unaffected. The one that declined was an active foraging species, more vulnerable to predation in the post-fire environment (which had an open canopy and reduced shrub layer), while the other was an ambush hunter, making it less vulnerable to increased predation. Post-fire changes in prey availability can also affect herpetofauna population dynamics. Kerby & Kats (1998) studied California newts Taricha torosa in the Santa Monica Mountains of California, USA following a wildfire. Winter rains following the wildfire washed soils from burnt slopes into the streams, carrying with them a large number of earthworms. Prior to the wildfire, cannibalism of larvae was high among adult newts. After the fire, there was no cannibalism for two years. Instead, the adult newts were eating earthworms. Neither the effects of reduced cannibalism on larvae survival or growth rates, nor its impact on overall newt population dynamics were studied. However, given the importance of predation to population dynamics (Volterra 1928), this unusual indirect fire-effect likely has at least a short-term impact on newt populations. While post-fire habitats can be detrimental to some amphibian and reptile

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populations, fire also maintains habitat for many species (e.g., Fig. 8.12). In pine forests in Florida, USA, there are some reptile species that are only found in open habitat (Greenberg et al. 1994). Early successional habitat is maintained by a combination of frequent small-scale fires and infrequent large-scale severe fires (Myers 1985). When this fire regime is not maintained, these species are no longer found in the resulting mature hardwood stands that replace the open pine forests (Greenberg et al. 1994). Similarly, fire can maintain the aquatic habitat necessary for amphibians and reptile breeding and overwintering (Wharton 1966, Cutter & Guyette 1994). For example, wetlands of coastal plains in southern USA are maintained by periodic fires (Frost 1995, Kirkman 1995, Kirkman et al. 2000). Without fire, organic material accumulations alter substrate conditions and allow shrubs to form a thicket. This can reduce wetland water levels (Kirkman 1995, Kirkman et al. 2000).

Jessica Bowen, US FWS

Figure 8.12: Alligators were found only on burnt shoreline following a wildfire in Florida, USA (Vogl 1973).

Terrestrial invertebrates The potential for insect mortality from fire is high (see section on direct fire effects), resulting in depressed population abundance immediately following fire. But many insect populations rebound quickly, although responses are highly species-specific (Bock & Bock 1991, Galley & Flowers 1998, Reed 1997, Panzer 2002, Elia et al. 2012, Anderson et al. 1989, Tooker & Hanks 2004). For instance, Panzer (2002) studied the responses of insects to prescribed burning on prairie remnants and found that in the first growing season after a dormant season fire, 26% of species had higher population abundance relative to those in areas that had not been burned and 40% had lower abundances. 63% of the species with lower abundance rebounded within a year. Differences in fire effects on insect populations are the result of interspecific differences in insect habitat selection (e.g., above-ground species are slower to rebound than below-ground species because of greater immediate mortality; Wikars & Schimmel 2001) and the mobility of the insect or life-stage affected (Seastedt 1984, Bock & Bock 1991, Koltz et al. 2018). It also depends on the resource requirements of the insect—generalist insect populations are less affected than specialists by severe fire (Koltz et al. 2018). The influence of each of these variables is mediated by fire size and patchiness (Panzer 2003). Some insects are attracted to or require recently burned areas. Evans (1971) found 40 species of arthropod that were attracted to areas that had burned. Similarly, Saint-Germain et al. (2004) studied Coleoptera distribution in fire-prone landscapes and found 40 species that were only found in recently burned stands, and many boreal insects that live in trees breed only on trees that have been burned (Wikars 2002). These species will increase in abundance in areas with fire.

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Terrestrial vertebrates Fire-related habitat alteration is an important driver of mammalian population dynamics (e.g., Singer et al. 1989, Romme et al. 2011, Green & Sanecki 2006, Banks et al. 2011a, Fox 1982, Griffiths & Brook 2014, Hedlund & Rickard 1981, Lacki et al. 2009). As with all other taxa, the direction and extent of the impact is species- and context- dependent. Post-fire conditions can strongly modulate indirect fire effects on mammals, since resource availability following fire depends on vegetation regrowth (Recher et al. 2009, Singer et al. 1989, Crowther et al. 2018). Fluctuation in population dynamics following the Yellowstone fires of 1988 illustrates the importance of habitat alteration modulated by post-fire effects. While direct fatalities of large mammals were low, fire (in combination with a drought in the region) led to elk Cervus canadensis die-off the subsequent winter from loss of forage. Winter die-off ranged from 24 to 50% for Yellowstone elk populations in the winter following the fire; die-off in the previous winter was only 5% on average (Singer et al. 1989). However, that population reduction was relatively short-lived and elk populations rebounded to pre-fire numbers by 1995 (Romme et al. 2011). Fire-mediated habitat modification can also lead to changes in predatorprey dynamics. For instance, two small mammal populations known to be preyed upon by foxes declined over time after a fire in the Snowy Mountains of Australia (Green & Sanecki 2006). This decline in abundance corresponded with an increase in mammal remains in fox faeces. Foxes typically consume mostly insects during snow-free months (Green 2003), but a decline in insect abundance following fire resulted in increased small mammal predation (Green & Sanecki 2006). Similarly, ringtail possum numbers declined precipitously after a wildfire near Sydney, Australia. Possums switched from nesting predominantly in dreys (nests made of twigs and sticks located in treetops) to nesting in a mix of dreys and tree cavities after the fire reduced the amount of dreybuilding material. Nesting in tree cavities increased their vulnerability to snakes—snake predation increased proportionally to possum declines (Russell et al. 2003).

Birds

104

The importance of fecundity to population dynamics varies across life history strategies. Fecundity is the most important vital rate for birds with short lifespans and high reproductive output (Clark & Martin 2007, Sæther & Bakke 2000).

Most adult birds can escape direct mortality from fire by flying, but their eggs and young are still susceptible because they lack the mobility of their adult counterparts (Erwin & Stasiak 1979). The loss of nests and young can have a large impact on population abundances for many birds as small changes in fecundity result in large increases in future reproduction.104 Nest success can be affected by fire indirectly as well. Brooker & Rowley (1991) found that a wildfire in Southwestern Australia led to poor splendid fairy-wren and western thornbill breeding success because fire reduced the availability of nesting materials and food. The effect on fairy-wrens (Malurus spp.) was found to be mediated by timing of fire (Murphy et al.

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Species-specific responses to fire are modulated by fire severity. Smucker et al. (2005) studied bird populations before and after a mixed-severity wildfire in the Bitterroot Mountains, USA. They found that species-specific effects on population size resulted in changes in the relative abundance of 9 species. When they included fire severity as a variable, they found changes in relative abundance for 10 more species at specific levels of fire severity (high, moderate, and low). Fire severity often explains contradictory findings in studies of bird population responses to fire (Smucker et al. 2005, Johnson et al. 1994).105 Some bird species are restricted to areas that have burned recently (Hutto 1995). Black-backed woodpeckers Picoides arcticus are one such species. They are found in much higher densities in high-severity burnt boreal forest (Hutto 2008, Murphy & Lehnhausen 1998, Hoyt & Hannon 2002). They forage on bark beetles and wood-boring beetles, which are abundant there (Saab et al. 2002). Black-backed woodpecker nest success and productivity are high in the year immediately following a fire, but decline in subsequent years when lower availability of beetles leads to high rates of nest abandonment (Nappi & Drapeau 2009; Fig. 8.14).

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0.2 0.1 ●

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As with other taxa, indirect population-level fire effects differ across bird species. This difference is highly related to species’ characteristics (Apfelbaum & Haney 1981, Raphael et al. 1987, Smucker et al. 2005, Saab et al. 2002). Foraging strategy is an important determinant of how populations will respond to fire. Long-term data on bird population responses to fire in the Sierra Nevada Mountains, USA showed increases in burnt areas for ground foraging species and brush foragers; foliage foraging species were more numerous in areas that had not burned (Raphael et al. 1987). Apfelbaum & Haney (1981) found similar patterns for bird species in the Great Lakes area of North America.

0.3

Se E as arl on y D Bu ry rn Se L as at on e D Bu ry rn

2010). Nestling survival was much higher following early dry season fire than late dry season fire—the time that wildfires typically burn (Fig. 8.13). In addition, following the late dry season fires, the nesting season was shorter—no birds had two clutches (Murphy et al. 2010). Early dry season prescribed fires are often conducted to limit risk of wildfire. The relative effects of early and late dry season fires on fairy-wrens suggests that this strategy could be beneficial for their populations.

Probability of Mortality

Individuals and populations  125

Data: Murphy et al. (2010)

Figure 8.13: The probability of fairy-wren nestling survival was related to timing of fire. Nestling mortality was high following late dry season burning, but not different from areas with no fire following early dry season burning.

105

In a review of bird population responses to fire in Western USA conifer forests, Kotliar et al. (2002) discovered studies with contradictory findings for 18 species.

Figure 8.14: Black-backed woodpeckers occur almost exclusively in forests burned within 5 years (Saab et al. 2002, Hutto 1995). Nest success (middle panel) and nest productivity—average number of young per nest (right panel) decreased with time since fire.

Photo: Francesco Veronesi CC BY-SA 2.0; Data: Nappi & Drapeau (2009)

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CONDUCTING ROBUST POPULATION ECOLOGY Metapopulation dynamics

106

Levins (1969) coined the term metapopulation and described it as a population of populations.

Immigration and emigration are also important modulators of population dynamics (Schemske et al. 1994). Immigration and emigration occur among populations of the same species located in proximity. All of these spatially discrete populations that interact through individual movement among them form a metapopulation (Levins 1969).106 Since metapopulation dynamics affect population vital rates, even fires that happen outside of the habitat of a particular population can modulate its dynamics. For instance, because black-backed woodpeckers require forests burned within 5 years, patches of burned forest become source populations—those that provide new individuals to other populations. Woodpeckers emigrate to other recently burned patches that were not previously occupied once post-fire habitat has become unfavourable for nesting (Hutto 1995, Nappi & Drapeau 2009). Given the loss in habitat suitability of a burnt area over time, black-backed woodpecker populations depend on a mosaic of patches burned at different times across the broader landscape (Hutto 1995). We discuss the importance of landscape-level variables for understanding fire effects in Chapter 9.

Animal-specific considerations Because animals are highly mobile, it can be difficult to quantify the overall amount of animal mortality from fire and changes in fecundity resulting from fire-induced habitat alteration. As we saw above, metapopulations allow movement among populations. Movement out of a burnt area is a common fire-avoidance strategy and movement into burnt areas is also common, as many animals are attracted to recently burnt areas. Animals must be identifiable as unique individuals to determine fire effects, but keeping track of individuals is difficult and costly. Most studies of fire effects on animals use pre- and post-fire abundance sampling (reviewed in Griffiths & Brook 2014). This does not provide the clearest picture of fire effects as changes in pre- and post- fire abundances likely reflect immigration into or emigration out of the burnt area in addition to births and deaths resulting from fire. Some taxa are easier to track than others. Typically, for a large mammal, a carcass is left behind as evidence of fire-caused mortality. Singer et al. (1989) studied mortality rates for large mammals in Yellowstone National Park after the 1988 fires. They conducted post-fire surveys of dead mammals via helicopter, horseback, and on foot, finding the remains of 261 large mammals (including elk, moose, deer, and bears). While this might sound like a lot, it represented 1% or less of the total numbers for each of these animals in the park. This is not a fool-proof method, however, as some carcasses could be the result of pre-fire mortality.

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One of the best ways to determine direct fire effects is with radio collared individuals. They can be identified to the individual and located directly after the fire to determine what impacts the fire had. Fecske et al. (2004) report the discovery of a dead female cougar following a wildfire in the Black Hills, USA. They located the dead cougar and were able to determine that it died from suffocation as the fire passed. Similarly, Garvey et al. (2010) were able to compare swamp wallaby mortality from wildfire versus prescribed fire by following radio collared wallabies. They were able to determine direct effects of fire on wallabies, and could also document emigrations and assess the impact of post-fire habitat changes (indirect effects) on survival. Tracking technology used to limit telemetry studies to large animals because of the size of radio collars. Additionally, resolution was limited because scientists had to track animals to locate them. New technologies are allowing for smaller tracking devices (Fig. 8.15) with longer lifespans (Kays et al. 2015).

Figure 8.15: Three examples of wildlife fitted with tracking technology. (A) Whooping crane Grus americana with a transmitter on its leg; (B) a bog turtle Glyptemys muhlenbergii with a radio transmitter affixed to its shell; (C) a transmitter attached via harness to a Texas horned lizard Phrynosoma cornutum. A: Don Faulkner, CC BY-SA 2.0; B: Peter Pattavina, US FWS; C: Margo Wright, US Air Force

In addition, animal locations can be monitored continuously in real time and remotely through the use of satellite tracking and Global Positioning Systems devices (Wikelski et al. 2007). As technology continues to advance, there is real potential for more accurately determining fire effects on animals. Radio telemetry has been used to study fire effects on a diversity of species such as bats (Lacki et al. 2009), birds (Sandercock et al. 2015), toads (Hossack et al. 2009), and snakes (Smith et al. 2001).

First-order fire effects models First-order fire effects models allow predictions of plant and animal injury or mortality as a result of fire. They have become important tools for fire managers and ecologists seeking to understand fire effects. They can be used to explore mechanisms of mortality (Reinhardt & Dickinson 2010), predict mortality under different prescriptions when planning prescribed burns (Butler & Dickinson 2010), and provide input to larger models operating over longer timeframes or larger spatial scales (Reinhardt et al. 2001a). Statistical fire effects models use post-fire observations to relate mortality and injury to indicators of fire severity, such as bole char or crown scorch (e.g., Ryan & Reinhardt 1988, Starker 1934, McCarthy & Sims 1935).

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They include plant characteristics, such as height or diameter, as covariates to increase the accuracy of predictions. These types of models are widely used, but are species-specific, requiring unique regressions for each species in a given location (Butler & Dickinson 2010). As a result, there is little information for some regions, and models are often parameterised based on low-intensity fires (Bond 1983, Gromtsev 2002), providing little information regarding effects of high-intensity fires.

107

Campbell’s soil heating model requires information on soil moisture, thermal conductivity, soil bulk and particle densities, soil texture, and initial soil temperature to determine soil heating at a given location (Campbell et al. 1994, Campbell et al. 1995).

More recently, process-based models have been developed. Processbased models simulate the transfer of energy from the fire to the organism, modelling injury or mortality as a direct result of that energy transfer. They are therefore applicable across locations and species (Reinhardt & Dickinson 2010). In order to be transferable across location and environmental conditions, they often require many inputs that are not easily obtained. Therefore, they are only useful in cases where a lot of data on biophysical conditions of the fuels, focal species, and environment are available (Reinhardt et al. 2001a).107 Many fire effects models (both statistical and process-based) are available. The most appropriate model depends on the modeller’s objectives and the types of data available; see discussions in Reinhardt et al. (2001a) and Reinhardt & Dickinson (2010).

Demographic models Demographic models have emerged as the go-to method for integrating vital rates across a population to determine its overall structure and poten´ tial to increase or decrease in abundance over time (Salguero-Gomez & De Kroon 2010, Caswell 1986). They are mathematical models that translate data on individual organisms into a range of information on population dynamics. They make disparate data easy to integrate and provide opportunity for comparisons among studies of organisms under different environmental pressures (Griffith et al. 2016). Demographic models were first used to explore the effect of different life history strategies on animal population dynamics (Leslie 1945, 1948). The original demographic models were age-structured models. This allows for vital rates to be estimated for each age class, increasing the precision of fecundity and survival estimates, since they vary among individuals of different ages. The models require age-specific abundance and vital rate data for parameterisation, and they assume a stable age structure (Leslie 1945).

108

λ is an eigenvalue of the square matrix A if Av = λv for a column vector v. 109 The stable age distribution is the proportions of organisms of each age when the population is growing exponentially at a rate of λ.

Using the age-specific vital rates, a transition matrix is constructed with age-specific fecundity on the top row and age-specific survival listed diagonally (Fig. 8.16). The population abundance at time t + 1 is calculated by multiplying the transition matrix by a vector of population abundances at time t. The asymptotic growth rate, λ, is the dominant eigenvalue108 of the transition matrix. The stable age distribution109 is the corresponding eigenvector—the vectors that satisfy the equation Av = λv . Models based on stage rather than age were developed later as some organisms’ vital rates varied more with life stage (e.g., nestling, juvenile, sub-

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Figure 8.16: (a) A generic life stage graph and (b) transition matrix representing a population of organisms that live 5 years, reproducing from age 1-4 and dying at age 5. S1-S4 are the age-specific survival probabilities. F1-F4 are the age-specific fecundity rates. The number of individuals at any time step (N) is calculated by multiplying the Leslie matrix by a vector of population counts for each time step.

adult, adult, etc.) than actual age (Lefkovitch 1965). Stage-based models make more sense than age-based when vital rates differ among stages and the time spent in each stage varies among individuals. Stage-based models allow demographic models to be applied to species with complex life cycles, such as plants. For instance, perennial plants can experience stages of dormancy that differ among individuals or have reproductive structures emerge independently from growth-related structures. Figure 8.17 shows the life cycle of Navasota lady’s tresses Spiranthes parksii, and endangered orchid endemic to Texas, USA. The orchid can remain dormant for many years before producing any above-ground structures. Additionally, flowering stalks are produced predominantly in the autumn, while photosynthetic organs emerge in the spring (Wonkka et al. 2012). Even this complicated life history is easily captured by a life-cycle graph and transition matrix, making demographic analysis of population viability possible. Demographic models are often used in management contexts to explore population viability for endangered and rare species, compare management techniques, and assess the vulnerability of populations to changes in climate or environmental characteristics (Lebreton & Clobert 1991, Beissinger & Westphal 1998, Brown et al. 2008). By incorporating data from populations experiencing different conditions, modellers can estimate the sensitivity of different life stages or age classes to those conditions. Fire effects have often been the focus of demographic models. Matrix models have been used to assess fire effects on species of concern (e.g., endangered, invasive, ecologically or economically important) of many

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Artwork by Christine Chitwood

Figure 8.17: Stage-based population matrix models can represent complicated life histories. The figure shows a Spiranthes parksii life stage transition diagram: circles represent life stages; arrows represent transitions from one stage to the next. The transitions are (g) growth, (r) reproduction, (s) stasis, (d) dormancy. 110

Menges & Quintana-Ascencio (2004) studied the effect of time since fire on populations of Eryngium cuneifolium, a perennial plant endemic to Florida rosemary scrub. They used different matrices to represent population demography at different times since fire. Using this method, they were able to represent the correlations among life stages within a given population and also explore patterns along a time since fire gradient. They found that λ > 1 in the first 10 years following fire, but λ < 1 after that, with a very high extinction risk for populations in areas that had not had fire for 20 years or more.

taxa—plants (e.g., Menges & Quintana-Ascencio 2004, Caswell & Kaye 2001, Evans et al. 2008, Lesica 1999, Pfab & Witkowski 2000), mammals (e.g., Griffith et al. 2016, Lunney et al. 2007), birds (e.g., Brooker & Brooker 1994, Kern & Shriver 2014), and insects (e.g., McElderry et al. 2015). Models have explored effects of different fire return intervals and fire seasons as well as population responses to single fire events.110 Demographic models have continued to advance beyond stage-structuring ´ to address other shortcomings and increase precision (Salguero-Gomez & De Kroon 2010). Integral projection models allow the modeller to include continuous state variables, such as size, in addition to categorical stages (e.g., age, sex; Enright et al. 1995). This allows demographic models to more accurately tie vital rates to physiological and morphological differences in individuals (Zuidema et al. 2010). The integrated population model is another demographic model that increases precision in estimating population parameters by providing a method for integrating different types of population data, such as markrecapture data and population survey data (Abadi et al. 2010) into the same population model.

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CHAPTER

9

Pyrodiversity Community and landscape ecology Biodiversity, species interactions, and ecosystem function; refugia, patch dynamics, and landscape heterogeneity

This chapter draws on several sub-disciplines and concepts in ecology: biodiversity, community ecology, and landscape ecology. With respect to wildland fire, we discuss these elements within the framework of pyrodiversity, the idea that diverse fire enhances ecosystem structure and function. The term biodiversity—short for “biological diversity”—was introduced as the title of a conference proceedings (Wilson 1988). Contemporaneously, conservation biology emerged as a discipline (Soule´ 1985) and through the 1990s, “biodiversity” was widely adopted as conservation biologists defined their objective. Sarkar (2002) linked the two through their ambiguity: Put bluntly, [I] argue . . . that biodiversity is to be (implicitly) defined as what is being conserved by the practice of conservation biology. . . . The medical analog of ‘biodiversity’ is ‘health,’ equally difficult to define explicitly, but implicitly embedded in the practice of good medicine. (pp. 132–133) Meanwhile, landscape ecology developed as a set of theories and methods to explain how environmental patterns affect ecological processes (Turner 1989). Landscape ecology demonstrated its utility after the 1988 fires in Yellowstone National Park, connecting patterns of fire type, severity, and burned area with plant regeneration (Turner et al. 1994). The spatially and temporally explicit focus on patterns and processes continue to help explain and predict how global environmental change affects the structure and function of ecosystems worldwide (Turner 2010). Pyrodiversity conceptually links biodiversity and fire regimes (He et al. 2019). Pyrodiversity was introduced in recognition of the variability inherent in pre-European fire regimes.111 An early application reduced the spatial extent of fires to create patches within burn units; over time, differences in the time-since-fire among patches creates a mosaic of patches at different successional stages (e.g., Brockett et al. 2001). 131

111

Like biodiversity, pyrodiversity was first introduced at a conference, by Martin & Sapsis (1992): “Fires have occurred in a wide range of patterns—return intervals, seasons, dimensions, and fire characteristics” (p. 156).

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Scope

Consideration

Sub-hypotheses

Examples

Single species

Configuration

Habitat complementarity—Diverse re- Greater Prairie Chickens in the USA require sources within the post-fire landscape proximity to specific successional stages of prairie vegetation at different life history stages.1 meet individual requirements.

Single or multiple species

Interactions

Refugia—Quality and arrangement of resources affect survival during a fire event, facilitate recolonisation and persistence in post-fire landscapes. Redundancy—diverse floristic resources, pollinators stabilise ecosystem services.

Burrows provide many species refuge from heating in the Australian Outback.2 Unburned flowers maintain plant-pollinator networks in South Africa.3 In SE Australia, forested gullies provide refugia for birds and arboreal mammals during fire,4 and bush rats avoid exposure and predation in unburned areas after fire.5

Multiple species

Composition

Habitat heterogeneity—Diversity in successional stages enhances the coexistence of multiple species.

In North America, bird diversity and abundance per species is greatest in patchy grasslands with different post-fire successional stages.6

Temporal

Seasonality—Seasonal differences in fire behaviour (intensity, extent, severity) diversify post-fire environments and enhance survival of more species.

Wet season fires in Florida, USA, are patchy, creating post-fire habitat variability.7 Summer fires in Texas, USA, increased arthropod abundance and relative abundance of several functional groups versus winter burns.8

1

Hovick et al. (2014, 2015); 2 Dawson et al. (2019); 3 Adedoja et al. (2019); 4 Robinson et al. (2016) and Chia et al. (2015); 5 Fordyce et al. (2015); 6 Fuhlendorf et al. (2006); 7 Slocum et al. (2003); 8 Johnson et al. (2008) Table 9.1: Five pyrodiversity subhypotheses, modified from Kelly et al. (2017). While each hypothesis tests general predictions of pyrodiversity, different mechanisms underly each. Understanding the specific processes that generate patterns of pyrodiversity help ensure wildland fire scientists and managers properly understand the relevant components of fire regime that optimise ecosystem management and biodiversity conservation.

Pyrodiversity also derives from diversity in fire severity. A meta-analysis of fire effects on birds and small mammals at different severities concluded, “The varied response of taxa . . . makes it clear that the full range of firebased disturbances is necessary to maintain a full complement of vertebrate species, including fire-sensitive taxa. This is especially true for highseverity fire . . . where maintenance of regional vertebrate biodiversity is a goal (Fontaine & Kennedy 2012, p. 1547).” Initially, the pyrodiversity paradigm lacked explanatory mechanisms and sufficient data to ensure mosaic burns actually benefit biodiversity (Parr & Andersen 2006). Kelly et al. (2017) proposed several sub-hypotheses driven by different mechanisms or relating to different fire regime parameters; we give examples of each in Table 9.1. We structure our discussion of community and landscape ecology around these sub-hypotheses.

BIODIVERSITY AND COMMUNITY ECOLOGY An ecological community consists of two or more co-existing populations of organisms, and thus sits above population in the biological hierarchy (Lidicker 2008). “Community” can refer to either the species assemblage within a taxon, or to the species across all taxa within an identifiable vegetation type. For example, one might refer to fire effects within the avian community in montane forests (e.g., Tingley et al. 2016), or one might refer to fire effects on the montane forest community (e.g., Becker & Lutz 2016).

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Succession and assembly Ecologists have debated the processes that determine community composition for over a century. Succession and assembly are two models that pertain to how ecological community composition responds to a disturbance like fire (Young et al. 2001). In the succession process, fairly discrete groups of species increase their abundance and dominate for a period of time before another group establishes and attains dominance; these groups rotate through dominance until the final, climax community is established. In classic succession theory (e.g., Clements 1916) the intermediary groups are known as seral stages or seres, and succession proceeds until a stable climax community establishes. Early versions of assembly theory emphasised species-specific factors that make community composition an individualistic rather than group-based process (Gleason 1927).112 Succession theory has been widely used in wildland fire science. Two modifications make modern succession theory look substantially different from that described by Clements (1916): Firstly, instead of a single successional trajectory towards an essentially pre-determined climax community, ecologists often recognise one or more alternative stable states. Alternative stable states include succession stages that are either stable at an intermediary state or result from an alternate trajectory, often due to additional biotic or abiotic influence from other disturbances, such as herbivory, or environmental conditions, such as drought (Young et al. 2001). In a major departure from the linear, deterministic models of early succession theory, state-and-transition models can include all potential community types (states), succession pathways between them (transitions), and critical levels of biotic and abiotic conditions that cause major shifts (thresholds) in community composition (Westoby et al. 1989; Fig. 9.1). Secondly, many ecologists use components of assembly theory to predict which species are potential members of a given community. Regional processes such as dispersal, extinction, and competitive interactions affect which species are candidates for a community, before ecological succession determines when they occur after a disturbance. In wildland ecosystems, fire alters competitive interactions and acts as a filter that limits the potential species pool to those that have some evolutionary adaptation to fire (Pausas & Verdu´ 2008). Local processes such as soil variability can also affect post-fire community composition (Ojeda et al. 2010). By altering local conditions that affect habitat and resource availability, fire affects both the overall size of the potential species pool (community size) and the spatial aggregation of species based on niche availability (Myers et al. 2015).

112

Assembly theory is limited by the fact that it largely focuses on explaining the pattern of observed community composition, but provides little insight into processes (Young et al. 2001).

Figure 9.1: A simple example of a state-and-transition model. States are fairly stable vegetation conditions that only shift after crossing thresholds (T) of degradation or major restoration (R). Within states, communities are shaped by succession or minor climatic differences. Transitions (dotted arrows) can be controlled through management.

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134  Ecology of Fire-Dependent Ecosystems

Emergent properties of biodiversity

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Such ecosystem services include direct products like fiber, protein, and timber, as well as the processes that support natural and agricultural ecosystems, such as water quality and supply, pest and weed control, and pollination (Millenium Ecosystem Assessment 2005).

Biodiversity conservation is broadly motived by either the intrinsic value of species themselves, or the utilitarian value of goods and services delivered by intact, functioning ecosystems (Piccolo et al. 2018).113 Ecosystem services are an example of an emergent property of biodiversity because the process is a function of the co-existing species together, a sum greater than the individual species themselves. Ecologists have described several ways by which biodiversity supports ecosystem services such as primary productivity and nutrient cycling (Duffy 2009, Cardinale et al. 2011); ecologists have also documented the loss of those services as communities are removed (Cardinale et al. 2012, Zhang & Biswas 2017). Thus, the relationships between biodiversity and ecological function make community ecology particularly important in ecosystem management. Fire affects relationships between biodiversity and ecological function. Direct effects on ecosystem service delivery can occur by altering succes´ sion—such as maintaining early successional stages (Perez-Granados et al. 2018, Buis et al. 2009) or preventing understory encroachment into latesuccessional forest structure (Kirkman et al. 2001). Indirect effects stem from fire-modified environmental conditions, such as how carbon storage increases in boreal forest after fire modifies microbial habitat and slows decomposition (Ueyama et al. 2019, Cutler et al. 2017).

114

Seasonal effects can also override fire effects. For example, Fraser (1989) found bird communities in South African fynbos varied more by season than in response to burning.

Whether fire enhances or diminishes ecosystem services depends on how the fire regime compares to the range of variability to which biodiversity is adapted. For example, annual burning stimulates primary productivity in mesic grassland relative to unburned areas (Buis et al. 2009), but productivity is sensitive to fire seasonality (Towne & Owensby 1984).114 Ecosystem service delivery and biodiversity can have opposite responses to fire, creating a “disturbance paradox” when ecosystem management goals call for maximising both (Thom & Seidl 2016). Finally, costly impacts to ecosystem services are expected where climate change pushes wildland fire regimes beyond their range of variability (Lee et al. 2015, Seidl et al. 2016).

Quantifying biodiversity Problems with measuring biodiversity have persisted for decades. Biodiversity is an abstract concept, and its contributing factors and emergent properties are complex, dynamic, and interactive. Attempts to reduce biodiversity to manageable data are equally abstract—in general, the more simple the biodiversity metric, the farther the measurement is removed from ecological patterns and processes (Ricotta 2005). Here we review various approaches to biodiversity measurement and analysis, with examples of how these measures have been used in wildland fire science.

Species richness If one understands biodiversity to refer to the number of species in a community, an obvious solution is to count them. This value

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Pyrodiversity  135

Pi

Shannon (1948)

H=−

Simpson (1949)

λ=

Hurlbert (1971)

E(Sn ) =

PR

n=1

n=1

pi log pi ,

where p is relative abundance of the ith species

p2i ,

S P i=1

where R is richness and p is abundance of the ith species.



1−



i (N −N n ) , N (n)

where E(Sn ) is the expected number of species in n individuals from a community of N individuals and S species.

is species richness. Whittaker (1960) specified three measures of species richness: alpha diversity, which refers to the species richness of a local sampled area or stand; beta diversity, species turnover between sample locations, or the degree of overlap from one stand to another; and gamma diversity, the total count of species among all sampled communities.115 Sampled area is important when counting species, as greater area should include more species (Whittaker et al. 2001). Environmental heterogeneity might also affect differences in alpha diversity independent of disturbance. Thus, species-area curves can show fire effects on species richness at multiple spatial scales. When curves are expressed as lines on log-log scales (Fig. 9.2), Y axis values can be interpreted as alpha diversity (number of species) and the slope can be interpreted as one measure of beta diversity (rate of new species accumulation as sampled area increases).

Diversity indices Species richness overlooks how many members of each species make up the community—surely, from a functional perspective, a relatively abundant species is more important than a rare one. Relative abundance is used to calculate community evenness—whether each member of the species occurs more or less in equal proportion, or whether the stand has dominant and/or rare species (Magurran 2004). Diversity indices combine species richness and evenness into a single value meant to quantify community diversity. This is a convenient way to use large species-level datasets to compare diversity across communities (Sarkar 2007); some popular metrics are presented in Table 9.2. In theory, diversity indices are a measure of uncertainty in the population provided by the information in a sample.116 But this approach has been criticised for lacking biological evidence that collections of multiple species—i.e., communities—ought to be ranked along linear scales (e.g., Hurlbert 1971).

Dissimilarity Because diversity indices do not account for the identity of species, they cannot detect differences in composition, such as across areas with different fire histories. It is numerically possible for two communities with entirely different species to have identical diversity values; they need only have the same species richness and equal relative abundances. This result obscures the fire effect: 100% turnover in species composition.

Table 9.2: Some diversity indices used in community ecology. To quote Oksanen et al. (2017): Better stories can be told about Simpson’s index than about Shannon’s index, and still grander narratives about rarefaction (Hurlbert 1971). However, these indices are all very closely related (Hill 1973), and there is no reason to despise one more than others (but if you are a graduate student, don’t drag me in, but obey your Professor’s orders).

115

Whittaker et al. (2001) update the framework to provide a more direct reference to spatial scale, namely local, landscape, and regional, respectively.

Data: Reilly et al. (2006)

Figure 9.2: Typical species-area curves on log-log scales. Fire increased total plant species richness from 0.01–400 m2 in four forest communities in North Carolina, USA.

116

Pielou (1966, p. 463): The more species there are and the more nearly even their representation, the greater the uncertainty and hence the greater the diversity. Information content, which is a measure of uncertainty, is therefore a reasonable measure of diversity.

Change in species composition is a useful way to measure how eco-

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136  Ecology of Fire-Dependent Ecosystems

Jaccard (1912)

a a+b+c ,

Bray & Curtis (1957)

1−

2·Cminij Si +Sj ,

where Cminij is the sum of the smallest values for species shared by samples i & j , and S is total species richness in samples i & j

Ruˇziˇcka (1958)1

1−

Cminij Cmaxij ,

where Cminij is the sum of the smallest values for species shared by samples i & j , and Cmaxij is the sum of the largest values for species shared by samples i & j

where a = count of species shared in samples 1 & 2, b = count of species unique to sample 1, and c = count of species unique to sample 2 (Presence-absence data only)

´ Table 9.3: Three dissimilarity indices used to compare composition of ecological communities. 1 Equation from Caceres et al. (2013).

117

A common vector in wildland fire science is wind, often expressed as two values: speed and direction. 118

While community ecologists often use these values to measure how similar communities are to one another, some methods generally approach the reciprocal of the question and actually calculate how dissimilar they are.

119

Weitzman (1992, p. 365): Distance is such an absolutely fundamental concept in the measurement of dissimilarity that it must play an essential role in any meaningful theory of diversity or classification.

logical communities respond to fire. Species composition data are vectors—multivariate quantities comprised of an abundance value for each species in the community (Philippi et al. 1998).117 Dissimilarity indices reduce the differences among vectors into single values that describe the degree to which a pair of communities are similar or different.118 In this way, dissimilarity measures allow comparison of community responses to fire. Several indices have been used to compare changes in community com´ position following wildland fire (Table 9.3). Caceres et al. (2013) found the Bray-Curtis and Ruˇziˇcka indices robustly describe community resemblance by both species composition and size structure. The Ruˇziˇcka index gives an easy-to-interpret percentage of similarity. For example, Johansen et al. (1982) found post-fire soil algal communities in a North American desert were 85% similar to unburned sites, while Malanson (1984) used the Ruˇziˇcka index to show that community composition in California, USA shrublands was driven more by floristic sub-associations than fire history. The Bray-Curtis dissimilarity index has long been popular among community ecologists and has thus been included in many advances in analytical software. For example, in their determination that montane South African grassland plant species composition generally changes little across different fire regimes, Gordijn et al. (2018) applied statistical transformation and permutational optimisation methods to the classic Bray-Curtis index.

Ecological distance and ordination Ecologists seek to explain variation in responses along environmental gradients. Rather than simply classifying samples into response categories, gradient analysis focuses on the degree and type of responses (Whittaker 1967). Thus, gradient analysis provides insight into the processes associated with community change. Communities can be analysed along environmental gradients by comparing their ecological distance along the gradient.119 Ecological distances among multiple sites are represented as a matrix of pairwise dissimilarity values (Gauch 1973; see example matrices in Fig. 9.3). Ordination is a general term for arranging, or ordering, multivariate values along gradients. Fig. 9.3 gives an example of ordinations for four communities based first on one variable—conifer abundance (top)—then two variables—both conifer and shrub abundance together (bottom). Ordering the

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Figure 9.3: A simple example of dissimilarity and ecological distance in one-species and two-species communities. Top: A single-axis ordination simply ranks sites by the number of conifers. Ecological distance is simply pairwise differences in counts. Bottom: Adding a species adds an ordination axis, along which sites can also be ranked in the second dimension. Ecological distances are still expressed as single values, here the shortest (Euclidean) pairwise distance. Each additional species adds an axis to the ordination; computers calculate pairwise distances in multivariate space.

communities by conifers alone is a straightforward arrangement of increasing conifer abundance, and ecological distance is simply a matrix of differences between pairs: A & B are as similar to each other as B & C and C & D, while A and D are the most dissimilar. Adding a variable—shrub abundance—adds a dimension, but ecological distance is still a matrix. In this example, the distance measure is simply the shortest distance between each pair.120 Any number of subsequent variables (species) can be added, and an axis is added for each—this is impossible to draw in two dimensions, but computers easily handle the multi-dimensional space required to perform ordinations on datasets with many species. As personal computing has become more powerful and affordable, ordination methods have grown in both their number and popularity (von Wehrden et al. 2009).121 Two major advances in multivariate analyses—including ordination—are methods that allow users to choose among several dissimilarity measurements to better fit ecological data, and permutational analyses that allow users to test the probability that clusters and associations along gradients are ecologically valid or due to chance. There are two general approaches to using an ordination to test treatment groups and environmental gradients. Most ordination methods first calculate pairwise similarities between sampled communities and reduce the multi-dimensional space into a two-dimensional projection by determining which arrangement of axes represents the greatest degree of variation in the dataset (Field et al. 1982). With respect to groups and gradients, this final projection can either be constrained or unconstrained, depending on whether the ordination solution is fit against specific variables (constraints), or whether the source of variation in the dataset is understood to be an unknown latent variable and variables of interest are analysed statistically for correlation with patterns in the ordination solution after the fact.

120

Because the shortest distance in two dimensions is the hypotenuse of the right triangle formed by each pair, it is referred to as Euclidean distance. While Euclidean distance is commonly used in ordination analyses, other measures are more appropriate for ecological community composition data, specifically (Faith et al. 1987). 121

There are many types of ordination methods, each with its advantages, disadvantages, and respective groups of advocates and detractors in the scientific community—we don’t go into that here. For more information on multivariate techniques, we recommend Greenacre & Primicerio (2014). See also Kindt & Coe (2005) and James & McCulloch (1990).

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138  Ecology of Fire-Dependent Ecosystems

Evidence for pyrodiversity

122

Spatial and temporal dynamics are critical to enhancing pyrodiversity, and are discussed later in this chapter.

The multivariate methods described above, especially ordination, have been used to test the habitat configuration and composition subhypotheses of pyrodiversity (Kelly et al. 2017; Table 9.1). In the North American tallgrass prairie, research on vegetation succession and bird community composition essentially found evidence for both subhypotheses (Fuhlendorf et al. 2006; Fig. 9.4). Each season since fire provides a unique set of habitat variables in the tallgrass prairie. When different sites within an area are burned at different times, each of these unique variables occurs simultaneously, creating a diverse configuration of habitat and resources (McGranahan et al. 2013b).122 In turn, the diversity of vegetation structure and foraging resources allows many species to find necessary resources and coexist in compositionally diverse local communities.

Figure 9.4: Support for pyrodiversity in two ordinations of data from the North American tallgrass prairie. Rangeland with three different times-since-fire—1, 2, and 3 years—was compared to unburned prairie. The shaded ellipses enclose sites from each treatment group; the degree to which the ellipses are separate is a measure of community dissimilarity based on ecological distance. Both vegetation and bird communities in the most-recently burned sites (1 yr since fire) were entirely dissimilar to sites burned 2 and 3 years ago. Interestingly, unburned sites were entirely dissimilar to sites burned 3 years ago, likely due to the association between unburned sites and shrubs (L); labels denote plant functional groups associated with each treatment. (R) Open circles denote bird species associated with each treatment. Note strong affinity of three species with sites burned most recently.

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