U.S. Climate Change Science Program

Synthesis and Assessment Product 4.2

January 2009

Thresholds of Climate 

Change in Ecosystems
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Thresholds of Climate Change in 

Ecosystems 

Final Report, Synthesis and Assessment Product 4.2 

U.S. Climate Change Science Program 

And the Subcommittee on Global Change Research 

Lead Agency 

U.S. Geological Survey 

Contributing Agencies 

National Oceanic and Atmospheric Administration 

National Science Foundation 

U.S. Department of Agriculture, Forest Service 

U.S. Department of Energy 

U.S. Environmental Protection Agency
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Federal Executive Team 

Director, Climate Change Science Program William J. Brennan 

Director, Climate Change Science Program Office Peter A. Schultz 

Lead Agency Principal Representative to CCSP; Senior Advisor 

for Global Change Programs, U.S. Geological Survey Thomas R. Armstrong 

Product Lead, U.S. Geological Survey Colleen W. Charles 

Synthesis and Assessment Product Advisory Group Chair; 

Associate Director, EPA National Center for Environmental 

Assessment Michael W. Slimak 

Synthesis and Assessment Product Coordinator, Climate Change 

Science Program Office Fabien J.G. Laurier 

Editorial and Production Team 

Layout editor, U.S. Geological Survey Anna Glover 

Copy editor, U.S. Geological Survey Jeanette Ishee 

This document, part of the Synthesis and Assessment Products described in the U.S. Climate Change 

Science Program (CCSP) Strategic Plan, was prepared in accordance with Section 515 of the Treasury and 

General Government Appropriations Act for Fiscal Year 2001 (Public Law 106-554) and theinformation 

quality act guidelines issued by the Department of the Interior and the U.S. Geological Survey pursuant to 

Section 15). The CCSP Interagency Committee relies on the Department of the Interior and the U.S. 

Geological Survey certifications regarding compliance with Section 515 and Department guidelines as the 

basis for determining that this product conforms with Section 515. For purposes of compliance with 

Section 515, this CCSP Synthesis and Assessment Product is an “interpreted product” as that term is used 

in U.S. Geological Survey guidelines and is classified as “highly influential”. This document does not 

express any regulatory policies of the United States or any of its agencies, or provide recommendations for 

regulatory action.

ii 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

January 2009, 

Members of Congress: 

On behalf of the National Science and Technology Council, the U.S. Climate Change Science 

Program (CCSP) is pleased to transmit to the President and the Congress this Synthesis and 

Assessment Product (SAP) Thresholds of Climate Change in Ecosystems. This is part of a series 

of 21 SAPs produced by the CCSP aimed at providing current assessments of climate change 

science to inform public debate, policy, and operational decisions. These reports are also 

intended to help the CCSP develop future program research priorities. 

The CCSP’s guiding vision is to provide the Nation and the global community with the sciencebased knowledge needed to manage the risks and capture the opportunities associated with 

climate and related environmental changes. The SAPs are important steps toward achieving that 

vision and help to translate the CCSP’s extensive observational and research database into 

informational tools that directly address key questions being asked of the research community. 

This SAP addresses and synthesizes the current state of scientific understanding regarding 

potential abrupt state changes or regime shifts in ecosystems in response to climate change. It 

was developed in accordance with the Guidelines for Producing CCSP SAPs, the Information 

Quality Act (Section 515 of the Treasury and General Government Appropriations Act for Fiscal 

Year 2001 (Public Law 106-554)), and the guidelines issued by the Department of Interior and 

the U.S. Geological Survey pursuant to Section 515. 

We commend the report’s authors for both the thorough nature of their work and their adherence 

to an inclusive review process. 

Sincerely, 

Carlos M. Gutierrez 

Secretary of Commerce 

Chair, Committee on 

Climate Change Science 

and Technology Integration 

Samuel W. Bodman 

Secretary of Energy 

Vice Chair, Committee on 

Climate Change Science 

and Technology Integration 

John H. Marburger III 

Director, Office of Science 

and Technology Policy 

Executive Director, Committee 

on Climate Change Science and 

Technology Integration 

iii 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Author Team for this Report 

Lead Authors: 

Daniel B. Fagre, USGS; Colleen W. Charles, USGS. 

Authors: 

Craig D. Allen, USGS; Charles Birkeland, USGS-University of Hawaii; F. Stuart Chapin, 

III, University of Alaska; Peter M. Groffman, Institute of Ecosystem Studies; Glenn R. 

Guntenspergen, USGS; Alan K. Knapp, Colorado State University; A. David McGuire, 

USGS-University of Alaska; Patrick J. Mulholland, Oak Ridge National Laboratory; 

Debra P.C. Peters, USDA Agricultural Research Service; Daniel D. Roby, USGS-Oregon 

State University; George Sugihara, Scripps Institute of Oceanography and University of 

California at San Diego. 

Contributors: 

Brandon Bestelmeyer, Jornada Basin LTER; Julio L. Betancourt, USGS; Jeffrey E. 

Herrick, Jornada Basin LTER 

Thresholds of Climate Change in Ecosystems Federal Advisory Committee 

An eight-member Federal Advisory Committee oversaw the scientific review and 

development of this Synthesis and Assessment Product (SAP) at the request of the U.S. 

Geological Survey. 

Linda Joyce, Forest Service 

Steven McNulty, Forest Service 

Ronald Neilson, Forest Service 

Dennis Ojima, John H. Heinz III Center for Science, Economics and theEnvironment 

David Peterson, Forest Service 

Franklin Schwing, National Oceanic and Atmospheric Administration Fisheries Service 

John P. Smol, Queen’s University, Kingston, Ontario 

Leigh Welling, National Park Service 

v 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Acknowledgments 

 CCSP Synthesis and Assessment Product 4.2 (SAP 4.2) was developed with the 

benefit of a scientifically rigorous first draft peer review conducted by a committee 

appointed under the Federal Advisory Committee Act—Thresholds of Climate Change in 

Ecosystems Federal Advisory Committee (FAC). The resultant review was instrumental 

in shaping the final version of SAP 4.2 and in improving its completeness, sharpening its 

focus, and communicating its conclusions and recommendations. 

 We would like to acknowledge Susan Haseltine, Associate Director-Biology 

USGS, Robert Szaro, Chief Scientist-Biology USGS (retired), Bruce Jones, Chief 

Scientist- Biology, and Jeffrey Kershner, Northern Rocky Mountain Science CenterUSGS for the support, encouragement, and accommodation provided in order to carry out 

this complex task. 

 The authors extend their sincere thanks and appreciation to Fabien Laurier, CCSP, 

for his patience, guidance, and encouragement all through the development of SAP 4.2. 

We wish to thank the members of the Review FAC: Linda Joyce, USDA Forest Service; 

Steven McNulty, USDA Forest Service; Ronald Neilson, USDA Forest Service; Dennis 

Ojima, The H. John Heinz III Center for Science, Economics and the Environment; 

David Peterson, USDA Forest Service; Franklin Schwing, National Oceanic and 

Atmospheric Administration Fisheries Service; John P. Smol, Queen’s University, 

Kingston, Ontario; Leigh Welling, National Park Service. 

 We would like to thank Anthony C. Janetos, Joint Global Change Research 

Institute, for providing guidance and support in developing this document and 

vi 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

vii 

encouragement in getting this information out. We also extend appreciation to Patricia 

Jellison, USGS, for her support in completing SAP4.2. 

 The authors also express deep gratitude to Lindsey Bengtson and Kimberly 

Dueling, Jeannete Ishee, and Anna Glover, USGS, for their invaluable time and 

dedication to the production of SAP 4.2. 

 We are also grateful to all the reviewers who provided valuable comments during 

the public comment period. The author team carefully considered all comments and the 

subsequent revisions resulted in improving the quality and clarity of this report.
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Synopsis 

 As defined in this Synthesis and Assessment Report, 

‘an ecological threshold is the point at which there is an abrupt 

change in an ecosystem quality, property, or phenomenon, or 

where small changes in one or more external conditions produce 

large and persistent responses in an ecosystem’. 

 Ecological thresholds occur when external factors, positive feedbacks, or 

nonlinear instabilities in a system cause changes to propagate in a domino-like fashion 

that is potentially irreversible. This report reviews threshold changes in North American 

ecosystems that are potentially induced by climatic change and addresses the significant 

challenges these threshold crossings impose on resource and land managers. Sudden 

changes to ecosystems and the goods and services they provide are not well understood, 

but they are extremely important if natural resource managers are to succeed in 

developing adaptation strategies in a changing world. 

 The report provides an overview of what is known about ecological thresholds 

and where they are likely to occur. It also identifies those areas where research is most 

needed to improve knowledge and understand the uncertainties regarding them. The 

report suggests a suite of potential actions that land and resource managers could use to 

improve the likelihood of success for the resources they manage, even under conditions 

of incomplete understanding of what drives thresholds of change and when changes will 

occur. 

 Key examples of climate-induced threshold changes are presented. This synthesis 

effort identified a suite of potential actions that, taken together or separately, can begin to 

viii 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

ix 

improve the understanding of thresholds and increase the likelihood of success in 

developing management and adaptation strategies in a changing climate, before, during, 

and after thresholds are crossed. In general, it is essential to increase the resilience of 

ecosystems and thus to slow or prevent the crossing of thresholds; to identify early 

warning signals of impending threshold changes; and to employ adaptive management 

strategies to deal with new conditions, new successional trajectories and new 

combinations of species. 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Recommended Citation 

For the Report as a Whole: 

CCSP, 2009: Thresholds of Climate Change in Ecosystems. A report by the U.S. Climate 

Change Science Program and the Subcommittee on Global Change Research 

[Fagre, D.B., C.W. Charles, C.D. Allen, C. Birkeland, F.S. Chapin III, P.M. 

Groffman, G.R. Guntenspergen, A.K. Knapp, A.D. McGuire, P.J. Mulholland, 

D.P.C. Peters, D.D. Roby, and George Sugihara]. U.S. Geological Survey, 

Reston, VA, 156 pp. 

x 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Table of Contents 

Federal Executive Team ..................................................................................................... ii 

Letter to Congress.............................................................................................................. iv 

Author Team for this Report............................................................................................... v 

Acknowledgments.............................................................................................................. vi 

Synopsis ...........................................................................................................................viii 

Recommended Citation....................................................................................................... x 

Executive Summary............................................................................................................ 1 

Introduction................................................................................................................... 1 

Examples of Ecosystem Thresholds ............................................................................. 2 

Recommendations......................................................................................................... 5 

Chapter 1—Introduction and Background........................................................................ 10 

1.1 The Problem of Sudden Change in Ecological Systems....................................... 10 

1.2 The Response of the Climate Change Community............................................... 13 

1.3 The Goal of SAP 4.2............................................................................................. 15 

1.4 Standard Terms ..................................................................................................... 17 

Chapter 2—Ecological Thresholds................................................................................... 18 

2.1 Introduction........................................................................................................... 18 

2.2 Early Development ............................................................................................... 20 

2.3 Current Discussions of Threshold Phenomena..................................................... 22 

2.4 Ecological Thresholds Defined for SAP 4.2......................................................... 25 

2.5 Factors That Influence Resilience......................................................................... 27 

2.6 The Bottom Line................................................................................................... 29 

xi 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Chapter 3—Case Studies .................................................................................................. 32 

Case Study 1: Ecological Thresholds in Alaska ......................................................... 32 

Case Study 2. The Mid-Continent Prairie Pothole Region: Threshold 

Responses to Climate Change......................................................................... 46 

Case Study 3. Broad-Scale Forest Die-Back as a Threshold Response to 

Climate Change in the Southwestern United States ....................................... 53 

Case Study 4. Thresholds in Climate Change for Coral-Reef Ecosystem 

Functioning ..................................................................................................... 62 

Chapter 4—Examples of Threshold Change in Ecosystems ............................................ 74 

4.1 Background........................................................................................................... 74 

4.2 Example of Thresholds from the Past................................................................... 75 

4.3 Temperature Increases Are Pushing Ecosystems Towards Thresholds................ 76 

4.3.1 Climate Interactions Drive Ecosystems to Thresholds ................................ 78 

4.3.2 Climate Variability Increases Likelihood of Threshold Shifts .................... 79 

4.3.3 Other Human Stressors and Climate Change............................................... 80 

4.3.4 Ecosystem Vulnerability and Climate Change ............................................ 84 

Chapter 5—What Can Be Done?...................................................................................... 87 

5.1 Integration of Management and Research ............................................................ 87 

5.1.1 Need for Conceptual Models ....................................................................... 87 

5.1.2 Scaling.......................................................................................................... 89 

5.1.3 Applying Models from Other Disciplines.................................................... 91 

5.2 Adaptive Management to Increase Resilience...................................................... 92 

5.2.1 Role of Monitoring ...................................................................................... 94 

xii 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

xiii 

5.2.2 Role of Experiments .................................................................................... 96 

5.3 Management by Coping........................................................................................ 97 

5.3.1 Reducing Multiple Stressors........................................................................ 98 

5.3.2 Triage ........................................................................................................... 99 

5.3.3 System-Level Planning and Policy ............................................................ 100 

5.3.4 Capacity Building and Awareness............................................................. 101 

5.4 Summary............................................................................................................. 102 

Chapter 6—Summary and Science Recommendations .................................................. 103 

Summary................................................................................................................... 103 

Science Recommendations ....................................................................................... 104 

Before ................................................................................................................. 104 

During ................................................................................................................. 107 

After ................................................................................................................. 110 

Conclusion ................................................................................................................ 110 

References Cited ............................................................................................................. 112 

Appendix A—Glossary................................................................................................... 154 

Contact Information........................................................................................................ 157 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Executive Summary 

Introduction 

As defined in this assessment, an ecological threshold is the point at which there 

is an abrupt change in an ecosystem quality, property, or phenomenon, or where small 

changes in one or more external conditions produce large and persistent responses in an 

ecosystem. Ecological thresholds occur when external factors, positive feedbacks, or 

nonlinear instabilities in a system cause changes to propagate in a domino-like fashion 

that is potentially irreversible. Once an ecological threshold is crossed, the ecosystem in 

question is not likely to return to its previous state. 

Over the past three decades, climate change has become a recognized driver of 

ecosystem change. Changes in phenology, ra shifts of species, and increases in such 

disturbances as wildland fires are all examples of ecosystem-scale responses to a 

warming biosphere. Much ecosystems research focuses on enhancing understanding of 

climate change impacts on ecosystems (and vice versa) and in developing the capability 

to predict the potential impacts of future climate change. In addition to the gradual types 

of climate-related change mentioned above, there is increasing recognition that small 

changes in climate can trigger major, abrupt responses in ecosystems when a threshold is 

crossed 

The potential for sudden, unanticipated shifts in ecosystem dynamics make 

resource planning, preparation, and management intensely difficult. These sudden 

changes to ecosystems and the goods and services they provide are not well understood, 

but they are extremely important if natural resource managers are to succeed in 

developing adaptation strategies in a changing world. This report provides an overview of 

1 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

what is known about ecological thresholds and where they are likely to occur. It also 

identifies those areas where research is most needed to improve knowledge and 

understand the uncertainties regarding them. The report suggests a suite of potential 

actions that land and resource managers could use to improve the likelihood of success 

for the resources they manage, even under conditions of incomplete understanding of 

what drives thresholds of change and when changes will occur. The focus of this report is 

on North American ecosystem threshold changes and what they mean for human society. 

Examples of Ecosystem Thresholds 

There are numerous examples of sudden ecological change that fit the current 

qualitative definition of an ecological threshold and that were likely caused by climatic 

changes such as warming temperatures. A clear example comes from recent observations 

of the Arctic tundra, where the effects of warmer temperatures have included reduced 

snow cover duration, which leads to reduced reflectivity of the surface. Reduced 

reflectivity causes greater absorption of solar energy, resulting in local warming, which, 

in turn, further accelerates the loss of snow cover. This amplified, positive feedback 

effect quickly leads to warmer conditions that foster the invasion of shrubs into the 

tundra. The new shrubs themselves then further reduce albedo and add to the local 

warming. The net result is a relatively sudden, domino-like chain of events that result in 

conversion of the arctic tundra to shrubland, triggered by a relatively slight increase in 

temperature. 

Examples like this illustrate the importance of positive feedbacks. Positive 

feedbacks are those that tend to increase alteration of the nature of the system, while 

negative feedbacks tend to minimize these changes. Ecosystems include both positive and 

2 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

negative feedbacks. Changes in external or internal factors that favor and strengthen 

positive feedbacks can lead to a change in conditions that may overwhelm other 

components of the system, leading to threshold changes. For example, the invasion and 

spread of a highly flammable grass in deserts will change the susceptibility of that 

landscape to fire. As another example, persistent drought will push an ecosystem’s 

vegetation toward the limits of its physiological tolerance to water stress, creating 

conditions that favor drought-tolerant species at the expense of thirstier plants; this leads 

to system change, until a new state (with different, more drought-tolerant species) is 

achieved. 

Ecosystems are not simple, and complex interactions between multiple factors 

and feedbacks can lead to even greater nonlinear changes in their dynamics. For example, 

the interaction of drought together with overgrazing can trigger desertification. 

Disturbance mechanisms, such as fire and insect outbreaks, shape many landscapes and 

may predispose many of them to threshold change when the additional stress of climate 

change is added. Furthermore, climate change will alter not only the landscape, but it will 

also affect the disturbance mechanisms themselves; in the example above, a warmer 

climate may not only lead to vegetation changes, but may also favor increased dryness, 

which will increase the likelihood of fire. 

On a global scale, such altered disturbance regimes may influence rates of climate 

change. For example, as mentioned above, warm, dry conditions favor fire, and more 

fires release more carbon dioxide from burning vegetation, which in turn favors more 

warming. Adding additional complexity to already-complex systems, human actions also 

interact with natural drivers of change, producing multifaceted ecosystem changes that 

3 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

have important implications for the services provided by those ecosystems. For instance, 

the introduction of exotic, invasive plants may change the way in which an ecosystem 

responds to drought, and the conversion of woodland to farmed fields or urban areas will 

change the manner in which that landscape responds to intense storms. 

The stories of several important ecosystems provide concrete examples of 

ecological thresholds, and illustrate the kinds of complex change that natural resource 

managers are facing, and that they must manage in the future. 

As mentioned briefly above, a key example of observed climate-related threshold 

change is the warming of Alaska. Warming has caused a number of effects, including 

earlier snowmelt in the spring, reductions in sea-ice coverage, warming of permafrost, 

and resultant impacts to ecosystems including dramatic changes to wetlands, tundra, 

fisheries, and forests, including increases in the frequency and spatial extent of insect 

outbreaks and wildfire. During the 1990s, south-central Alaska experienced the largest 

outbreak of spruce bark beetles in the world. Milder winters and warmer temperatures 

increased the over-winter survival of the spruce bark beetle and allowed the bark beetle to 

complete its life cycle in 1 year instead of the normal 2 years. Added to this were 9 years 

of drought stress, which resulted in spruce trees that were too weak to fight off the beetle 

infestation. For these forests, multiple climate-triggered stresses amplified each others’ 

effects to cause a profound ecosystem change. 

The Alaskan spruce bark beetle outbreak and consequent forest die-off are an 

example of an actual climate-induced threshold crossing. There are additional ecosystems 

for which conditions suggest an approaching climate-related threshold. These include 

coral reefs, prairie pothole wetlands, and southwestern forests. Climate-related processes 

4 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

that affect coral reefs include sea-level rise, ocean acidification, and the increased water 

temperatures that are responsible for coral bleaching events. The Prairie Pothole Region 

of north-central North America is one of the most ecologically valuable freshwater 

resources of the Nation, with numerous wetlands that provide critical habitat for 

waterfowl populations. Climate models suggest a warmer, drier future climate for the 

Prairie Pothole Region, which would result in a reduction in, or elimination of, wetlands 

that provide waterfowl breeding habitat. Similarly, predicted warmer, drier conditions in 

the semiarid forests and woodlands of the southwestern United States would place those 

forests under more frequent water stress, resulting in the potential for shifts between 

vegetation types and distributions, and could trigger rapid, extensive, and dramatic forest 

dieback. 

In each of these cases, the anticipated changes would also be expected to tie to 

other nonlinear feedback relationships and other ecological disturbance processes, 

potentially leading to additional nonlinear threshold behaviors. Understanding and 

predicting the outcome of such complex interactions is not a trivial endeavor. Ecological 

systems are multivariate in nature, but current ecological forecasting model capabilities 

are comparatively simple and generally do not address the possibility or consequences of 

thresholds. Complex situations like those involving ecological thresholds thus tend to be 

beyond the limits of existing predictive capabilities. The end result is surprises for 

managers. 

Recommendations 

If climate change is pushing more ecosystems toward thresholds, what can be 

done by land and resource managers and others to better cope with the threat of 

5 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

transformative change? Although the science of ecological thresholds is still in its 

infancy, one outcome of this synthesis effort was the identification of a suite of potential 

actions that, taken together or separately, can improve the understanding of thresholds 

and increase the likelihood of success in developing management and adaptation 

strategies in a changing climate, before, during, and after thresholds are crossed: 

Support Research to Identify Thresholds.—While conceptually robust and widely 

acknowledged as already occurring, thresholds and threshold crossings have had 

relatively few empirical studies addressing them. Reliable identification of thresholds 

across different systems should be a national priority because of the potential for 

substantive surprises in the management of our natural resources. 

Enhance Adaptive Capacity.—Given that threshold changes are increasingly 

likely to occur, it is important to prepare for them by increasing societal and ecological 

resilience. Managers that understand ecological diversity and the other factors that 

influence the resilience of the systems they manage are in a better position to implement 

changes that reduce the likelihood that thresholds will be crossed. 

Monitor and Adjust Multiple Factors and Drivers.—Once the key factors 

controlling adaptive capacity and resilience are known, monitoring strategies should 

include those factors. Consideration should be given to monitoring indicators of 

ecosystem stress rather than the resources and ecological services of management 

interest. 

Develop Scenarios of the Consequences of Alternative Management Options for 

Dealing with Potential Changes.—In some cases, the kinds of external factors that can 

precipitate threshold changes are well known, and furthermore are known in advance (for 

6 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

example, hurricanes, wildfire, or invasive species). In these cases, scenario analysis is a 

powerful tool for predicting and understanding the potential consequences of specific 

management actions. 

Collate and Integrate Information Better at Different Scales.—Because agencies 

and institutions have different management mandates, there can be a focus on those 

resources and at their scales of interest to the exclusion of others. Better information 

sharing and integration have great potential for improving the understanding of 

thresholds and identifying when they might occur. 

Reduce Other Stressors.—Many trigger points for abrupt change in ecosystems 

that are responding to climate change are not recognized, because human civilizations 

have not previously witnessed climate change of this magnitude. However, other 

stressors for which reliable information exists can be reduced to make ecosystems 

healthier and more resilient as climate changes. 

Manage Threshold Shifts.—There may be constraints to reducing or reversing 

climate-change-induced stresses to components of an ecosystem. If a threshold seems 

likely to occur but the uncertainties remain high as to when it will occur, contingency 

plans should be created. These can be implemented when the threshold shift begins to 

occur or can be carried out in advance if the approaching threshold is clear. 

Project Impacts to Natural Resources.—There are many efforts to project climate 

change (for example, general circulation models) and ecosystem responses to climate 

change (for example, mapped atmosphere-plant-soil systems) using simulation modeling 

and other tools. These models generally project ecosystem trends and shifts, but do not 

explicitly consider the possibility of thresholds. A concerted effort must be made to 

7 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

understand, model, and project ecosystem responses to climate change with explicit 

acknowledgment of thresholds. 

Recognize Need for Decisionmaking at Multiple Scales.—Much of the recent 

information on climate change impacts suggests that changes are occurring more quickly 

than forecast only a few years ago. It is also apparent that many changes are causing 

secondary, or cascading, domino-like, changes in other parts of ecosystems. Management 

policies that were developed during relatively stable climate conditions may be 

inadequate for a variable world with more surprises. A shift toward multiple scales of 

information integration and subsequent decisionmaking can enhance and leverage 

existing management resources. 

Instigate Institutional Change to Increase Adaptive Capacity.—In many cases, 

current institutional structures are geared towards disciplinary and jurisdictional isolation 

by agencies and, therefore, they do not facilitate synthesis across resources, regions or 

issues. The capacity for such synthesis will be critical for identifying potential thresholds 

in ecosystem processes on multiple scales. 

Identify Research Needs and Priorities to Address Thresholds.—At this point in 

time, very little is understood about thresholds in ecosystems. The major research needs 

and priorities that will enhance the ability in the future to forecast and detect abrupt 

changes in ecosystems caused by climate change must be articulated. The ubiquity of 

threshold problems across so many fields suggests the possibility of finding common 

principles at work. The cross-cutting nature of the problem of large-scale system change 

suggests an unusual opportunity to leverage effort from other fields and apply it to 

investigating systemic risk of crossing thresholds. 

8 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

In summary, the science of understanding and predicting ecological thresholds is 

still in its infancy, and our existing understanding of many aspects and potential impacts 

of these thresholds is qualitative at best. The challenge is to improve the science needed 

to support decisionmaking, while recognizing that managing lands and resources is a 

continual process and that strategies are needed to inform management decisions that 

must be made under conditions of high uncertainties regarding potential thresholds. To 

better understand and prepare for ecological threshold crossings and their consequences, 

it is essential to increase the resilience of ecosystems and thus to slow or prevent the 

crossing of thresholds; to identify early warning signals of impending threshold changes; 

and to employ adaptive management strategies to deal with new conditions, new 

successional trajectories and new combinations of species. Better integration of existing 

monitoring information across a range of spatial scales will be needed to detect potential 

thresholds, and research will need to focus on ecosystems undergoing a threshold shift to 

better understand the underlying processes. In a world being altered by climate change, 

natural resource managers may also have to be increasingly nimble, and adjust their goals 

for desired states of resources away from static, historic benchmarks and focus on 

increased resilience, biodiversity, and adaptive capacity as measures of success. 

9 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Chapter 1—Introduction and Background 

1.1 The Problem of Sudden Change in Ecological Systems 

The carbon dioxide (CO2) concentration in the earth’s atmosphere has reached 

385 parts per million (ppm), a level that is unprecedented over the past one-half million 

years (based on ice core data) to 24 million years (based on soil data) (Hoegh-Guldberget 

et al. 2007). CO2 levels have been increasing during the past 150 years, with most of the 

change occurring in just the past few decades. Global mean temperature has risen in 

response to increased CO2 concentration and is now higher than at any time in the past 

1,000 years (based on tree rings) to 160,000 years (based on oxygen 18 (18O) and 

deuterium (D) isotopes in ice). The relatively sudden increase in the energy balance of 

the planet, due to an increase in greenhouse gas concentrations, has led to abrupt global 

climate changes that alter physical processes and biological systems on many scales and 

will certainly affect ecosystems that support human society (IPCC 2007). One of the 

ways that a rapidly changing climate may affect ecosystems is by causing sudden, 

irreversible effects that fundamentally change the function and structure of the ecosystem 

with potentially huge impacts to human society (Wamelink et al.2003). 

Even small, gradual change can induce threshold changes. For instance, in 1976–

1977, major shifts occurred in sea surface temperatures, fisheries landings, zooplankton 

abundance, and community composition in the North Pacific (Hare and Mantua, 2000). 

Later analysis suggested that nonlinear regime shifts operate in this ecosystem, such that 

even small changes in physical conditions can provoke a regime shift that may not be 

easily or symmetrically reversed (for example, an increase in temperature from global 

warming, even as small as 0.50

C, has led to responses that have been well documented) 

10 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

(IPCC 2007; Hsieh et al. 2006). This tendency can be compounded by additional 

environmental stressors that predispose ecosystems to experience threshold changes in 

response to climate change. For example, in North America in the late 1990s, forests, 

woodlands, grasslands, and shrublands exhibited extensive dieback across the arid 

southwestern United States as overgrazing, fire suppression, and climate variability led to 

massive insect outbreaks and an unprecedented breadth of area consumed by fire (Allen, 

2007). 

Abrupt changes in ecosystems may result in dramatic reductions in ecosystem 

services, such as water supplies for human use. In the Klamath River basin in the Pacific 

Northwest, for example, the delicate socioecological balance of water allocation between 

needs for irrigated agriculture and habitat for endangered species of fish, which had been 

established in 1902, collapsed in 2002 during a multiyear drought because the system’s 

resilience to maintain water quality in the face of climatic variability was degraded by 

long-term nutrient loading (NRC 2002). Thresholds pose perhaps the greatest challenge 

currently facing climate change scientists. There is clear evidence that climate change has 

the potential to increase threshold changes in a wide range of ecosystems, but the basic 

and practical science necessary to predict and manage these changes is not well 

developed (Groffman et al. 2006). In addition, climate change interacts with other natural 

processes to produce threshold changes. Disturbance mechanisms, such as fire and insect 

outbreaks (Crutzen and Goldammer 1993, Lovett et al. 2002, respectively), shape 

landscapes and may predispose many of them to threshold change when the stress of 

climate change is added (Swetnam and Betancourt 1998). To complicate matters further, 

climate change can alter the disturbance mechanisms themselves and, on a global scale, 

11 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

altered disturbance regimes may influence rates of climate change. Another challenge is 

the multiscaled nature of threshold changes. These changes almost always involve 

coupled socioecological dynamics where human actions interact with natural drivers of 

change to produce complex changes in ecosystems that have important implications for 

the services provided by the ecosystems (Wamelink et al. 2003). 

A sense of urgency regarding thresholds exists because of the increasing pace of 

change, the changing features of the drivers that lead to thresholds, the increasing 

vulnerabilities of ecosystem services, and the challenges the existence of thresholds poses 

for natural resource management. These challenges include the potential for major 

disruption of ecosystem services and the possibility of social upheaval that might occur 

as new ways to manage and adapt for climate change and to cope with the unanticipated 

change are required. 

Research on ecological thresholds is being assessed critically. The Heinz Center 

conducted several workshops that presented case studies of likely threshold change and 

began looking at possible social and policy responses. Another effort included numerous 

case studies focused on nonlinearities in ecological systems (Burkett et al. 2005) and 

considered how thresholds are nonlinear responses to climate change. Recently, specific 

requests for proposals have been issued for research on thresholds (for example, see 

http://es.epa.gov/ncer/rfa/2004/2004_aqua_sys.html; 

http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/reccipients.display/rfa_id/422/r

ecords_per_page/ALL), and there are active efforts to bridge the gap between research 

and application in this area (see, for example, www.ecothresholds.org). Assessment of 

12 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

the “state of the science” as it relates to ecosystems in the United States and for 

articulation of critical research needs is needed. 

1.2 The Response of the Climate Change Community 

Climate change is a very complex issue, and policymakers need an objective 

source of information about the causes of climate change, its potential environmental and 

socioeconomic consequences, and the adaptation and mitigation strategies to respond to 

the effects of climate change. In 1979, the first World Climate Conference was organized 

by the World Meteorological Organization. This conference expressed concern about 

man’s activities on Earth and the potential to “cause significant extended regional and 

even global changes of climate” and called for “global cooperation to explore the 

possible future course of global climate and to take this new understanding into account 

in planning for the future development of human society” (IPCC 2007a). A subsequent 

conference in 1985 focused on the assessment of the role of CO2 and other greenhouse 

gases in climate variations and associated impacts, concluding that an increase of global 

mean temperature could occur that would be greater than at any time in humanity’s 

history. As a follow up to this conference, the Advisory Group on Greenhouse Gases, a 

precursor to the Intergovernmental Panel on Climate Change (IPCC), was set up to 

ensure periodic assessments of the state of scientific knowledge on climate change and 

the implications of climate change for society. Recognizing the need for objective, 

balanced, and internationally coordinated scientific assessment of the understanding of 

the effects of increasing concentrations of greenhouse gases on the earth’s climate and on 

ways in which these changes may potentially affect socioeconomic patterns, the World 

Meteorological Organization and the United Nations Environment Programme 

13 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

coordinated to establish an ad hoc intergovernmental mechanism to provide scientific 

assessments of climate change. Thus, in 1988, the IPCC was established to provide 

decisionmakers and others interested in climate change with an objective source of 

information about climate change. 

The role of the IPCC is to assess (on a comprehensive, objective, open, and 

transparent basis) the scientific, technical, and socioeconomic information relevant to 

understanding the scientific basis of risk of human-induced climate change, its potential 

impacts, and options for adaptation and mitigation and to provide reports on a periodic 

basis that reflect existing viewpoints within the scientific community. Because of the 

intergovernmental nature of the IPCC, the reports provide decisionmakers with policyrelevant information in a policy neutral way (IPCC, 2007a). The first IPCC report was 

published in 1990, with subsequent reports published in 1995, 2003, and 2007. 

In 1989, the U.S. Global Change Research Program began as a Presidential 

initiative and was codified by Congress in the Global Change Research Act of 1990 (Pub. 

L. 101–606), which mandates development of a coordinated interagency research 

program. The Climate Change Science Program (CCSP, www.climatescience.gov), a 

consortium of Federal agencies that perform climate science, integrates the research 

activities of the U.S. Global Change Research Program with the U.S. Climate Change 

Research Initiative. 

The CCSP integrates federally supported research on global change and climate 

change as conducted by the 13 U.S. Government departments and agencies involved in 

climate science. To provide an open and transparent process for assessing the state of 

scientific information relevant to understanding climate change, the CCSP established a 

14 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

synthesis and assessment program as part of its strategic plan. A primary objective of the 

CCSP is to provide the best science-based knowledge possible to support public 

discussion and government and private sector decisionmaking on the risks and 

opportunities associated with changes in the climate and related environmental systems. 

The CCSP has identified an initial set of 21 synthesis and assessment products 

(SAPs) that address the highest priority research, observation, and decision-support needs 

to advance decisionmaking on climate change-related issues. This assessment, SAP 4.2, 

focuses on abrupt ecological responses to climate change, or thresholds of ecological 

change. It examines the impacts to ecosystems when thresholds are crossed. It does not 

address those ecological changes that are caused by major disturbances, such as 

hurricanes. These externally driven changes, or exogenous triggers, are distinguished 

from changes caused by shifts in the ecosystem’s response to a driver, such as a gradual 

rise in temperature. These internal changes in system response, or endogenous triggers, 

are the focus of this SAP. This SAP is one of four reports that address the Ecosystems 

research element and Goal 4 of the CCSP strategic plan to understand the sensitivity and 

adaptability of different natural and managed ecosystems and human systems to climate 

and related global changes. 

1.3 The Goal of SAP 4.2 

This SAP summarizes the present state of scientific understanding regarding 

potential abrupt state changes or regime shifts in ecosystems in response to climate 

change. The goal is to identify specific difficulties or shortcomings in our current ability 

to identify the likelihood of abrupt state changes in ecosystems as a consequence of 

climate change. 

15 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Questions addressed by this SAP include: 

1. What specifically is meant by abrupt state changes or regime shifts in the 

structure and function of ecosystems in response to climate change? 

2. What evidence is available from current ecological theory, ecological 

modeling studies, or the paleoecological record that abrupt changes in 

ecosystems are likely to occur in response to climate change? 

3. Are some ecosystems more likely to exhibit abrupt state changes or threshold 

responses to climate change? 

4. If abrupt changes are likely to occur in ecosystems in response to climate 

change, what does this imply about the ability of ecosystems to provide a 

continuing supply of ecosystem goods and services to meet the needs of 

humans? 

5. If there is a high potential for abrupt or threshold-type changes in ecosystems 

in response to climate change, what changes must be made in existing 

management models, premises, and practices in order to manage these 

systems in a sustainable, resilient manner? 

6. How can monitoring systems be designed and implemented, at various spatial 

scales, in order to detect and anticipate abrupt or threshold changes in 

ecosystems in response to future climate change? 

7. What are the major research needs and priorities that will enhance the ability 

in the future to forecast and detect abrupt changes in ecosystems caused by 

climate change? 

16 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

1.4 Standard Terms 

The 2007 Intergovernmental Panel on Climate Change Fourth Assessment Report 

(IPCC, 2007) is the most comprehensive and up-to-date report on the scientific 

assessment of climate change. This assessment (SAP 4.2) uses the standard terms defined 

in the IPCC’s Fourth Assessment Report with respect to the treatment of uncertainty and 

the likelihood of an outcome or result based on expert judgment about the state of that 

knowledge. The definitions are shown in figure 1.1. This set of definitions is for 

descriptive purposes only and is not a quantitative approach from which probabilities 

relating to uncertainty can be derived. 

Figure 1.1. Degrees of outcome likelihood as defined in the IPCC’s Fourth Assessment Report (AR4) 

(IPCC, 2007). 

Likelihood terminology Likelihood of the occurrence/ 

outcome 

Virtually certain > 99% probability 

Extremely likely > 95% probability 

Very likely > 90% probability 

Likely > 66% probability 

More likely than not > 50% probability 

About as likely as not 33 to 66% probability 

Unlikely < 33% probability 

Very unlikely < 10% probability 

Extremely unlikely < 5% probability 

Exceptionally unlikely < 1% probability 

17 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Chapter 2—Ecological Thresholds 

2.1 Introduction 

Temperature, precipitation, and related climate variables are fundamental 

regulators of biological processes and it is reasonable to expect that significant changes in 

the climate system may alter linkages and feedbacks between ecosystems and regional 

climate systems. Increasing focus is being placed on the existence and likelihood of 

abrupt state changes or threshold responses in the structure and functioning of ecosystems 

(Holling 1986; Scheffer et al., 2001; Higgins et al. 2002; Foley et al. 2003; Schneider 

2004; Burkett et al. 2005; Hsieh et al. 2005). Various interrelated terms are employed in 

the scientific literature to characterize these types of discontinuous and rapid changes in 

ecosystems, including ecosystem tipping points, regime shifts, threshold responses, 

alternative or multiple stable states, and abrupt state changes. Our current understanding 

of thresholds and ecosystem responses makes it unlikely that we can predict such 

discontinuities in ecosystems, and these discontinuities are likely to result in profound 

changes to natural resources that are sensitive to climate changes, as well as to human 

societies that depend on ecosystem goods and services. This assessment, based on the 

literature and the synthesis teams’ expertise, indicates that thresholds are likely to 

represent large-scale risk and uncertainty and will likely be a major challenge to natural 

resource managers. 

Abrupt transitions have occurred in numerous ecosystems where incremental 

increases in global temperature have produced sudden and dramatic changes in the state 

of and the dynamics governing these systems (Anderson et al. 2008). These thresholds of 

magnified ecological change are a consequence of the underlying nonlinear nature of 

18 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

ecosystems and are very likely critical to adaptation strategies for managing natural 

resources in a rapidly changing world. Sudden, unanticipated shifts in ecosystem 

dynamics are a major source of uncertainty for managers and make planning and 

preparation difficult. One of the primary objectives of this report (SAP 4.2) is to enhance 

the understanding and ability of managers to forecast the effects of climate change on 

ecosystems. 

As discussed elsewhere in this chapter, the occurrence of threshold, or abrupt 

changes in ecosystems, is suggested by current ecological theory and models, and is 

documented with laboratory and field examples and even in the paleoecological record. 

However, on a predictive level, thresholds remain poorly understood, particularly in 

terms of the underlying causal mechanisms and the general factors that predispose 

systems to threshold effects. For example, it is unclear under what circumstances climate 

change (both in its mean state and in its variance in space and time, including occurrence 

of extreme weather events) might cause ecosystem threshold shifts, instead of more 

gradual, continuous changes in ecosystems and species. Further, it is not known what the 

resulting effects of very abrupt climate change (that is, crossing climate thresholds) on 

ecosystems will be. However, it will likely increase the likelihood of an ecosystem 

threshold shift. Thus, while rapid transitions in ecosystems are clear, reaching a level of 

understanding that enables one to anticipate or actually predict threshold effects is the 

main bottleneck to producing results that are useful to managers (Muradian 2001; 

Bestelmeyer 2006; Groffman et al. 2006; Kinzig et al. 2006). 

19 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

2.2 Early Development 

The concepts of ecological thresholds, multiple stable states, and regime shifts 

originated in early theoretical work on the stability or persistence of ecosystems 

(Margalef, 1963; Lewontin, 1969; Odum, 1969; Holling, 1973; May 1973, 1977). The 

two key components of stability were considered to be the system’s “resilience,” or the 

speed at which it would return to its current “stable equilibrium,” and its “resistance,” or 

ability to maintain its current “stable” state in the face of disturbance of a given 

magnitude. According to this early thinking, given enough disturbance, systems could be 

pushed into alternative stable states. This theoretical work was complemented (however 

sparsely) with early empirical demonstrations of multiple stable states in marine 

experimental systems (Sutherland, 1974) and with field data combined with model 

analysis for terrestrial ecosystems (Ludwig et al. 1978). 

“Stability” as a well-defined mathematical concept was central to these early 

theoretical discussions of thresholds. Lewontin (1969) reviewed mathematical models of 

stability and discussed the forces required to move an ecosystem out of a basin of 

attraction or stable state. May (1973) presented a precise definition of stability and a 

crater-and-ball analogy to illustrate the concepts. Later, May (1977) focused attention on 

the existence of alternative stable states and multiple equilibrium points with an emphasis 

on the thresholds between them. Holling (1973) drew attention to the ability of 

ecosystems to absorb and respond to disturbance and introduced the concept of resilience. 

Again, resilience focuses on dynamics far from equilibrium and was used to measure the 

magnitude of perturbations from which recovery of a system was no longer possible. 

20 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Although mathematically tractable and well defined in static engineering contexts, 

in the 1990s “stability” and the implication of “equilibrium” in ecological systems began 

gradually to give way to growing evidence that real ecological systems are neither static 

nor even well approximated as such. Notions of stable equilibrium, which continue to 

dominate much of our thinking and research to date are based on models and controlled 

experiments (for example, on paramecia and flour beetles) from the middle of the last 

century where singular static equilibrium was the ideal. Cracks in the equilibrium view 

began to appear as quantitative evidence mounted from natural systems demonstrating 

that “change” rather than “constancy” is the rule, and that nonlinear instability, 

thresholds, and chaos can be ubiquitous in nature (Dublin et al. 1990; Sugihara and May, 

1990; Tilman and Wedin, 1991; Grenfell, 1992; Knowlton, 1992; Hanski et al., 1993; and 

Sugihara 1994). The possibility that so-called “pathological” nonequilibrium, nonlinear 

behaviors seen in theoretical treatments could be the rule in nature as opposed to a 

mathematical curiosity, opened the door for credible studies of thresholds. Indeed, 

threshold changes now appear to be everywhere. Recognition and documentation of 

sudden, not readily reversible changes in ecosystem structure and function have become a 

major research focus during the past 10 to 20 years (Scheffer et al. 2001; Scheffer and 

Carpenter, 2003). 

One of the important drivers of current interest in nonlinear ecosystem behavior 

and, in particular, threshold effects has been the recognition of the importance of 

unanticipated effects of climate change (Scholze et al. 2006). Although much climate 

change research has focused on the direct effects of long-term changes in climate on the 

structure and function of ecosystems, there has been increasing recognition that the most 

21 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

dramatic consequences of climate change may occur as a result of indirect effects, 

including threshold changes (Vitousek, 1994; Carpenter, 2002; Schneider, 2004; Hobbs 

et al. 2006). 

2.3 Current Discussions of Threshold Phenomena 

As ecologists were exploring the existence of alternative stable states in 

ecosystems, oceanographers were documenting the impacts of major climatic events on 

the North Atlantic Ocean (Steele and Henderson, 1984), North Pacific Ocean, and Bering 

Sea ecosystems. They eventually used the term “regime shift” to describe the sudden 

shifts in biota that are driven by ocean climate events (Steele, 1996; Hare and Mantua, 

2000). More recently, for the California Current Ecosystem (CCE), regime shifts in the 

biota have been distinguished from random excursions in the ocean climate based on the 

nonlinear signature of the time series (Hsieh et al. 2006). The main idea here is that 

regimes represent different rules governing local dynamics (that is, they depend on 

environmental context), and that inherent positive feedbacks drive the system across 

thresholds into different dynamic domains. Thus, regime shifts in marine ecosystems are 

an amplified biological response to ocean climate variation (mainly temperature 

variation) rather than a simple tracking of environmental variation (Anderson et al. 2008). 

On the other hand, ocean climate for the CCE in the 20th century did not have this 

nonlinear signature because the dynamic rules were the same in both warm and cold 

periods. Hsieh et al. (2006) and Anderson et al. (2008) suggest nonlinear forecasting 

methods as a rigorous way to detect thresholds because of the circularities of statistical 

methods. Current interest in regime shifts and thresholds in marine science has focused 

22 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

on understanding the factors that determine thresholds and on ways of extracting 

dynamics from observational data to make predictions. 

Muradian (2001) and Walkers and Meyers (2004) used a definition of regime shift 

developed by Scheffer and Carpenter (2003) emphasizing changes in the threshold level 

of a controlling variable in a system, such that the nature and extent of feedbacks change 

and result in a change in the system itself. Scheffer and Carpenter (2003) built on work in 

shallow lakes to demonstrate empirically the concept of threshold-like change and used 

these examples to further reinforce the idea that ecosystems are never stable but are 

dynamic and that fluctuations (in populations, environmental conditions, or ecosystems) 

are more the rule than not. 

Given the move in thinking among many ecologists toward nonequilibrium and 

unstable dynamics, the broader technical concept that may eventually replace 

“equilibrium” in this context is a more general notion concept that includes equilibrium, 

stable limit cycles, and nonequilibrium dynamics or chaos (Sugihara and May, 1990; 

Hsieh et al. 2006). Depending on whether the control variable is thought of as part of the 

system (an intrinsic variable) or as external to the system (an extrinsic variable), 

threshold behavior may be thought of as a ridge of instability that separates control 

variables. From a more descriptive point of view, the idea suggests that there are 

particular states or characteristic combinations of species (grasslands, chaparral, oakhickory forests, and so forth) that make up the biological component, and that ecosystem 

thresholds can be identified in the physical part of the system. Part of the nonlinearity or 

nonequilibrium nature of ecosystems comes from the fact that the biology (especially the 

23 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

dynamics) of the system is contingent on its own particular state (suite and abundance of 

species), as well as on the physical context in which it resides. 

The field of range science has a parallel and largely independent literature on 

thresholds, resilience, regime shifts, and alternative stable states that has engendered a 

lively debate over how these terms are used in that field. Bestelmeyer (2006) argued that 

there is a lack of clarity in the use of the term “threshold” and its application to state-andtransition models that are used in range management. State-and-transition models 

describe alternative states and the nature of thresholds between states. Bestelmeyer’s 

argument reflects a broad lack of consensus or understanding among range scientists 

about how best to define and use the threshold concept. Watson and others (1996) 

criticized a focus on the consequences of threshold shifts at the expense of the processes 

that precede them. Many definitions of threshold phenomena emphasize relatively rapid, 

discontinuous phenomena (for example, Wissel, 1984, and Denoël and Ficetola, 2007). 

Others emphasize the points of instability at which systems collapse (Radford et al. 2005) 

or the point at which even small changes in environmental conditions lead to large 

changes in state variables (Suding et al. 2004). Still other definitions emphasize changes 

in controlling variables. According to Walker and Meyers (2004), “a regime shift 

involving alternative stable states occurs when a threshold level of a controlling variable 

in a system is passed.” 

There is clearly a need for these concepts to be tested across a wider set of 

ecosystems and have these experiments conducted with greater consistency and rigor to 

better evaluate the veracity of these concepts developed under rangeland conditions to 

other ecosystems and environmental conditions. One point of consensus underlying both 

24 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

the theoretical and empirical approaches to the topic of thresholds is that changes from 

one ecological condition to another take place around specific points or boundaries. But 

further advancement and agreement is limited by the small number of empirical studies 

that address this topic. While some believe that further advancement will depend on 

rigorous statistical testing for reliable identification of thresholds across different systems 

(Huggett, 2005), many in fields outside of range science see the danger of circularity in 

such arguments and suggest dynamic testing for determining threshold behavior (Hsieh et 

al. 2005). 

2.4 Ecological Thresholds Defined for SAP 4.2 

Because of the variety of ways that the concept of thresholds has been developed, 

this assessment (SAP 4.2) uses the following general definition of ecological thresholds: 

An ecological threshold is the point at which there is an abrupt change in an ecosystem 

quality, property, or phenomenon or where small changes in an environmental driver 

produce large, persistent responses in an ecosystem. Fundamental to this definition is the 

idea that positive feedbacks or nonlinear instabilities drive the domino-like propagation 

of change that is potentially irreversible. 

In line with this definition, threshold phenomena are particular nonlinear 

behaviors that involve a rapid shift from one ecosystem state (or dynamic regime) to 

another that is the result of (or that provokes) instability in any ecosystem quality, 

property, or phenomenon. Such instability always involves nonlinear amplification (some 

form of positive feedback) and is often the result of the particular structure of the 

interactions or the complex web of interactions. This definition distinguishes thresholds 

from other biological changes that are simple responses to external environmental 

25 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

change. Thus, bifurcation cascades (the point at which events take one of two possible 

directions with important final consequences, making dynamic systems evolve in a 

nonlinear way with successive disruptions, divergences, or breaks from previous trends), 

nonlinear amplification (Dixon et al. 1999), and the propagation of positive feedback 

(increasing instabilities) through complex webs of interactions are all interrelated 

attributes that fit our general working definition of threshold phenomena. 

“Systemic” risk, or risk that affects the whole ecosystem rather than just isolated 

parts of the system, provides a useful analogy. Systemic risk corresponds to widespread 

change in an ecosystem characterized by a break from previous trends in the overall state 

of the system. Runaway changes are propagated by positive feedbacks (nonlinear 

instabilities) that are often hidden in the complex web of interconnected parts. Recovery 

may be much slower to achieve than the collapse, and the changes may be irreversible, in 

that the original state may not be fully recoverable (Chapin et al. 1995). Our concept of 

threshold transitions include so-called bifurcation cascades where, for example, small 

changes in a controlling variable, such that the nature and extent of feedback change, 

leads to a sudden destabilization of the system. 

Several examples of threshold crossings or transitions that illustrate this definition 

are described in Groffman et al., 2006. These include the interactions of drought and 

overgrazing that trigger runaway desertification, and the exceeding of some critical load, 

as with the toxicity limit of a contaminant or elimination of a keystone species by 

grazing, so that when one component of the system fails, it provokes a domino-like 

cascade of instability that substantially alters the rest of the system. Other examples are 

discussed in more detail in the case studies presented in Chapter 3. 

26 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

2.5 Factors That Influence Resilience 

At a general level, systems can be viewed as consisting of mixtures of positive 

and negative feedbacks, with positive feedbacks tending to alter the nature of the system, 

and negative feedbacks tending to minimize these changes (Chapin et al. 1996). Changes 

that strengthen positive feedbacks (for example, the invasion and spread of highly 

flammable grass in a desert) can lead to a change in conditions (for example, the fire 

regime) that may exceed the tolerance of other components of the system. This, in turn, 

leads to destabilization and threshold changes. Threshold crossings occur when positive 

feedbacks amplify changes in system characteristics in ways that exceed the buffering 

capacity of negative feedbacks that tend to maintain the system in its current state or the 

current limits of the control variables. Viewed from an adaptive management perspective, 

threshold crossings occur when changes in the system exceed the adaptive capacity of the 

system to adjust to change (Groffman et al. 2006). Because systems have adapted to the 

natural variability experienced in the past, anything that disrupts that variability can make 

them vulnerable to further change and amplified instability (Walker et al. 2006; Folke, 

2006). 

The following is a partial list of factors that are believed to come into play in 

determining a system’s resilience, and sensitivity to threshold behavior (see also May and 

McLean, 2007): 

1. A higher diversity of very weakly connected and substitutable components are 

thought to enhance resilience. Such arguments were made in the classic 

stability complexity debate [see reviews by Pimm (1984) and McCann 

(2000)]. 

27 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

2. Compartmentalization of interactions into guilds is a way to make model 

ecosystems more resilient to systemic events (May et al. 2008). 

Compartmentalization acts as a fire-break that prevents the spread of a 

system’s collapse. 

3. A predominance of weak linkages in a system with a few strong linkages 

leads to relatively low connectance (McCann, 2000; May et al. 2008) and is 

thought to increase resilience. Real ecological systems are thought to have a 

lognormal distribution of interaction strengths, which has been associated with 

increased resilience (Sala and Graham, 2002). 

4. Ecosystems are resilient by virtue of their existence. They are the selected 

survivors of billions of years of upheaval and perturbation (continental drift, 

meteor extinctions, and so forth), and show some remarkable constancy in 

structure that persists for hundreds of millions of years (for example, the 

constancy of predator-to-prey ratios). As such, enumerating the common 

attributes of these diverse naturally selected surviving systems, including 

those that change without experiencing thresholds, could be of interest to 

understanding thresholds. 

5. Higher measured nonlinearity (greater instability) in the dynamics that 

provoke an increase in boom and bust population variability (Anderson et al. 

2008) is directly associated with regime shifts. This is true in exploited marine 

fish populations, which show greater swings in abundance than their 

unexploited counterparts from the same environment. Exploited species show 

an amplified response to regime shifts, with greater extremes in abundance. 

28 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

6. In line with the so-called “paradox of enrichment” (Rosensweig, 1971), 

fertilizing a system to increase growth rates and carrying capacity can 

differentially advantage some species and provoke a rapid loss of species to a 

much simpler state. 

7. Increasing time lags involved in population regulatory responses can 

destabilize systems (May 1977), and this effect becomes more pronounced 

with higher growth rates. This is analogous to a large furnace (rapid growth) 

with a poor thermostat (regulatory delay), which tends to produce 

undershooting and overshooting of temperature in a way that predisposes the 

system to large-scale failure. 

8. Reductions in variance, as might occur when managing systems for a stable 

flow of one particular good or service, tends to favor those species and 

components that are typical of this set of conditions at the expense of species 

that function more effectively under other conditions. Consequently the 

system as a whole remains stable under a narrower range of conditions. 

2.6 The Bottom Line 

To manage risks associated with ecological thresholds, it is essential to be able to 

forecast such events and to plan for and study alternative management scenarios. Because 

of the multiscale nature of thresholds, better integration of existing monitoring 

information from the local to the largest possible spatial scales will be required to 

monitor and identify ecosystems that are approaching and undergoing critical transitions. 

Field research that focuses on ecosystems undergoing a threshold shift can help clarify 

the underlying processes at work. The rapid forest dieback in the southwestern United 

29 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

States, described in detail in the next chapter, is an example of a threshold shift for which 

field research identified the trigger (sudden tree mortality) that caused multiple other 

ecosystems changes. And natural resource managers will very likely have to adjust their 

goals for the desired states of resources away from historic benchmarks that may no 

longer be achievable in a nonequilibrium world that is continually changing and now 

being altered by climate change (Julius et al. 2008). Such changes in methods and 

outlook as the following may be required: 

• Abandon classic management models that assume a constant world in 

equilibrium (for example, maximum sustained yield models). 

• Acknowledge in our management strategies and in our models that 

ecosystems are nonlinear, interdependent, and nonequilibrium 

systems. 

• Use near-term forecasting tools, statistical and otherwise, that are 

appropriate to this class of system (for example, nonlinear time series 

prediction coupled with scenario models). 

• Continue to identify the characteristics of systems that make them 

more or less vulnerable. 

• Continue to identify early warning signals of impending threshold 

changes (and to monitor for those signals). 

• Survey the major biomes to identify which systems might be most 

vulnerable to current climatic trends. 

• Employ adaptive management strategies, such as skillful short-term 

forecasting methods coupled with scenario exploration models that are 

30 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

capable of dealing with new successional scenarios and novel 

combinations of species. 

31 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Chapter 3—Case Studies 

Thresholds of ecological change can occur at many spatio-temporal scales and in 

a diversity of ecosystems. The following examples were chosen to illustrate that 

thresholds probably have already been crossed in ecosystems in response to climate 

change and that the crossing of these thresholds will likely have implications at 

continental and global scales. Because these changes will likely impact American society 

significantly, these examples make clear the usefulness of considering thresholds in the 

monitoring and management of natural resources. 

Four case studies are presented below in detail. They cover distinctly different 

types of ecosystems, all of which are potentially undergoing threshold-type changes. 

These studies are arranged in order of latitude, beginning with the highest. The first study 

is at a latitude in the far north where climate change has resulted in large temperature 

changes. The next study is of the mid-latitude Prairie Pothole Region where continental 

drying is expected because the subtropical high-pressure zone is broadening. The third 

case study is of forests of the West and Southwest, which are at slightly lower latitude, 

are generally already water-limited, and will be sensitive to the decreased water 

availability that will profoundly impact the western half of the United States. Finally, in 

the lowest latitude example, the effects of climate change in forcing threshold changes in 

coral reef ecosystems are examined. 

Case Study 1: Ecological Thresholds in Alaska 

In recent decades, Alaska has warmed at more than twice the rate of the rest of the 

United States. The statewide annual average temperature has increased by 3.4°F since the 

mid-20th century, and the increase is much greater in winter (6.3°F). A substantial portion 

32 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

of the increase occurred during the regime shift of the Pacific Decadal Oscillation in 

1976–1977. The higher temperatures of recent decades have been associated with 

changes in the physical environment, such as earlier snowmelt in the spring (Dye, 2002; 

Stone et al., 2002; Dye and Tucker, 2003; Euskirchen et al., 2006, 2007), a reduction of 

sea-ice coverage (Stroeve et al., 2005), a retreat of many glaciers (Hinzman et al., 2005), 

and a warming of permafrost (Osterkamp, 2007). In parallel with these changes in the 

physical environment, substantial changes in ecological systems have been observed, 

including major increases in the frequency of large-fire years in interior Alaska 

(Kasischke et al., 2002), dramatic changes in the wetlands of interior Alaska (Yoshikawa 

and Hinzman, 2003), vegetation changes in the tundra of northern Alaska (Goetz et al., 

2005), and ecological changes that are affecting fisheries in the Bering Sea (Overland and 

Stabeno, 2004; Mueter and Litzow, 2008). Because Alaska is experiencing substantial 

changes in ecological systems, we divide the Alaska case study into four themes that 

focus on (1) changes in insect and wildfire regimes, (2) changes in wetlands, (3) 

vegetation change in northern Alaska, and (4) changes in Bering Sea Fisheries. For each 

of these themes we evaluate the occurrence and implications of threshold responses. 

Ecological Thresholds and Changes in Insect and Wildfire Regimes of Interior 

Alaska.—Analyses of historical insect and fire disturbance in Alaska indicate that the 

extent and severity of these disturbances are intimately associated with longer and drier 

summers (Juday et al. 2005; Balshi et al. 2008). Between 1970 and 2000, the snow-free 

season increased by approximately 10 days across Alaska, primarily because of earlier 

snowmelt in the spring (Euskirchen et al. 2006, 2007). Longer summers have the 

potential to be beneficial to the growth of plants; however, the satellite record suggests 

33 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

that the response of plant growth to warming differs in different regions of the State, with 

aboveground vegetation growth increasing in the tundra of northern Alaska and 

decreasing in the boreal forest of interior Alaska (Jia et al. 2003; Goetz et al. 2005). 

Analysis of forest growth data indicates that the growth of white spruce forests in interior 

Alaska is declining because of drought stress (Barber et al. 2002), and there is the 

potential that continued warming could lead to forest dieback in interior Alaska (Juday et 

al. 2005). The drought stress that has been experienced by trees in Alaska during recent 

decades makes them particularly vulnerable to attack by insects. 

During the 1990s, south-central Alaska experienced the largest outbreak of spruce 

bark beetles in the world (Juday et al. 2005). This outbreak was associated with a 

threshold response to milder winters and warmer temperatures that increased the 

overwinter survival of the spruce bark beetle and allowed the bark beetle to complete its 

life cycle in 1 year instead of the normal 2 years. This was superimposed on 9 years of 

drought stress between 1989 and 1997, which resulted in spruce trees that were too 

stressed to resist the infestation. The forests of interior Alaska are now threatened by an 

outbreak of spruce budworms, which generally erupt after hot, dry summers (Fleming 

and Volney 1995). The spruce budworm has been a major insect pest in Canadian forests, 

where it has erupted approximately every 30 years (Kurz and Apps 1999), but was not 

able to reproduce in interior Alaska before 1990 (Juday et al. 2005). Areas that 

experience the death of trees over large areas of forest are vulnerable to wildfire as the 

dead trees are highly flammable. This is of particular concern in interior Alaska where 

the frequency of large-fire years has been increasing in recent decades. 

34 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

The area burned in the North American boreal region has tripled from the 1960s 

to the 1990s owing to the increased frequency of large-fire years (Kasischke and 

Turetsky 2006). For example, two of the three most extensive wildfire seasons in 

Alaska’s 56-year record occurred in 2004 and 2005, and half of the years with the largest 

fires during this 50-year time period have been since 1990 (Kasischke et al. 2002, 2006; 

Kasischke and Turetsky 2006). The increase in fire frequency in Alaska appears to be 

primarily associated with the shift in the Pacific Decadal Oscillation that occurred in the 

late 1970s, as large-fire years occurred once every 6 years before the shift and increased 

to once every 3 years after the shift (Kasischke et al. 2002). Analyses of fire probability 

in interior Alaska indicate that fire probability increases as a step function when the mean 

temperature in June increases above 14°C or when the August mean precipitation 

decreases below 40 millimeters (mm). Because the mean June temperature has been 

increasing in interior Alaska during the last several decades, the crossing of these 

thresholds will likely lead to substantial increases in area burned in interior Alaska, and 

there is the potential that the large-fire years of 2004 and 2005 in Alaska may occur 

several times a decade instead of once or twice every 50 years. 

Analyses of the response of fire to scenarios of future climate change indicate that 

the average area burned per year in Alaska will double by the middle of the 21st century 

for scenarios of both moderate and high rates of fossil fuel burning (Balshi et al. 2008). 

By the end of the 21st century, fire is projected to triple in Alaska for a scenario of 

moderate rates of increase in fossil fuel burning and to quadruple for scenarios of high 

rates of increase in fossil fuel burning. Such increases have the potential to release large 

stocks of carbon stored in Alaska soils to the atmosphere, which would be a positive 

35 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

feedback to climate warming (Balshi et al. 2008). The projected increase in the burned 

area also increases the fire risk to rural indigenous communities, reduces subsistence 

opportunities, and has implications for fire policy (Chapin et al. 2008). 

Ecological Thresholds and Changes in Wetlands of Interior Alaska.—There has 

been a documented decrease in the area of closed-basin lakes (that is, lakes without 

stream inputs and outputs) during the latter half of the 20th century in the southern twothirds of Alaska (Klein et al. 2005; Riordan et al. 2006). The decrease in lake area 

appears to be caused by greater evaporation associated with longer and drier summers 

and by sudden drainage associated with thawing of permafrost in areas where the 

temperature of permafrost is close to melting. A decrease in the area of closed-basin lakes 

has also been documented in Siberia in areas of “warm” permafrost (Smith et al. 2005). 

Discontinuous permafrost in Alaska is warming and thawing, and extensive areas 

of thermokarst terrain (marked subsidence of the surface resulting from thawing of icerich permafrost) are now developing as a result of climatic change. Estimates of the 

magnitude of the warming at the discontinuous permafrost surface are 0.5° to 1.5°C 

(Osterkamp and Romanovsky 1999). Thermokarst is developing in the boreal forests of 

Alaska where ice-rich discontinuous permafrost is thawing. Thaw subsidence at the 

thermokarst sites is typically 1 to 2 meters (m) with some sites experiencing subsidence 

of up to 6 m (Osterkamp et al. 1997). Much of the discontinuous permafrost in Alaska is 

warm and is highly susceptible to thermal degradation if regional warming continues. 

Warming of permafrost may be causing a significant loss of open water across Alaska as 

thawing of permafrost connects closed watersheds with groundwater (Yoshikawa and 

Hinzman 2003). 

36 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Examination of satellite imagery indicates that the loss of water can occur 

suddenly, which suggests catastrophic drainage associated with thawing of permafrost 

(Riordan et al. 2006). However, the reduction of open water bodies may also reflect 

increased evaporation under a warmer and effectively drier climate as the loss of open 

water has also been observed in permafrost-free areas (Klein et al. 2005). 

In wetland complexes underlain by ice-rich permafrost in areas of hydrologic 

upwelling (for example, wetland complexes abutting up against the foothills of large 

mountain ranges), the thawing of that permafrost may result in wetland expansion as trees 

die when their roots are regularly flooded, causing wet sedge meadows, bogs, and 

thermokarst ponds and lakes to replace forests (Osterkamp et al. 2000). The Tanana flats, 

which extends nearly 70 miles from the northern foothills of the Alaska Range to 

Fairbanks, Alaska, is underlain by ice-rich permafrost that is thawing rapidly and causing 

birch forests to be converted to minerotrophic floating mat fens (Jorgenson et al. 2001). It 

is estimated that 84 percent of a 260,000-hectare (ha) (642,000-acre) area of the Tanana 

flats was underlain by permafrost a century or more ago. About one-half of this 

permafrost has partially or totally degraded. These new ecosystems favor aquatic birds 

and mammals, whereas the previous forest ecosystems favored land-based birds and 

mammals. 

During the past 50 years, it appears that warming has generally resulted in the loss 

of open water in closed-basin lakes in wetland complexes located in areas of 

discontinuous permafrost in the southern two-thirds of Alaska (Riordan et al. 2006). The 

Tanana flats near Fairbanks is the only area where an increase in water area has been 

documented (Jorgenson et al. 2001), and closed-basin lakes in the tundra region of 

37 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

northern Alaska have shown no changes in area during the past 50 years (Riordan et al. 

2006). The loss of area of closed-basin lakes in interior Alaska may be indicative of a 

lowering of the water table that has the potential to convert wetland ecosystems in 

interior Alaska into upland vegetation. A substantial loss of wetlands in Alaska has 

profound consequences for management of natural resources on national wildlife refuges 

in Alaska, which cover about 3.1 million hectares (more than 77 million acres) and make 

up 81 percent of the National Wildlife Refuge System. These refuges provide breeding 

habitat for millions of waterfowl and shorebirds that winter in more southerly regions of 

North America. Reduction of habitat area would present a substantial challenge for 

waterfowl management across the National Wildlife Refuge System (Julius et al. 2008). 

Wetland areas have also been traditionally important in the subsistence lifestyles of 

native peoples in interior Alaska, as many villages are located adjacent to wetland 

complexes that support an abundance of wildlife subsistence resources. Thus, the loss of 

wetland area has the potential to affect the sustainability of subsistence lifestyles of 

indigenous peoples in interior Alaska. 

Ecological Thresholds and Vegetation Changes in Northern Alaska.—Shrub cover in 

northern Alaska has increased by about 16 percent since 1950 (Sturm et al. 2001; Tape et 

al. 2006), and the tree line in Alaska is expanding in most places (Lloyd and Fastie 2003; 

Lloyd, in press). This is consistent with satellite observations, which show an 

approximately 16 percent increase per decade in the normalized difference vegetation 

index (NDVI) (Jia et al. 2003; Goetz et al. 2005). The increased growth of vegetation at 

or above the tree line appears to be a response to longer and warmer growing seasons. 

Tundra vegetation in northern Alaska may not be experiencing drought stress to the 

38 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

extent experienced by forests in interior Alaska because the surface water in tundra 

regions is not able to drain away through the ice-rich continuous permafrost. 

Experimental studies demonstrate that arctic summer warming of 1°C increases shrub 

growth within a decade (Arft et al. 1999). Satellite analyses of relationships between 

NDVI and summer warming (Jia et al. 2003) suggest that the response of tundra 

vegetation is linearly related to summer warmth. Thus, it appears that the response of 

tundra vegetation to warming is not a threshold response. 

While growth of shrubs and trees may not be threshold responses to warming, the 

changing snow cover and vegetation in northern Alaska have the potential to result in 

sudden changes in the absorption of heat from incoming solar radiation and the transfer 

of that heat to warm the atmosphere. For example, the advance in snowmelt reduces 

spring albedo, causing the ecosystem to absorb more heat and transfer it to the 

atmosphere. The snowmelt-induced increase in heating in northern Alaska has been about 

3.3 watts per square meter (W m-2) averaged over the summer, similar in magnitude to 

the 4.4 W m-2 caused by a doubling of atmospheric CO2 over several decades (Chapin et 

al. 2005). Thus, gradual warming has caused a rapid advance in the snowmelt date and a 

very large increase in local heating. Although vegetation changes to date have had 

minimal effects on atmospheric heating, conversion to shrubland would increase summer 

heating by 8.9 W m-2, with even larger changes triggered by conversion to forest. 

Warming experiments that increase shrubs also reduce the abundance of lichens, an 

important winter food of caribou (Cornelissen et al. 2001). Most arctic caribou herds are 

currently declining in population, although the reasons are uncertain. In summary, 

positive feedback associated with earlier snowmelt and shrub expansion is amplifying 

39 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

arctic warming and may be altering food-web dynamics in ways that have important 

cultural and nutritional implications for northern indigenous people. 

Ecological Thresholds and Fisheries of the Bering Sea.—Alaska leads the United 

States in the value of its commercial fishing catch, and most of the Nation’s salmon, crab, 

and herring come from Alaska, and specifically from the Bering Sea. The Bering Sea is 

one of the most productive marine ecosystems in the world, supporting some of the 

largest oceanic populations of fish, seabirds, and marine mammals anywhere (Loughlin et 

al. 1999). The Bering Sea provides 47 percent of total U.S. fishery production by mass, 

including the largest single species fishery in the United States, walleye pollock 

(Theragra chalcogramma) (Criddle et al. 1998). It is also an important source of 

subsistence resources (such as, fish, marine mammals, and seabirds) for more than 30 

Alaska Native communities and supports 95 percent of the worldwide population of 

northern fur seals, 80 percent of the total number of seabirds that breed in the United 

States, and major populations of tens of thousands of Pacific walrus, Steller sea lion, and 

several species of great whales. This production is fueled by nutrients annually 

replenished from slope and oceanic waters across the very broad (more-than-500-

kilometer-wide) continental shelf (Stabeno et al. 2001, 2006). 

Changes in fisheries of the Bering Sea occurred during and after the transition 

from cool to warm conditions in 1976–1977, in association with a regime shift in the 

Pacific Decadal Oscillation, and were followed by historically high commercial catches 

of salmon and pollock, as well as a shift away from crab dominance on the ocean floor 

(Overland and Stabeno 2004). In the past decade, geographic displacement of marine 

mammal populations to the north has been documented in the Bering Sea region (Moore 

40 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

et al. 2003). The displacements of walrus and seal populations are already apparent to 

coastal communities. The northward displacements of fauna in the Bering Sea has 

coincided with a reduction of benthic (organisms that live on or near the ocean floor) prey 

populations, an increase and northward shift in pelagic (those of the open seas and 

oceans) fishes, an increase in air and ocean temperatures, and a reduction in sea ice 

(Stroeve et al. 2005; Grebmeier et al. 2006). Ultimately, populations of fish, seabirds, 

seals, walruses, and other species depend on water temperatures and plankton blooms that 

are regulated by the thickness, extent, and location of the ice edge in spring (Hunt and 

Stabeno 2002). As the sea ice continues to retreat, the location, timing, and species 

makeup of the blooms is changing, subarctic pelagic food webs are replacing arctic ones, 

and the amount of food reaching the living things on the ocean floor, the benthos, is 

declining dramatically. This in turn radically changes the species makeup and populations 

of fish and other marine life forms, with significant repercussions for fisheries (Anderson 

and Piatt 1999; Litzow et al. 2008; Hatfield et al. 2008; Julius et al. 2008). Reductions in 

sea-ice cover also result in reduced albedo (reflectance of solar radiation), greater sea 

surface temperatures, and accelerated sea-ice retreat, a positive feedback loop that is at 

least partly responsible for the unexpected and record-setting extent of open water in the 

Arctic Ocean in recent years. Thus, changes in sea ice are the major driver of concern 

with respect to threshold changes in fisheries of the Bering Sea (Mueter and Litzow 

2008). 

Seasonal sea-ice extent currently divides the Bering Sea eastern shelf into two 

biogeographic provinces, which differ in production pathways. In the subarctic 

biogeographic province (south of the average annual maximum extent of the sea ice), 

41 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

most primary production remains within the pelagic ecosystem, and pollock is the 

dominant tertiary consumer (Macklin and Hunt 2004). In contrast, in the arctic 

biogeographic province, tight coupling between pelagic primary production and the 

benthos benefits benthic foragers, such as gray whales, walrus, and diving ducks 

(Lovvorn et al. 2003; Grebmeier et al. 2006). The boundary between the two 

biogeographic provinces varies in location on longer time scales (decadal or longer) and 

is expected to move northward as the region becomes warmer. The average southern edge 

of the maximum ice extent currently lies north of the Pribilof Islands (Byrd et al. 2008). 

The Bering Sea ecosystem, however, is in a state of rapid flux due to climate 

change. Present data and climate projections from atmosphere-ocean models predict 

major loss of sea ice during the next few decades (Overland and Stabeno 2004; Holland 

et al. 2006); the Bering Sea is particularly sensitive to global warming because of the 

seasonal nature of sea-ice cover (Grebmeier et al. 2006). Recent relative temperature 

extremes (above 2°C) in Alaska and adjacent waters represent the largest recent change 

on the planet (Hansen et al. 2006). However, these models and empirical data also 

demonstrate large natural variability. Ecosystems will likely be affected by how the path 

of such warming occurs—that is, whether there will be a continued slow warming trend 

with little interannual variability (in which case crossing of ecological thresholds is less 

likely) versus a warming trend that incorporates wide swings in temperature and extent of 

sea ice (enhancing the likelihood of threshold crossings). Climatic and oceanographic 

conditions in the Bering Sea during 2007–2008 were unexpectedly cold, supporting the 

latter scenario. 

42 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Warming of the Bering Sea is altering the geographic distributions and behaviors 

of humans, marine mammals, seabirds, and fish by restructuring their habitats and food 

webs (Grebmeier et al. 2006; Mueter and Litzow 2008). As a result of warming, changes 

in the time and place of food production lead to dominance of top-down control processes 

in the pelagic marine environment and the decline of benthic production. Under a longterm warming scenario with early ice retreat, bottom-up control mechanisms 

(temperature, sea-ice extent and duration, ocean currents, and nutrient fluxes) set the 

stage for the emergence and dominance of top-down control processes in the pelagic 

marine environment and the decline of benthic production (Mueter and Litzow 2008), a 

threshold change akin to that was documented after the 1976–1977 regime shift in the 

Pacific Decadal Oscillation. Increased heat content would increase the combined 

populations of the subarctic piscivores—arrowtooth flounder, pollock, and cod—in 

proportion to expanded breeding grounds and increased availability of food during 

critical developmental stages (Hunt and Stabeno 2002). Because arrowtooth flounder is 

not targeted by fishing, it is likely to become the dominant component of the biomass of 

the three subarctic piscivores in this system and is predicted to be one of the principal 

agents of top-down control in the Bering Sea, as predator and competitor of the nowdominant, but commercially exploited, pollock and cod. Such a rapid and dramatic 

restructuring of subarctic marine communities is not unprecedented; the 1976–1977 

regime shift in the Pacific Decadal Oscillation resulted in threshold community 

reorganization in the Gulf of Alaska (Anderson and Piatt 1999). 

Arrowtooth flounder is also an agent of change as a direct and indirect competitor 

of fur seals, murres, kittiwakes, and other top trophic-level piscivores for their respective 

43 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

forage species (juvenile pollock, capelin, sand lance, herring, and myctophids). 

Populations of fur seals, murres, and kittiwakes could decline or increase in the near 

term, depending on the locality of rookeries and nesting colonies, but long-term overall 

trends would be downward under warming. Fur seals, murres, and kittiwakes would 

further decline owing to competition from humpback and fin whales, with fur seal 

declines being further accelerated by increasing killer whale predation. Dislocation of 

feeding hot spots would likely disadvantage breeding fur seals, murres, and kittiwakes as 

central place foragers, but would work to the advantage of humpback and fin whales, 

further exacerbating direct and indirect competition between these two groups of species. 

Dislocations and declines in fur seals, kittiwakes, murres, pollock, and cod would stress 

human communities by increasing the costs of maintaining a livelihood and obtaining 

food and by necessitating changes in the types of food taken and the means of harvest. 

Both commercial fishers based in Dutch harbor and subsistence fishers based in over 30 

Native Alaskan communities on the shores of the Bering Sea are facing greater 

commuting distances and higher risks to exploit fisheries resources that were formerly 

close to home. 

The northern Bering Sea, in particular, is experiencing a rapid shift in the 

structure and function of the formerly arctic community to conditions typical of marine 

ecosystems of the subarctic (Hunt et al. 2002; Grebmeier et al. 2006). The earlier sea-ice 

retreat results in a later, warm-water spring phytoplankton bloom, increased grazing by 

zooplankton, and greater pelagic secondary productivity (Hunt et al. 2002). Concurrently, 

benthic productivity is decreasing (Grebmeier et al. 2006). The formerly ice-dominated, 

shallow marine ecosystem that favored highly productive benthic communities also 

44 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

supported high densities of upper trophic-level bottom-feeders, such as Pacific walruses, 

gray whales, and seaducks, including the Endangered Species Act (ESA)-listed 

spectacled eider. 

The northward flowing Anadyr Current, which originates in the southern Bering 

Sea, transports nutrient-rich water far onto the Bering Shelf and the northern Bering Sea. 

This largely wind-forced transport creates highly productive shelf waters in the area north 

of St. Lawrence Island and south of the Bering Strait, known as the Chirikov Basin 

(Springer et al. 1989; Piatt and Springer 2003). Oceanic copepods, such as Neocalanus 

cristatus and N. flemingeri, transported by the Anadyr Current, along with the large 

euphausiid Thysanoessa raschii provide abundant prey for planktivores foraging near St. 

Lawrence Island (Piatt et al. 1988). The Anadyr Current is highly variable on a seasonal 

and annual basis, usually reaching its greatest velocity during July (about 1.3 Sv, or 13 

million cubic meters per second) (Roach et al. 1995). Consequently, the primary 

productivity on the Bering Shelf during summer months varies with the strength of 

northward flow associated with the Anadyr Current (Springer et al. 1989; Russell et al. 

1999). 

When the Anadyr Current is weaker, planktivores presumably rely more on 

zooplankton associated with northern Bering Shelf waters, such as the small copepod 

Calanus marshallae and the large amphipod Themisto libellula (Coyle, Chavtur, and 

Pinchuk 1996; Russell et al. 1999). Neocalanus copepods are larger and have higher 

energy content per prey item than the small, neritic copepod C. marshallae, which is 

characteristic of Bering Shelf water. The lipid content of Neocalanus copepods is also 

probably higher (Obst et al. 1995), making these oceanic species more energy-dense than 

45 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

their shelf domain counterparts. When preferred Neocalanus copepods are not available, 

planktivores must switch to other prey types. The progressively earlier transition from 

winter to spring in the Bering Sea, changes in prevailing weather patterns and associated 

wind forcing, and the resulting changes in primary and secondary productivity are 

expected to have large impacts on upper trophic-level consumers that rely on the Anadyr 

Current (Stabeno and Overland 2001; Grebmeier et al. 2006). 

Projected warming of the Bering Sea is also expected to profoundly alter the 

structure of the southeastern Bering Sea ecosystem by changing pathways and fluxes of 

energy flow, as well as the abundance, spatial distribution, and species composition of 

fish, seabirds, and marine mammals, thereby affecting commercial and subsistence 

fisheries that support local, regional, and national economies (Hunt and Stabeno 2002; 

Grebmeier et al. 2006; Mueter and Litzow 2008). Climate-induced changes in physical 

forcing of the Bering Sea modify the partitioning of food resources at all trophic levels on 

the continental shelf through bottom-up processes. The emergent properties of this 

formerly seasonal sea-ice-dominated marine ecosystem under warming are still the 

subject of intense scientific inquiry, but the weight of evidence suggests that the Bering 

Sea ecosystem has reached a threshold of major ecosystem change and community 

restructuring. 

Case Study 2. The Mid-Continent Prairie Pothole Region: Threshold 

Responses to Climate Change 

The Prairie Pothole Region of north-central North America is one of the most 

ecologically valuable freshwater resources of the Nation (van der Valk, 1989). It contains 

5 million to 8 million wetlands, which supply critical habitat for continental waterfowl 

46 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

populations and provide numerous valuable ecosystem services for the region and Nation. 

The weather extremes associated with this region are particularly important for the longterm productivity of waterfowl dependent on these wetlands. 

The Prairie Pothole Region (fig. 3.1) exhibits a variable climate, ranging from 

severe droughts, exemplified by the 1930s when agriculture was devastated, grassland 

communities shifted eastward, and trees died by the millions (Albertson and Weaver, 

1942, 1945; Woodhouse and Overpeck, 1998; Rosenzweig and Hillel, 1993), to periods 

of deluge, such as occurred in the late 1900s when closed-basin lakes flooded, causing 

high mortality of shoreline trees and considerable economic damage to farmland, roads, 

and towns (Winter and Rosenberry, 1998; Johnson et al. 2005; Shapley et al. 2005). The 

20th-century climate of the Prairie Pothole Region was punctuated by significant 

droughts. These conditions have occurred over small and large areas and lasted as short 

as several growing seasons to as long as a decade (Skaggs, 1975; Laird and Cumming, 

1998; Nkemdirim and Weber, 1999). 

47 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Figure 3.1. Location of the Prairie Pothole Region (Prairie Pothole Region) of North America (red 

highlighted area). (Boldsethet et al. 2007) 

Wetlands in the Prairie Pothole Region are likely to be strongly affected by 

gradual changes in climate (Poiani and Johnson, 1991; Covichet et al. 1997). Climate 

drives surface processes, such as the hydrologic cycle, and hydrology is the most 

important factor that controls key wetland processes and services (Winter and Woo, 

1990). A warmer and drier climate, as indicated by general circulation models for the 

northern Great Plains (Ojima and Lackett, 2002), could affect the wetland hydroperiod, 

the ratio of emergent plant cover to open water, the species composition, wetland 

permanence, and primary and secondary productivity, among others (van der Valk, 

1989). Winter (2000) predicted that the surface area of seasonal and semipermanent 

wetlands in the Prairie Pothole Region would be reduced by increases in 

evapotranspiration and reduced summer soil moisture. With increased temperatures, 

48 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

summer evapotranspiration would put increasing demands on groundwater, resulting in 

earlier drying of wetlands. Thus, additional climate variability of the magnitude 

suggested by global climate change models would profoundly affect wetland water 

budgets and the many processes and attributes linked to these wetlands. 

Changing climate can have direct effects on the trajectories of these wetland 

ecosystems and their sustainability. Shifts in climate in this region over decadal time 

scales could result in longer or more frequent drought periods and may lead to threshold 

responses by the wetland systems. The interaction of extrinsic and intrinsic processes 

reflected in such hydrologically, geologically, and biologically linked systems as 

wetlands and their surrounding watersheds could result in rapid nonlinear changes at 

broad spatial scales that are triggered by small differences in temperature and 

precipitation if threshold values are exceeded that may also result in these systems 

exhibiting hysteresis. 

The first quantitative assessments of the possible effects of climate change on 

Prairie Pothole Region wetlands used the WETSIM (WETland SIMulator), which is a 

rule-based, spatially explicit simulation model that is composed of hydrology and 

vegetation submodels (Poiani and Johnson, 1991, 1993a, b; Poiani et al. 1995, 1996). 

Simulations using this model and general circulation model climate forcings indicate that 

semipermanent wetlands would lose their historic highly dynamic character by drying up 

more frequently and becoming chronically choked with emergent cover. Shortened 

hydroperiods and continuous stands of emergent cover for semipermanent wetlands 

across the Prairie Pothole Region would have strong negative effects on the continental 

population of water birds (particularly ducks). 

49 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Johnson et al. (2005) used a simulation model (WETSIM) to contrast historical 

and future wetland conditions across the Prairie Pothole Region of North America (fig. 

3.1). They assembled 95-year climate data sets for 18 weather stations across the Prairie 

Pothole Region as input to a revised version of WETSIM (version 3.1), which enabled a 

much broader geographic assessment to be conducted of the effects of past and future 

climate variability on wetland conditions across the Prairie Pothole Region. Their model 

runs reflected the high level of spatial and temporal heterogeneity in wetland water levels 

historically across the Prairie Pothole Region. They were able to use model output to 

simulate the number of completions of the wetland cover cycle across the Prairie Pothole 

Region (fig. 3.2; Weller, 1965). 

Figure 3.2. Wetland cover cycle (modified from Weller, 1965). 

The wetland cover cycle was highly sensitive to alternative future climates. The 

geographic pattern of return times shifted markedly with changes in temperature and 

precipitation. A 3°C increase in temperature and no change in precipitation resulted in a 

50 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

greatly diminished area and geographic shift eastward for the region of fastest return 

times. However, reduced precipitation and warmer air temperatures resulted in no 

complete cover cycle return times across the Prairie Pothole Region except in a small 

area of north-central Iowa (fig. 3.3), thus representing a threshold response to climate 

change. Such dramatic shifts in wetland conditions emphasize the sensitivity of Prairie 

Pothole Region wetlands to climate variability. 

Using this information, Johnson et al. (2005) simulated the occurrence of highly 

favorable water and cover conditions for breeding waterfowl (fig. 3.4). The most 

productive habitat for breeding water birds would shift under an effectively drier climate 

from the center of the Prairie Pothole Region (the Dakotas and southeastern 

Saskatchewan) to the wetter eastern and northern fringes (in sync with the changes in the 

cover cycle return results). 

Continental waterfowl population cycles are largely dictated by regional wetland 

conditions, with population declines being commonplace during periods of drought and 

then rebounding during wetter periods. Under a warmer, drier climate, wetlands would be 

especially vulnerable even if precipitation were to continue at historic levels (Johnson et 

al. 2005). The geographic shifts in the most favorable region for waterfowl breeding 

resulting from the model simulation runs will likely affect the rate at which the threshold 

for waterfowl population sustainability will be reached. 

51 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

A B

C D

Figure 3.3. Geographic patterns of the speed of the wetland cover cycle, simulated for the Prairie Pothole 

Region under historic (a) and alternative future (b, c, and d) climatic conditions. (Johnson et al. 2005) 

Figure 3.4. Simulated occurrence of highly favorable water and cover conditions for waterfowl breeding 

(occurrence of at least one return time and hemi-marsh conditions at more than 30 percent frequency) 

across the Prairie Pothole Region under historic (a) and alternative (b, c, and d) future climatic conditions. 

(Johnson et al. 2005) 

52 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Case Study 3. Broad-Scale Forest Die-Back as a Threshold Response to 

Climate Change in the Southwestern United States 

The ecological dynamics of semiarid forests and woodlands in the southwestern 

United States are observed to respond strongly to climate-driven variation in wateravailability, with major pulses of woody plant establishment and mortality commonly 

corresponding to wet and dry periods (Swetnam and Betancourt, 1998). Although human 

management of these forests is also a factor in tree mortality, it is clear that climateinduced water stress can trigger rapid, extensive, and dramatic forest dieback (Breshears 

et al. 2005), exemplifying significant ecosystem threshold responses to climate. Broadscale tree mortality can shift ecotones between vegetation types (Allen and Breshears 

1998) and alter regional distributions of overstory and understory vegetation (Gitlin et al. 

2006; Rich et al. 2008). Rapid forest dieback also has nonlinear feedbacks at multiple 

spatial scales with other ecological disturbance processes, such as fire and erosion (Allen, 

2007), which, in some cases, leads to additional nonlinear threshold behaviors. Massive 

forest mortality is an example of a threshold phenomenon with substantial implications 

for future ecosystem dynamics and management of lands undergoing such changes 

(Millar et al. 2007). 

Assessments of potential global change impacts initially focused on how 

vegetation types matched given climatic envelopes (IPCC, 1996). Subsequent research 

has considered how vegetation patterns might migrate in response to a changing climate 

with a focus on rates of plant establishment, has documented that forest turnover rates 

follow global and regional patterns of productivity (significantly driven by climate) 

(Stephenson and van Mantgem, 2005), and has increasingly moved toward dynamic 

53 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

global vegetation models that try to incorporate more realistic disturbance dynamics 

(Scholze et al. 2006; Purves and Pacala, 2008). Currently, climate-induced dieback of 

woody plants is being recognized as an important vegetation response to climate variation 

and change, with examples of forest dieback emerging from around the world (Allen and 

Breshears, 2007). [It should also be noted that other recent studies have documented 

increased tree growth in dry forests, perhaps because of increased water use efficiency 

(Soule and Knapp 1999).]. Recent research shows that water stress appears to be driving 

increases in background tree mortality rates in western North American forests (van 

Mantgem and Stephenson, 2007). In addition, observations of extensive tree die-off—

especially from semiarid ecosystems where woody plants are near their physiological 

limits of water stress tolerance—are being documented globally, for example, in 

Australia (Fensham and Holman, 1999), Africa (Gonzalez, 2001), west Asia (Fisher, 

1997), Europe (Dobbertin et al. 2007), South America (Suarez et al. 2004), and North 

America (Breshears et al. 2005). Climate-induced water stress over extended time periods 

can exceed the physiological tolerance thresholds of individual plants and directly cause 

mortality through either 1) cavitation of water columns in the xylem conduits (“hydraulic 

failure”) or 2) forcing plants to shut down photosynthesis to conserve water, leading to 

“carbon starvation” (McDowell et al. 2008). These individual-scale threshold responses 

to climate stress can trigger tree mortality that propagates to landscape and even regional 

spatial scales (Allen, 2007), sometimes amplified by biotic agents (like bark beetles) that 

can successfully attack and reproduce in weakened tree populations and generate massive 

insect population outbreaks with positive feedbacks that greatly increase broad-scale 

forest mortality (Kurz et al. 2008). 

54 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Ecotones are areas where vegetation changes in response to climate are expected 

to be most rapid and prominent (Beckage et al. 2008), as highlighted by a southwestern 

case study of drought effects on vegetation during the 1950s (fig. 3.5; Allen and 

Breshears, 1998). Severe drought across the southwestern United States during the 1950s 

caused ponderosa pine (Pinus ponderosa) trees at lower, drier sites to die, resulting in an 

upslope shift of the ponderosa pine forest and piñon-juniper woodland ecotone of as 

much as 2 kilometers (km) in less than 5 years, producing a rapid and persistent change 

in dominant vegetation cover. Similarly, within the distributional range for the piñon pine 

(Pinus edulis), many trees at lower or drier sites also died (Swetnam and Betancourt, 

1998). 

Allen and Breshears 1998, PNAS

Evidence of 1950s dieback:

-remnant dead wood

-air photos

-documents

Figure 3.5. Changes in vegetation cover between 1954 and 1963 at Frijolito Mesa, Jemez Mountains, New 

Mexico, showing the persistent ponderosa pine forest (365 ha), the persistent piñon-juniper woodland 

(1527 ha), and the ecotone shift zone (486 ha) where forest changed to woodland (from Allen and 

Breshears, 1998). 

55 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Although tree mortality almost certainly occurred across much of the 

southwestern United States in response to the 1950s drought (and probably for previous 

regional-scale droughts as well), few studies exist that allow scientists to test projections 

about the rapidity and extent of potential vegetation die-off responses to drought. A 

recent drought beginning in the late 1990s and peaking in the early 2000s affected most 

of the western United States. This was the most severe drought in the Southwest since the 

1950s. Substantial mortality of multiple tree species has been observed throughout the 

Southwest during this 2000s drought (fig. 3.6; Gitlin et al. 1996; U.S. Forest Service, 

2006; Allen, 2007). For example, mortality of the piñon pine spanned major portions of 

the species’ range, with substantial die-off occurring across at least 1,000,000 ha from 

2002 to 2004 (Breshears et al. 2005; U.S. Forest Service, 2006). For both droughts, much 

of the forest mortality was associated with bark beetle infestations, but the underlying 

cause of dieback appears to be water stress associated with the drought conditions. 

56 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

0

500

1000

1500

2000

1997 1998 1999 2000 2001 2002 2003

Year

Acres (1,000)

Ponderosa Pine

0

500

1000

1500

2000

2500

3000

3500

1997 1998 1999 2000 2001 2002 2003

Year

Acres (1,000)

Pinyon Pine

17.6 32.4 6.1 13.3 38.4

USFS aerial 

surveys for forest 

insect and disease 

activity, 

cumulative map 

of affected areas 

in Southwest for 

2000-2003.

Figure 3.6. Graph of the acreage of piñon pine (Pinus edulis) and ponderosa pine (Pinus ponderosa)

dieback from 1997–2004 in the Four Corners States of Arizona, New Mexico, Colorado, and Utah; map 

showing cumulative area from 2000 to 2004. Based upon annual aerial forest insect and disease activity 

inventories by the U.S. Forest Service. 

The precipitation deficit that triggered the recent regional-scale die-off of the 

piñon pine across the Southwest was not as severe (dry) as the previous regional drought 

of the 1950s, but the recent 2000s drought was hotter than the 1950s drought by several 

metrics, including mean, maximum, minimum, and summer (June–July) mean 

temperature (Breshears et al. 2005). Although historic data from the 1950s is limited, 

available data suggest that piñon pine mortality in response to the recent drought has been 

more extensive, affected greater proportions of more age classes, and occurred at higher 

elevation and wetter sites than in the 1950s drought. Hence, the warmer temperatures 

associated with the 2000s drought may have driven greater plant water stress through 

increased evapotranspirational demand and resulted in more extensive tree die-off. 

Because global change is projected to result in droughts under warmer conditions 

57 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

(referred to as “global-change type drought”) the severe piñon pine dieback from the 

recent drought may be a harbinger of vegetation response to future global-change type 

droughts (Breshears et al. 2005). 

In addition to the die-off of dominant overstory tree species, high levels of 

dieback also were observed in other Southwestern U.S. species and life forms in response 

to the warm regional drought in the 2000s (Gitlin et al. 2006; Allen, 2007). These include 

species where bark beetles are unimportant or nonexistent, including one-seed juniper 

(Juniperus monosperma)—a co-dominant with piñon pine for much of its range; shrubs 

such as wavy-leaf oak (Quercus undulate) and mountain mahogany (Cercocarpus 

montanus); and blue grama (Bouteloua gracilis), the dominant herbaceous species in 

many of these woodland systems. 

In addition to direct climate-induced mortality, severe protracted drought also can 

cause substantial reductions in the productivity and soil surface cover of herbaceous 

plants, which in turn affects numerous other ecological processes. In particular, 

reductions in herbaceous ground cover can trigger a nonlinear increase in soil erosion 

once a threshold of decreased herbaceous cover has been crossed, through increased 

connectivity of bare soil patches (fig. 3.7; Davenport et al. 1998; Wilcox et al. 2003; 

Ludwig et al. 2005). On the other hand, dieback of woody canopies tends to cause an 

immediate successional shift toward greater cover of understory vegetation if moisture 

conditions are adequate (for example, Rich et al. 2008), which propagates a different set 

of effects. 

58 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

CLIMATE (drought, temperature);

OVERGRAZING by livestock

High tree density, death of 

individual trees

Patchy standscale mortalityLandscape-scale

forest mortality

FOREST

DIEBACK

FIRE

Fine Intermediate Broad

S P A T I A L S C A L E

Beetle outbreak overwhelms 

even healthy trees

Beetle population grows rapidly,

colonizing more trees

Physiological tree stress, 

beetles attack weak and dying trees 

Within-patch

fuel connectivity

Fire ignition and surface fire 

spread

Torching and between-patch fire 

spread

Long-distance fire spread beyond L 

fuel connections

Explosive crown fire. 

Self-generated weather

EROSION

r Grass cover,  within-patch bare 

connectivity

 Runoff and erosion,

drier microsite, within IC patch

 Hillslope runoff/erosion

Percolating network 

of bare IC patches

 L-scale connectivity 

of sediment flux

 Net loss of water and soil from 

watershed

Treefall

Between-patch

fuel connectivity

Figure 3.7. Diagram representing interactions across spatial scales for three different disturbance processes 

(forest dieback, fire, and erosion) in northern New Mexico landscapes (from Allen 2007). Dashed black 

arrows represent pattern-process feedbacks within three different spatial-scale domains, with one example 

of pattern and process shown for each domain for each disturbance. Solid black arrows indicate the 

overarching direct effects of widespread environmental drivers or disturbances (such as climate and 

overgrazing) on patterns and processes at all scales. Blue arrows indicate the point at which altered 

feedbacks at finer spatial scales induce changes in feedbacks at broader scales (for example, fine-scale 

changes cascade to broader scales), and also where changes at broader scales overwhelm pattern-process 

relationships at finer scales. Red dashed arrows illustrate some examples of amplifying (positive feedback) 

interactions between disturbance processes within and between spatial scales; green dashed arrows 

illustrate dampening (negative feedback) interactions between disturbance processes. Abbreviations: L = 

landscape; IC = intercanopy (interspaces between tree canopies). 

Overall, the dieback of overstory vegetation affects numerous key ecosystem 

processes, which are tied to site-specific distributions of incoming energy and water (Zou 

et al. 2007), and has multiple cascading ecological effects. Widespread tree mortality 

may propagate additional pervasive changes in various ecosystem patterns and processes. 

Breshears (2007) summarizes the important ecological role of woody plant mosaics in 

semiarid ecosystems: 

A large portion of the terrestrial biosphere can be viewed as lying within a 

continuum of increasing coverage by woody plants (shrubs and trees), ranging 

59 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

from grasslands with no woody plants to forests with nearly complete closure 

and coverage by woody plants (Breshears & Barnes, 1999; Breshears, 2006). 

The characteristics of woody plants determine fundamental descriptors of 

vegetation types including grassland, shrubland, savanna, woodland, and 

forest. Because woody plants fundamentally affect many key aspects of 

energy, water and biogeochemical patterns and processes, changes in woody 

plant cover are of particular concern (Breshears, 2006). 

Climate-driven, rapid forest dieback has feedbacks with other ecological 

disturbance processes, such as fire and erosion, in some cases leading to further nonlinear 

ecosystem threshold behaviors (fig. 3.7). Warming and drying climate conditions are 

driving higher-severity fire activity at broader scales in the southwestern United States 

directly (Swetnam and Betancourt, 1998; Westerling et al. 2006), and probably also 

indirectly where forest dieback changes fuel conditions (fig. 3.7: Bigler et al. 2005). 

High-severity stand-replacing fires within woodlands and forests can almost instantly 

cause large reductions in tree canopies and soil surface covers, thereby also triggering 

dramatically increased rates of runoff and soil erosion for several years post-fire until 

vegetation regrowth restores adequate land surface cover (Veenhuis, 2002). Forest 

dieback, fire, and erosion also have significant effects on ecosystem carbon pools 

(Breshears and Allen, 2002; Kurz et al. 2008). The combined interactive effects of 

climate-driven ecological disturbance processes (vegetation dieback, fire, and erosion) 

are highlighted by the major changes in woodland and forest ecosystems that have 

occurred in northern New Mexico during the past 60 years (fig. 3.8; Allen, 2007). 

Climate-induced forest dieback, fire, and accelerated erosion already may be causing 

60 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

permanent type-conversion changes to some southwestern ecosystems. Even without 

factoring in ongoing or predicted climate changes, it will be at least several decades to 

centuries before reestablishment of predisturbance tree canopy covers will occur on many 

semiarid woodland and forest sites in this region (Allen and Breshears, 1998; Savage and 

Mast, 2005). 

Figure 3.8 Increased herbaceous cover has developed since recent piñon pine forest dieback in the Jemez 

Mountains of New Mexico and may promote surface fire regimes and changes in runoff and erosion 

patterns. July 2004. 

Examples of drought-induced tree die-off in semiarid forests and woodlands 

highlight the rapidity and extensiveness with which climate stress can trigger pervasive 

and persistent ecosystem changes. Climate change has the potential to drive multiple 

nonlinear or threshold-like processes that can interact in complex ways, including tree 

mortality, altered fire regimes, energy and water budget changes, and soil erosion 

thresholds (Allen, 2007), making ecological predictions difficult (McKenzie and Allen, 

2007). For example, the projections of state-of-the-art dynamic global vegetation models 

61 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

“are currently highly uncertain, making vegetation dynamics one of the largest sources of 

uncertainty in Earth system models” (Purves and Pacala, 2008). Additional research, 

including research on threshold responses, is needed to improve projections of the 

nonlinear ecological effects of expected climate changes, such as broad-scale forest 

dieback, associated ecosystem dynamics, and effects on carbon budgets and other 

ecosystem goods and services (Breshears and Allen, 2002; Millennium Ecosystem 

Assessment, 2005; Millar et al. 2007). 

Case Study 4. Thresholds in Climate Change for Coral-Reef Ecosystem 

Functioning 

Corals are perpetually subjected to environmental changes in time and space. As 

adult colonies, corals are sessile, remaining in one location over time, and therefore, are 

subjected to changes in environmental factors through a temporal scale. As larvae, corals 

are motile, and each must select a location from a complex and variable array of available 

sites. Corals are resilient to changes, both spatially and temporally, through 

acclimatization, adaptation, local environmental ameliorations, initial community 

composition, and the morphological characteristics of the reef. It is reasonable to assume 

that most corals will not go extinct with global climate change because of their abilities to 

acclimatize, to adapt, and to broadcast their larvae over a large scale landscape (Paulay 

1997). Systems consist of mixtures of positive and negative feedbacks, with positive 

feedbacks tending to alter the nature of the system, and negative feedbacks tending to 

minimize these changes (Chapin et al. 1996). The threshold, or tipping point, for coralreef ecosystems is the point along the environmental gradient at which the ecological or 

biological processes change from negative feedback for net accretion to positive feedback 

62 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

or reef erosion. When net accretion decreases to a point of net erosion of the reef, the 

resiliency of the system to return to a functioning coral ecosystem has been greatly 

reduced, potentially affecting the rate of reaching a threshold of coral mortality. Natural 

stressors, which are the results of anthropogenic stressors (for example, overfishing, 

pollutants, sedimentation, habitat destruction), that can lead to positive feedbacks, 

potentially decreasing the threshold level of coral mortality, include the following 

(Birkeland 2004): 

• inverse density dependence (or Allee effect); 

• algal abundance at levels beyond the capacity of herbivores to keep in 

balance; 

• predation of corals at a rate higher than the rate of recovery and coral 

population replenishment; 

• bioerosion of corals; 

• the prevalence of crustose coralline algae, which weakens binding of 

the substratum, is decreased and thereby decreases successful coral 

recruitment; and 

• invasive species—the establishment of introduced species, which 

modify the habitat in ways that favor the survival and dominance of 

the introduced species and displacement of natural species. 

Such processes as these stressors and the feedback mechanisms of corals to these 

stressors have determined the substantial degradation of coral reefs over the past 3 

decades in the tropical western Atlantic Ocean (Gardner et al. 2003) and in the IndoPacific Ocean (Bruno and Selig, 2007). It is likely that the crossing of thresholds in coral 

63 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

ecosystems began nearly 3 decades ago with no evidence that the rate of degradation is 

decreasing (Birkeland 2004). 

Although anthropogenic modification of local ecological processes has been the 

dominant force in coral-reef degradation (Birkeland, 2004) and tipping points have been 

crossed decades ago in many areas (Gardner et al. 2003; Bruno and Selig, 2007), global 

changes in climate and oceanic characteristics are now becoming more apparent. Global 

processes that are affecting coral reefs are sea-level rise, the decline in pH of seawater, 

and the increase in seawater temperature, which are related to the increased concentration 

of atmospheric CO2. 

Rise and Fall of Sea Level.—Coral reef ecosystems have experienced rise and fall 

of sea levels several times in geological history with associated effects on reef 

functioning (Hallock 1997) (with “reef functioning defined as constructing reefs 

upwardly). Reef accretion has stopped for periods of time in excess of 10 million years 

(Copper 1994, Hallock 1997), the threshold for the cessation of reef upward growth being 

the time of decreasing sea level (Hallock 1997, Hubbard 1997). It is hard to determine the 

effect of climate change alone on whether corals will keep pace with sea level rise, 

increasing water temperatures, and change in ocean pH. The rate of sea level rise alone 

does not provide a predictable tipping point for reef deposition that can be generalized 

over a region (Hallock et al. 1993, Kleypas et al. 2001, Garrison et al. 2003). Whether 

coral reefs keep up with sea level rise depends on a multitude of local environmental 

factors and the degree to which these factors stress the corals themselves which will 

affect the rate at which the threshold for coral mortality will be reached (Hubbard 1997). 

64 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Decrease in Seawater pH.—The concentration of CO2 in the atmosphere is 

generally expected to reach two times the preindustrial (late 18th century) levels by 2065 

(Houghton et al. 1996). As CO2 concentration increases in the atmosphere, the surface 

seawaters take up more CO2. The increased uptake of atmospheric CO2 by the surface 

waters of the ocean leads to a decrease in pH of surface waters, an increase in the 

proportion of bicarbonate ions ( HCO3

−

), and a decrease in the proportion of carbonate 

ions (CO )(Feely et al. 2008). The overall effect on the rate of precipitation of coral 

skeleton is negative. 

2

3

−

2

3 3 CO HCO H CO H2 − + − +⇔ + +2 2 + ⇔ H O

The oceans have already taken up an additional one-third to one-half of industrialage emissions of CO2, and the concentrations of carbonate ions in the oceans have 

decreased from 11 percent (preindustrial), to 9 percent (now) and are projected to 

decrease to 7 percent when carbonate concentrations are double the preindustrial 

concentrations, perhaps in 3 to 5 decades (ISRS, 2007). 

Kleypas and others (1999) determined that doubled atmospheric CO2 will lead to 

a 14 percent to 30 percent decrease in reef calcification rates. This was estimated to be a 

general threshold from net carbonate accretion to net carbonate loss by Kleypas and 

others (2001). Net reef accretion is potentially reduced to zero when increased CO2 in the 

atmosphere reaches about 500 to 600 ppm. On the other hand, CO2 is less soluble in 

seawater at higher temperatures. While increased concentrations of atmospheric CO2 may 

be accelerating the uptake of CO2 by surface seawater, global warming may be slightly 

damping the uptake. But of more substantial influence in accelerating the tipping point of 

65 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

net reef accretion are the synergistic biological effects on corals of reduced growth in the 

face of natural and anthropogenic stressors. 

Sabine and others (2004) showed that uptake of anthropogenic CO2 by subtropical 

Atlantic waters has been greater than by Pacific waters. The north Atlantic occupies only 

15 percent of the world’s total ocean area and stores 23 percent of the total anthropogenic 

(fossil-fuel and cement-manufacturing emissions) CO2 taken up by the world oceans. 

Pacific waters are less receptive to the uptake of CO2 and therefore are buffered from a 

decrease in pH because of higher concentrations of dissolved inorganic carbon. As 

seawater becomes warmer coral reef net accretion will probably become slightly more 

restricted in latitude (Kleypas et al. 1999, 2001) because of the changes in chemistry from 

CO2 uptake in the world’s oceans. 

Studies have shown that the resilience of corals to lower pH of ocean waters 

decreases with input of nutrients from continents. Anne Cohen of the Woods Hole 

Oceanographic Institution has taken core samples from 20 large Diploria labyrinthiformis 

colonies in Bermuda and found that the rate of calcification has significantly declined 

since 1959 (Cohen et al. 2008). This is consistent with the decrease in pH of the ocean 

waters of the northern Atlantic (Sabine et al. 2004) and the concomitant lowering of the 

saturation level of aragonite in coral skeletons. The corals are, nevertheless, doing well, 

and the coral-reef ecosystem is intact in Bermuda (Murdoch et al. 2008), which is 

relatively distant from Continental land masses. In contrast, the coral reefs have been 

degrading for decades in the Caribbean and western Atlantic (Gardner et al. 2003), which 

are close to continental land masses and associated land-surface runoff. (The input of 

fixed nitrogen from excess fertilizer runoff from the Mississippi River into the western 

66 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Atlantic has averaged 1.6 million metric tons per year since the 1980s and the input of 

phosphate has averaged a hundred thousand metric tons per year). 

 Ocean Uptake of CO2 

Figure 3.9. Status of oceanic uptake of CO2. Source: Sabine, S.L., R.A. Feely, Nicolas Gruber, R.M. Key, 

Kitack Lee, J.L. Bullister, Rick Wanninkhof, C.S. Wong, D.W.R. Wallace, Bronte Tilbrook, F.J. Millero, 

T.-H. Peng, Alexander Kozyr, Tsueno Ono, and A.F. Rios. 2004. The oceanic sink for anthropogenic CO2. 

Science, 305(5682), 367–371. 

Done et al. (in press) report that coral communities on the Great Barrier Reef have 

been losing their resilience since about 1997. Done et al. found that loss in resilience on 

the Great Barrier Reef is correlated with nutrient (fixed nitrogen) input. 

A number of studies presented at the 11th International Coral Reef Symposium 

reported that coral-reef systems are still resilient in areas far from continental land masses 

(for example, the Andaman and Maldive Archipelagoes, Chagos and Maldives in the 

Indian Ocean, Moorea, Fiji and American Samoa in the Pacific, and Bermuda in the 

Atlantic). 

67 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Seawater Warming.—The thresholds in tolerance of corals to an increase in water 

temperature and its duration before “bleaching” (expelling the symbiotic zooxanthellae) 

is predicted by the degree heating week (DHW) record (a NOAA satellite-derived 

experimental product), 12-week accumulations measured as °C weeks. The DHW 

product is an accumulation of hotspot values over the bleaching threshold (1°C over the 

maximum monthly mean. The threshold values of DHW vary from site to site because 

the maximum monthly mean varies from site to site; thus, corals are likely adapted to 

their own threshold temperatures at each site. Furthermore, the past history of events in 

the physical environment and local characteristics of the physical environment can 

modify the actual location of the threshold or tipping point (Smith and Birkeland 2007). 

Based on our knowledge of tolerances and the gaps in the literature on thresholds 

identified in developing this SAP, corals are likely to reach a threshold with an increase in 

sea water temperatures. 

Mechanisms of Reef Resilience That Alter Thresholds.—The resilience of corals 

to environmental changes is largely determined by their capacity to acclimatize (adjust 

physiologically and behaviorally). The thresholds of resilience of corals to environmental 

factors, such as water temperature and ultraviolet (UV) radiation, are altered by changes 

in symbiotic interactions. Reef-building corals are dependent on symbiotic dinoflagellate 

algae (zooxanthellae) in their endodermal cells for their nutrition and proficiency in 

deposition of skeleton. There are a number of clades or types of zooxanthellae, and the 

physiological and ecological attributes of zooxanthellae vary among clades (Abrego et al. 

2008; Berkelmans and van Oppen 2006; McClanahan et al.2005; Baker et al. 2004; Baker 

2004; Buddemeier et al. 2004; Rowan 2004; Baker 2003; Rowan and Knowlton 1995). 

68 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

The symbiotic relationship breaks down under stressful conditions of extra warm 

seawater or strong UV radiation. Under these conditions, corals sometimes expel much of 

the zooxanthellae of clade C and allow the buildup of clade D, with which the coral 

growth rate is slower but survival under stressful conditions may be greater. As with 

morphological adjustments, the symbiotic adjustments of corals may be determined by a 

balance between the stresses imposed by the physical environment and by ecological 

interactions with other species (Bruno and Selig 2007). In addition to adjustments in 

morphology and symbiotic relationships, acclimatization can occur through biochemical 

conditioning where increased water temperature triggers a substantial increase in 

biochemical activity in corals. Intense biochemical activities (such as the increase in the 

amounts of heat shock proteins and ubiquitin produced) resulting from changes in water 

temperature, may indicate processes of biochemical conditioning and acclimatization that 

might increase the resilience of the coral from increased seawater temperature (that is, 

increase the threshold level of coral mortality) (Smith and Birkeland 2007). 

Whether changes in morphology, symbiotic relationships, physiological 

conditioning, or production of biochemicals are the mechanisms to shift the threshold for 

survival from climate change, acclimatization costs the coral in terms of energy and 

materials that would otherwise be available for growth and successful competition. 

Acclimatization in corals can occur either as an accumulation of a simultaneous array of 

biochemical mechanisms to resist stress (robustness) or as an array of alternative paths of 

development or symbiotic associates (plasticity). The mound-shaped species of Porites

(such as P. lobata) are robust and live in a wide range of habitats. They are the last to 

drop out of the coral community near a river mouth or in bays with increasing turbidity. 

69 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Species of Acropora dominated the reef front at the municipal sewer outfall for Koror, 

Palau, until predation on corals by the crown-of-thorns starfish and bleaching by the 

large-scale seawater warming of 1997–98 killed the Acropora spp. but not the Porites

spp. (Richmond et al. 2002). Porites can maintain itself rather constantly despite 

fluctuations in the external physical environment, but at a metabolic cost (fig. 3.11). 

The relatively rapidly growing Pocillopora eydouxi display plasticity and can 

differ substantially among habitats in rates of growth, colony morphology, and types of 

zooxanthellae hosted. Pocillopora are generally more vulnerable to the physical 

environment and so their growth rates vary among habitats and they are more likely to 

bleach (expel zooxanthellae and/or photosynthetic pigments) with higher than usual water 

temperatures and with more intense UV radiation. 

Figure 3.10. Branching corals overgrowing mound-shaped corals. 

70 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Factors that Shift the Thresholds.—Corals are most vulnerable to infrequent or 

very frequent environmental changes. Corals can acclimatize (physiological or behavioral 

response) or adapt (genetic response) to environmental changes of intermediate 

frequency. (Adaptation is genetic change in a population in response to natural selection). 

If the phenomena, such as extraordinarily warm seawater, are infrequent enough to be 

unpredictable, corals will not be able to acclimatize or adapt, and if too frequent, will not 

have time to recover between events, thus decreasing the threshold level of coral 

mortality (Smith and Birkeland 2007). 

The factor of duration relates to the different effects of acute and chronic 

disturbances on the resilience of coral communities. The threshold seawater temperature 

associated with global climate change is determined in part by the duration of the warm 

water event. In 1997–98, an increased average surface seawater temperature of 1.0° to 

1.5°C (to about 30° or 31°C) over a period of several weeks caused extensive mortality of 

corals in the Indian Ocean, the southwestern Pacific Ocean, and the western Atlantic 

Ocean (Bruno and Selig 2007). In contrast, daily fluctuations of 6°C to 6.5°C (to about 

34°C or 35.5°C) in reef flat pools in American Samoa are endured in good health by 

about 80 species of corals. 

The threshold seawater temperature that severely affects a coral will be higher in 

areas of constant or even intermittent strong water motion and the threshold of 

temperature tolerance will be lower in areas of weak water motion (Smith and Birkeland, 

2007). Thresholds in levels of tolerable input of nutrients or sediment will be low in 

backwaters and relatively much higher in areas of strong current (Smith and Birkeland 

2007; Garrison et al. 2003). In contrast, it will take substantially longer for the ecosystem 

71 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

to solidify rubble into a stable substratum for reef recovery in areas of strong water 

motion than in areas of low water motion. The threshold of tolerance of corals to 

infection by disease is sometimes lowered by stress from other environmental factors and 

by abrasion of surface tissue by predators or other objects (Garrison et al. 2003). The 

physical and biological environments are a complex system of factors that potentially act 

synergistically to shift the threshold of the specific factor associated with climate change. 

Thresholds.—Thresholds should be considered at two stages: the first at which the 

population is killed or the ecosystem becomes dysfunctional, and the second at which the 

population or the ecosystem is prevented from becoming reestablished. An acute 

disturbance to a coral reef is a distinct event whereas a chronic disturbance is an ongoing 

process. Coral-reef communities in the Pacific (American Samoa) have been severely 

affected by large-scale acute disturbances, such as outbreaks of the coral-eating crownof-thorns starfish Acanthaster planci (1938, 1978), hurricanes (1981, 1987, 1990, 1991, 

2004, 2005), and bleaching in response to seawater warming (1994, 2002, 2003). This is 

in contrast to the western Atlantic where there has been chronic disturbance resulting in 

degradation of coral reef systems for a half a century (Gardner et al. 2003). When 

allowed a 15-year interval between acute disturbances, the Pacific coral communities 

have recovered (Birkeland et al. 2008). Whereas in the relatively small area of the 

tropical western Atlantic, external stressors such as nutrients (Hallock et al. 1993), 

pollutants (Garrison et al. 2003), and diseases (Lessios et al. 1984) from wide-scale 

events on continents (Hallock et al. 1993; Garrison et al. 2003) can disperse across the 

entire region. This chronic disturbance decreases the threshold of coral mortality. A 

recent paper by Bruno and Selig (2007) reported that 3,168 square kilometers of reef has 

72 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

been dying each year rather uniformly throughout the Indo-Pacific Ocean. Reefs are 

appearing to be losing their resilience globally. 

Coral reefs in the Pacific (America Samoa) have managed to maintain resilience 

because disturbances have been acute events. External stressors from overfishing, 

however, have been chronic, and the fish communities have not been as resilient as the 

corals (Zeller et al. 2006 a, b). Thresholds of coral reef systems need to take into account 

the whole system and not just the corals to ensure a resilient and adaptive system in the 

face of climate change. 

73 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Chapter 4—Examples of Threshold Change in Ecosystems 

4.1 Background 

The existence of ecological thresholds has long been apparent to people who 

depend on natural resources. Fisheries collapses, for instance, have been noted for 

centuries. However, ongoing climate change has given this issue greater urgency because 

more ecosystems may be getting pushed toward response thresholds simultaneously, and 

based on gaps in the literature identified through the development process for this 

assessment (SAP 4.2), little is known regarding where the tipping points are. 

Ecosystems are very likely to differ significantly in their potential for climate 

change to impact them to the point that thresholds are crossed and substantial alterations 

occur. Given the magnitude and pervasiveness of climate change, it is surprising how 

little is known regarding the sensitivity of different ecosystems to any single aspect of 

climate change (such as increased temperatures), and even less is known about the 

impacts of multiple climate change factors. This lack of basic understanding represents a 

critical knowledge gap and research challenge, one that is further complicated by the fact 

that climate change is only one component of global change and that multiple alterations 

to climate, biogeochemical cycles, and biodiversity are occurring in tandem. 

Summarized below are examples of where ecological thresholds have been 

crossed; they are less detailed than the case studies of Chapter 3 but represent different 

geographic areas, ecosystem types, and drivers of change. These examples include the 

new stressor of climate change and reflect how it leads to new ecosystem responses. For 

example, the temperature increases documented for many areas can likely cause an 

ecosystem changeover when normal droughts are experienced because the additional 

74 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

evapotranspiration demand of higher temperatures exceeds the tolerance capacity of 

trees, leading to the massive forest dieback described in Case Study 3. 

4.2 Example of Thresholds from the Past 

Thresholds appear to have been crossed in the past, leading to ecosystem changes 

that persist today. A recent example of threshold behavior is the encroachment of woody 

plants into perennial grasslands that has occurred throughout arid and semiarid regions of 

the world for at least the past several centuries. This broad-scale land-cover conversion 

and associated soil degradation (that is, desertification) has local to global consequences 

for ecosystem services, such as reduced air and water quality (Schlesinger et al. 1990; 

Reynolds and Stafford Smith, 2002). Multiple interacting processes and threshold 

behavior are involved in these dynamics (Rietkerk and van de Koppel, 1997). 

Cross-scale linkages among local soil and grass degradation, landscape 

connectivity of erosion processes, and land-cover and weather feedbacks have been 

invoked to explain threshold behavior in space and time that occur during desertification 

(Peters et al. 2006). Four stages and three thresholds have been identified as the spatial 

extent of desertified land increases through time (Peters et al. 2004). Following 

introduction of woody plant seeds into a grass-dominated system (Stage 1), local spread 

often occurs as a result of feedback mechanisms between plants and soil properties 

interacting with wind and water erosion to produce fertile plant islands surrounded by 

bare areas that move the system across a threshold into Stage 2 (Schlesinger et al. 1990). 

This rate of spread may be slower than other stages as a result of interactions between 

plant life history characteristics that occur infrequently, such as recruitment, and the low 

precipitation and high temperatures that characterize dry regions. As the size and density 

75 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

of woody plants increase through time, contagious processes among patches, primarily 

wind and water erosion that connect bare soil patches, become the dominant factors 

governing the rate of desertification. As a result, a nonlinear increase in woody plant 

cover occurs and a second threshold is crossed as the system enters Stage 3. Through 

time, sufficient land area can be converted from grassland (low bare area, low albedo) to 

woodland (high bare area, high albedo) so that regional atmospheric conditions, in 

particular wind speed, temperature, and precipitation, are affected. At this point, a third 

threshold is crossed where land-atmosphere interactions with feedbacks to vegetation 

control system dynamics (Stage 4) (Pielke et al. 1997). Feedbacks to broad-scale 

vegetation patterns have been documented in the Sahara region of Africa (Claussen et al. 

1999). 

4.3 Temperature Increases Are Pushing Ecosystems Towards 

Thresholds 

The impacts of increasing temperatures resulting from climate change are not 

independent of the effects of other important environmental stressors, and thus, need to 

be assessed in the context of multiple, interacting stressors. The IPCC (2007) reports with 

very high confidence that the increased warming effect of climate change is strongly 

affecting natural biological systems in both marine and freshwater systems. The chemical 

and physical characteristics of lakes experience major effects owing to changes in 

temperature, especially changes in nutrient dynamics. Increased temperatures in lake 

systems will affect the distributions, growth, and survival of fish and many other aquatic 

organisms. Tied with increased temperatures is a change in precipitation, which can cause 

substantial physical and chemical changes in lakes, streams, and wetlands (as discussed 

76 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

in Chapter 3) with large consequences for aquatic biota. In marine systems, increased 

temperature from climate change is affecting coastal resources and habitats because of 

sea-level rise that is caused by thermal expansion of the oceans and the melting of ice 

cover. It also is affecting the strongly coupled atmospheric and oceanic circulation that 

underpins ecosystem dynamics in wind-driven upwelling shelves and ecosystem 

susceptibility to modulations of upwelling wind stress causing present day global 

distribution of shelf anoxia (Chan et al. 2008). This has the potential to affect the rate at 

which the threshold for mortality will be reached for demersal fish and benthic 

invertebrate communities in these shallow waters. The rate of sea-level rise is expected to 

accelerate because of global warming. Salt marshes, which must increase their vertical 

elevation at rates that keep pace with sea-level rise or risk transformation to a lower 

position along the marsh gradient, may experience a change of marsh type. Transition 

from one type of marsh to another (for example, high marsh to low marsh) at a given 

point has been described as ecosystem state change (Miller et al. 2001). 

The effects of temperature increases on terrestrial systems are further emphasized 

in the IPCC Assessment Report for Working Group II (IPCC 2007), where it is stated 

with very high confidence that the overwhelming majority of studies of regional climate 

effects on terrestrial species reveal consistent responses to warming trends, including 

poleward and elevational range shifts of flora and fauna. Responses of terrestrial species 

to warming across the Northern Hemisphere are well documented by changes in the 

timing of growth stages (that is, phenological changes), especially the earlier onset of 

spring events, migration, and lengthening of the growing season. Changes in abundance 

of certain species, including limited evidence of a few local disappearances and changes 

77 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

in community composition over the last few decades have been attributed to climate 

change. A further indication of effects of increased temperatures is revealed in earlier 

snowmelt and stream runoff, which affects both aquatic and terrestrial ecosystems and 

species. Diminished snowpacks that melt earlier in the spring have affected the timing 

and extent of seasonal wetlands where amphibians breed. A threshold may occur wherein 

the reduced amphibian population cannot accommodate the necessary shift in the timing 

of breeding or cannot survive multiple dry years, causing local extinction (Corn 2003). 

There is a need to better understand the complexities of ecosystems and the 

drivers of change within them and to be able to identify the thresholds of these changes in 

a changing climate. 

4.3.1 Climate Interactions Drive Ecosystems to Thresholds 

As important as the increases in temperatures and changes in moisture availability 

are for causing ecosystems to go through thresholds, it is the interactions that are key to 

driving the change. In general, plants in undisturbed ecosystems are at their moisturelimited capacity for net primary productivity. Therefore, increased temperatures and

droughtiness will combine to produce severe stress on plant growth, whereas increased 

temperatures and increased moisture availability will lessen the stress or may promote 

plant productivity, leading to an ecosystem with increased resilience. Because 

evapotranspiration demands on vegetation increase with temperature, thresholds are more 

likely to occur whenever moisture availability does not simultaneously increase with 

warming temperatures. The exception is ecosystems that are primarily limited by 

temperature, such as arctic and alpine ecosystems. In these latter cases, ample moisture 

means that vegetation can respond without evapotranspiration limits but that threshold 

78 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

changes can still occur as competitive relationships are altered between plant species 

(Hansell et al. 1998). 

4.3.2 Climate Variability Increases Likelihood of Threshold Shifts 

The climate drivers that produce threshold ecosystem responses may be complex 

and involve the interaction of variability in phenology and weather episodes. The “2007 

spring freeze” in the Eastern United States is an example. A very warm late winter/early 

spring period in much of the Southeastern United States in 2007 led to bud-break and 

development of forest canopy 2 to 3 weeks earlier than usual. A very cold Arctic air mass 

spread across much of the Eastern United States in early April (an event not unusual for 

that time of year), dropping the low daily temperatures well below freezing for several 

days. The freeze killed newly formed leaves, shoots, and developing flowers and fruits 

and resulted in a sharp drop in vegetation greenness (NDVI) across a large swath of the 

southeast. The severity of impact was species specific; but at one site affected by this 

episode, there was a significant reduction in forest photosynthetic activity for at least 

several weeks after this event, and the leaf-area index was depressed throughout the 

summer (Gu et al., 2008). Smaller forest leaf area resulted in increased light availability 

to the stream draining the forest at this site throughout the late spring and summer, 

leading to increased primary and secondary productivity and higher rates of nutrient 

uptake and retention in the stream (Mulholland et al., 2009). Long-term climate records at 

this site showed that average late-winter temperatures are increasing but the date of the 

last hard freeze remains variable and shows no trend with time, suggesting that this 

heretofore unusual weather event may become more common in the future, which could 

lead to significant effects on forests and streams. While our understanding of the long79 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

term effects of this episode are unclear, they may likely include significant changes in 

forest composition due to mortality and/or increased susceptibility to pests of the more 

susceptible species if similar episodes occur in the future (IPCC, 2007). 

4.3.3 Other Human Stressors and Climate Change 

The interaction of human stresses on ecosystems (for example, land-use change) 

and climate change may be most evident for lotic ecosystems (those of rivers, streams, 

and springs) and may produce threshold responses that each stress alone would not 

produce. Flow variability over time and space is a fundamental characteristic of lotic 

ecosystems. It is this temporal and spatial flow variability that defines and regulates 

biotic composition and key ecosystem processes in streams and rivers (Poff et al., 1997; 

Palmer et al., 2007). Climate change will alter flow regimes and generate changes to 

biotic communities in many of these ecosystems, although it is not clear that these flow 

alterations will produce threshold-type responses in these systems that have evolved in 

response to high flow variability. However, growing water demands combined with 

climate-change-induced increases in the severity and duration of droughts in the western 

United States will likely lead to hydrologic regime shifts in many drainage basins 

(Barnett et al., 2008). 

Recent empirical evidence suggests that severe droughts can produce more 

dramatic and long lasting effects (for example, loss of biodiversity) on the biological 

communities of streams and river ecosystems than do other changes in the flow regime, 

such as floods (Boulton et al., 1992; Lake, 2004). Studies of drought effects on 

macroinvertebrates in Australian streams where drought is a common and widespread 

phenomenon suggest that there may be a significant lag effect that prevents recruitment 

80 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

after drought conditions end (Boulton, 2003). Historical evidence exists of large shifts in 

river fish communities in response to decades-to-century-scale droughts in the Colorado 

River basin at the end of the Pleistocene (Douglas et al. 2003), but recent findings 

indicate large uncertainties in long-term effects of drought on fish (Matthews and MarshMatthews, 2003). 

Many of the expected changes to flow regimes from climate change are similar to 

those that result from urbanization and other human alterations of drainages. Among 

these are increased flashiness of hydrographs and longer periods of low or intermittent 

flow, higher water temperatures, and simplified biotic assemblages (Paul and Meyer, 

2001; Roy et al., 2003; Allan, 2004; Nelson and Palmer, 2007). The increases in 

urbanization that have occurred and are likely to continue in many regions of the United 

States will very likely exacerbate the effects of climate change. 

The strongest evidence for potential threshold effects in rivers and streams 

appears to be the result of combined impacts of high or increasing human water 

withdrawals and the likelihood of more frequent or longer droughts under a warming 

climate. Defining a water stress index equivalent to total human water use divided by 

river discharge, Vorosmarty et al. (2000) showed that the combination of projected 

population and climate change results in substantial increases in water stress over large 

areas of the eastern and southwestern United States. In an analysis of sustainable water 

use in the United States, the Electric Power Research Institute (EPRI, 2003) reported that 

total freshwater withdrawal exceeded 30 percent of available precipitation over much of 

the semiarid and arid regions of the United States and over large areas of Florida and 

other metropolitan areas in the east. High rates of human water use reduce flow and 

81 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

extend low flow periods, restricting and degrading habitat for river and stream biota. 

Using two scenarios from the 2001 IPCC report, Xenopoulos et al. (2005) reported that 

the combination of climate change and increased water withdrawal may result in loss of 

up to 75 percent of the local fish biodiversity in global river basins. 

There are several examples of potential large-scale threshold responses to the 

combined effects of human water management and climate-induced drought. In the 

Columbia River basin of the Pacific northwest, multiple stressors (including population 

growth; conflicts between hydropower, agriculture, and recreation interests; and 

ineffective water management institutions and structures) have increased the vulnerability 

of water resources (Payne et al., 2004; Miles et al., 2007) that were already vulnerable as 

a result of reduced winter snowpack (Barnett et al., 2005), which generates much of the 

summer flow, and sustained or repetitive droughts projected by climate change models 

that would drive water supplies to extreme low levels. Because salmon populations are 

under considerable stress due to dams, water withdrawals, and other human actions, 

reduced summer flow under a warmer climate may exceed population sustainability 

thresholds (Neitzel et al., 1991). 

The Colorado River supplies much of the water needs of a large area of the 

western United States and northern Mexico. The lower portions of the river have become 

highly vulnerable to drought due to increased demand from population increases. A longterm drought, beginning in about 2000, has lowered water levels considerably in Lakes 

Powell and Mead, and many climate models project future conditions that will eventually 

lead to the drying up of Lake Powell and reduced flow in the Colorado River by more 

than 20 percent. Water allocations for maintaining the ecological integrity of natural 

82 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

communities could drop below thresholds that ensure their viability as scarce water is 

prioritized for human communities (Pulwarty et al., 2008). 

Even in the humid southeastern United States, the combined effects of increased 

water withdrawals and climate change may exceed thresholds in ecosystem response. The 

Chattahoochee-Apalachicola River basin in Alabama, Florida, and Georgia is both an 

important water source for agricultural, industrial, and municipal uses and an important 

fishery. More than 75 percent of the fish species inhabiting this river system depend on 

access to floodplain and tributary areas to forage and spawn, and there are flow 

thresholds below which fish cannot move into these critical areas (Light et al. 1998). 

Analysis of projected future water withdrawals and climate change for the 

Chattahoochee-Apalachicola River basin indicates that by 2050, minimum flows will 

drop below these minimum flow thresholds for at least 3 months in summer in some 

areas (Gibson et al. 2005).This situation will be exacerbated by the increased percentage 

of flow that is wastewater effluent combined with lower minimum flows in this rapidly 

urbanizing basin. This will increase biological oxygen demand and reduce dissolved 

oxygen concentrations potentially below threshold levels required by some species of fish 

(Gibson et al. 2005). 

The drying up of streams and wetlands represents thresholds that involve 

contraction or elimination of entire aquatic ecosystems. Prairie rivers, streams, and 

wetlands of the Great Plains may be particularly vulnerable to these types of thresholds 

because of the combined effects of water withdrawals for agricultural and municipal uses 

and projected climate changes that will result in longer periods of drought (Johnson et al., 

2005). For example, since the late 1970s, the Arkansas River and many of its tributaries 

83 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

in Kansas have had long periods of dry channels because of extensive surface and 

groundwater use in its drainage basin (Dodds et al. 2004). The drying up of headwater 

streams and even some larger streams and rivers for extended periods may become 

common in wetter areas of the United States as well, particularly as a result of the 

combined effects of increased water withdrawal and climate change. 

Riparian ecosystems are also vulnerable to drought-related thresholds, particularly 

in the more arid regions of the United States. Riparian forests dominated by cottonwood 

are being replaced by drought-tolerant shrubs along some rivers in the western United 

States. Increased surface and groundwater withdrawals combined with drought have 

resulted in the replacement of riparian forests of native cottonwood (Populus fremontii) 

and willow (Salix gooddingii) by an invasive shrub (Tamarix ramosissima), resulting in 

reduced animal species richness, diversity, and abundance over extensive areas along the 

San Pedro River in Arizona (Lite and Stromberg, 2005). Surface flow and the depth to 

groundwater appear to be the primary controls on riparian vegetation, with loss of native 

riparian communities when rivers and streams drop below flow permanence thresholds of 

50 percent to 75 percent (Stromberg et al. 2005, 2007). 

4.3.4 Ecosystem Vulnerability and Climate Change 

Some ecosystem attributes may be particularly important in generating 

differential ecosystem vulnerability to climate change, including the likelihood that 

important thresholds of response are crossed. For example, most ecosystems have a 

single or just a few dominant species that mediate ecological processes, control the 

majority of the resources (including space), and/or have disproportionate impacts on 

species interactions. Thus, if climate change favors a new dominant species, the 

84 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

prediction is that it will likely be the rate at which the extant species can be replaced and 

the traits of these new species that will determine the likelihood that the ecosystem will 

be altered significantly to result in threshold behavior in state or function. For example, 

ecosystems dominated by long-lived species (for example, trees) with slow population 

turnover would be expected to be relatively slow to respond to climate change, whereas 

those ecosystems dominated by short-lived species (for example, annual plants) are 

expected to be more vulnerable to experiencing substantial change if the new dominant 

species replacing the old have very different species traits. 

Ecosystems can differ dramatically in the sizes of key carbon and nutrient pools, 

as well as rates of biogeochemical transformations and turnover. These attributes may 

also determine the rate and magnitude of ecosystem response to climate change if climate 

forcings influence these biogeochemical attributes. For example, ecosystems with large 

nutrient pools and/or slow turnover rates are expected to respond minimally to climatechange-induced alterations in nutrients. In contrast, ecosystems with limited nutrient 

pools and rapid biogeochemical cycling are expected to be more vulnerable to climate 

change that results in critical thresholds being crossed. The general hydrologic balance of 

ecosystems would similarly impact ecosystem sensitivity to any climate change that 

affects water availability. In general, those ecosystems with a ratio of precipitation-topotential evapotranspiration that is near or below 1:1 will be predicted to be more 

vulnerable to change than ecosystems where this ratio is greater than 1:1. 

Levels of biodiversity (functional traits and species) within an ecosystem may 

also be important in influencing sensitivity to climate change (Grebmeier et al 2006). The 

number and traits of species may buffer ecosystems from change and influence the extent 

85 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

to which immigration of new species will occur. For example, depending on how well 

species in an ecosystem functionally complement each other and the ability of species to 

compensate for the change resulting from the loss of the dominant species, the 

replacement of a dominant species by another species could result in no change or large 

changes in ecosystem state. Similarly, invading species may result in the rapid crossing 

of thresholds or may have little or no impact depending on the traits of these species 

relative to the traits of native species. 

Finally, interactions with the natural disturbance regime inherent in an ecosystem, 

other climate change factors, and other global changes, such as habitat fragmentation and 

species invasions, will more than likely influence whether or not ecosystems cross 

response thresholds and experience substantial amounts of change in their structure and 

function. For example, ecosystems that are historically prone to fire may experience more 

frequent fires with climate change, making them more susceptible to invasions by exotic 

species as resources become available postfire. 

86 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Chapter 5—What Can Be Done? 

Because there is significant potential for abrupt or threshold-type changes in 

ecosystems in response to climate change, what changes must be made in existing 

management models, premises, and practices to manage these systems in a sustainable, 

resilient manner? What can be managed and at what scales, given that climate change is 

global in nature but manifests itself at local and regional scales of ecosystems? This 

section reviews the management models that predict how ecosystems will respond to 

climate change and examines their adequacy for addressing threshold behavior. 

5.1 Integration of Management and Research 

With ongoing climate change and the threat that ecosystems will experience 

threshold changes, managers and decisionmakers are facing more new challenges than 

ever. Strong partnerships between research and management can help in identifying and 

providing adaptive management responses to threshold crossings. Because 

decisionmakers are dealing with whole new ecosystem dynamics, the old ways of 

managing change do not apply. A new paradigm in which research and management 

work closely together is needed. The following sections highlight some of the needs of 

managers. 

5.1.1 Need for Conceptual Models 

Most frameworks for nonlinear ecosystem behavior are hierarchical so a small 

number of structuring processes control ecosystem dynamics; each process operates at its 

own temporal and spatial scale (O’Neill et al., 1986). Finer scales provide the 

mechanistic understanding for behavior at a particular scale, and broader scales provide 

the constraints or boundaries on that behavior. Functional relationships between pattern 

87 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

and process are consistent within each domain of scale so that linear extrapolation is 

possible within a domain (Wiens, 1989). Thresholds occur when pattern-and-process 

relationships change rapidly with a small or large change in a pattern or environmental 

driver (Bestelmeyer, 2006; Groffman et al., 2006), although both external stochastic 

events and internal dynamics can drive systems across thresholds (Scheffer et al., 2001). 

Crossing a threshold can result in a regime shift where there is a change in the direction 

of the system and the creation of an alternative stable state (Allen and Breshears, 1998; 

Davenport et al., 1998; Walker and Meyers, 2004). Under some conditions, thresholds 

may be recognized when changes in the rate of fine-scale processes within a defined area 

propagate to produce broad-scale responses (Gunderson and Holling, 2002; Redman and 

Kinzig, 2003). In these cases, fine-scale processes interact with processes at broader 

scales to determine system dynamics. A series of cascading thresholds can be recognized 

where crossing one pattern-and-process threshold induces the crossing of additional 

thresholds as processes interact (Kinzig et al., 2006). Conceptual models are particularly 

useful in linking hierarchical models across scales, because the existence of cross-scale 

interactions are often clearly recognized and can be incorporated as rules, even if they 

cannot be precisely parameterized. Field experiments that identify cause-and-effect 

relationships can then be implemented to test these cross-scale interactions. For example, 

manipulation of CO2 or water table depth (global-to-regional drivers of change) can be 

used to assess impacts on plot-scale patterns of biogeochemistry of community 

composition. 

88 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

5.1.2 Scaling 

Recent theories and ideas about system behavior have used hierarchy theory as a 

basis for describing interactions among processes at different scales. Such theories 

include complex systems (Milne, 1998; Allen and Holling, 2002), self-organization 

(Rietkerk et al., 2004), panarchy (Gunderson and Holling, 2002), and resilience (Holling, 

1992; Walker et al., 2006). Cross-scale interactions (CSIs, processes at one spatial or 

temporal scale interacting with processes at another scale that often result in nonlinear 

dynamics with thresholds) are an integral part of all of these ideas (Carpenter and Turner, 

2000; Gunderson and Holling, 2002; Peters et al., 2004). These interactions generate 

emergent behavior that cannot be predicted based on observations at single or multiple, 

independent scales (Michener et al., 2001). CSIs can be important for extrapolating 

information about fine-scale processes to broad-scales or for downscaling the effects of 

broad-scale drivers on fine-scale patterns (Ludwig et al., 2000; Diffenbaugh et al., 2005). 

The relative importance of fine- or broad-scale pattern-and-process relationships can vary 

through time and compete as the dominant factors controlling system dynamics (for 

example, Rodó et al.2002; King et al, 2004; Yao et al. 2006). 

Because CSI-driven dynamics are believed to occur in a variety of systems, 

including lotic invertebrate communities in freshwater streams (Palmer et al.1996) and 

lakes (Stoffels et al. 2005), mouse populations in forests (Tallmon et al. 2003), soil 

microbial communities (Smithwick et al. 2005), coral reef fish recruitment in the ocean 

(Cowen et al. 2006), human diseases (Rodó et al. 2002), and grass-shrub interactions in 

deserts (Peters et al. 2006)—it is critical that ecologists find ways to measure CSI. It is 

important to identify the key processes involved in these changing pattern-and-process 

89 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

relationships so that thresholds can, at a minimum, be understood and predicted if not 

averted through proactive measures. 

Recently, a framework was developed to explain how patterns and processes at 

different scales interact to create nonlinear dynamics (Peters et al. 2007). This framework 

focuses on intermediate-scale properties of transfer processes and spatial heterogeneity to 

determine how pattern-and-process relationships interact from fine to broad scales (fig. 

5.1). In this framework, within a domain of scale (that is, fine, intermediate, or broad), 

patterns and processes can reinforce one another and be relatively stable. Changes in 

external drivers or disturbances can alter pattern-and-process relationships in two ways. 

First, altered patterns at fine scales can result in positive feedbacks that change 

patterns to the point that new processes and feedbacks are induced. This shift is 

manifested in a nonlinear threshold change in pattern and process rates. For example, in 

arid systems, disturbance to grass patches via heavy livestock grazing can reduce the 

competitive ability of grasses and allow shrub colonization. After a certain density of 

shrubs is reached in an area and vectors of propagule transport (for example, livestock or 

small animals) are available to spread shrubs to nearby grasslands, shrub colonization and 

grass loss can become controlled by dispersal processes rather than by competition. 

Shrub expansion rates can increase dramatically (Peters et al. 2006). As shrub 

colonization and grazing diminish grass cover over large areas, broad-scale wind erosion 

may govern subsequent losses of grasses and increases in shrub dominance. These broadscale feedbacks downscale to overwhelm fine-scale processes in remnant grasslands. 

Once erosion becomes a pervasive landscape-scale process, neither competition nor 

dispersal effects have significant effects on grass cover. 

90 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Second, direct environmental effects on pattern-and-process relationships at broad 

scales can similarly overwhelm fine-scale processes. For example, regional, long-term 

drought can produce widespread erosion and minimize the importance of local grass 

cover or shrub dispersal to patterns in grasses and shrubs. 

Pattern

(patch type)

Process

(competition)

fine intermediate broad

Spatial Scale

Pattern

(connectivity)

Pattern

(land cover)

Process

(erosion) 

Process

(dispersal)

Environmental drivers/disturbances

Pattern

(patch type)

Process

(competition)

fine intermediate broad

Spatial Scale

Pattern

(connectivity)

Pattern

(land cover)

Process

(erosion) 

Process

(dispersal)

Environmental drivers/disturbances

Figure 5.1. Diagram representing cross-scale interactions. Solid arrows represent pattern-and-process 

feedbacks within three different scale domains with one example of pattern and process shown for each 

domain. Green arrows indicate the direct effects of environmental drivers or disturbances on patterns or 

processes at different scales (for example, patch disturbance versus climate). Blue arrows indicate the point 

at which altered feedbacks at finer scales induce changes in feedbacks at broader scales (for example, finescale changes cascade to broader scales). Red arrows indicate when changes at broader scales overwhelm 

pattern-and-process relationships at finer scales. 

5.1.3 Applying Models from Other Disciplines 

Climate requires interdisciplinary approaches. Recent and global environmental 

changes, including climatic change, changes in atmospheric composition, land-use 

change, habitat fragmentation, pollution, and the spread of invasive species, have the 

potential to affect the structure and functions of some ecosystems, and the services they 

provide. Many ecological effects of global environmental change have the potential for 

91 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

feedbacks (either positive or negative) to climatic and other environmental changes. 

Furthermore, because many global environmental changes are expected to increase in 

magnitude in the coming decades, the potential exists for more significant effects on 

ecosystems and their services. 

As climate change manifests itself at local and regional scales of ecosystems, it is 

necessary not only to downscale forecasting models but also to ensure that models used 

for predictions take into account not just the physical parameters that support ecosystems 

but also the biotic aspects of the ecosystems. Biomes and ecosystems do not shift as 

entities in response to climate change, but they change through the responses of 

individual species (Scott and Lemieux, 2005). The biogeochemical, temperature, and 

precipitation requirements of individual species need to be taken into account when 

predicting these shifts, thus the need for the use of interdisciplinary models that address 

these variables and their dynamic feedback. Our current understanding suggests that 

using interdisciplinary models will very likely reduce scientific uncertainties about the 

potential effects of global change on ecosystems and provide new information on the 

effects of feedbacks from ecosystems on global change processes. The challenge is to 

create a framework in which interdisciplinary models can work interactively to consider 

all the feedbacks involved. This integrative approach will provide a framework to 

organize observations and assessments of changes in the system in response to 

management actions. 

5.2 Adaptive Management to Increase Resilience 

The process of selecting, implementing, monitoring, assessing, and adjusting 

management actions is called adaptive management or, in the context of this report, 

92 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

adaptive ecosystem management (AEM) (Holling 1978; Walters 1986; Prato 2004, 

2007a). AEM can be done passively or actively. If passive AEM is used, the decision to 

adjust management actions or not depends on whether the indicators or multiple attributes 

of the outcomes of management actions suggest that the ecosystem is becoming more 

resilient or more variable and might cross a threshold. If active AEM is used, the decision 

of whether or not to adjust management actions is determined by testing hypotheses 

about how the ecosystem state is responding to management actions. Active AEM treats 

management actions as experiments. Unlike passive adaptive management, active AEM 

yields statistically reliable information about ecosystem responses to management 

actions, although it is more expensive and difficult to apply than passive AEM and 

requires sufficient monitoring (Lee 1993, Wilhere 2002). 

To increase ecosystem resilience, a number of approaches have been put forth for 

use in adaptive management (Julius et al. 2008). These include avoiding landscape 

fragmentation and its converse, restoring connectivity; ensuring that refugia are protected 

so that recolonization of species is possible; focusing protection on keystone species 

where applicable; reducing other stressors such as pollution; removing introduced 

invasive species; and reducing extraction of ecosystem services for humans (for example, 

ensuring water flows for aquatic ecosystems under stress) (Scott and Lemieux 2005, 

Groffman et al. 2006). For each ecosystem, AEM potentially provides quantitative 

documentation as to the relative efficacy of the different approaches to improving 

resilience (Keeley 2006; Millar et al. 2007: Newmaster et al. 2007). 

93 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

5.2.1 Role of Monitoring 

Because climate change effects are likely to interact with patterns and processes 

across spatial and temporal scales, it is clear the monitoring strategies must be integrated 

across scales. First and foremost, the earth’s surface must be hierarchically stratified (for 

example, using the Major Land Resource Area and Ecological Site Description System of 

the U.S. Department of Agriculture and National Resources Conservation Service and 

U.S. Forest Service ecoregions), and conceptual or simulation models of possible impacts 

and feedbacks must be specified for each stratum (Herrick et al., 2006). The models are 

used to develop scenarios and to identify key properties and processes that are likely to 

be associated with abrupt changes. Second, simultaneous multiple-scale monitoring 

should be implemented at up to three spatial scales based on these scenarios and the 

recognition of pattern-and-process coupling developed in the models (Bestelmeyer, 

2006), which may feature cross-scale interactions (Peters et al., 2004). 

Remote-sensing platforms can be used to monitor some broad-scale spatial 

patterns, including significant shifts in plant community composition; vegetation 

production; changes in plant mortality; bare-ground, soil, and water-surface temperatures; 

and water clarity. These platforms may also be used to detect rates of change in some 

contagious processes, such as the spread of readily observable invasive species. Changes 

in variance across space and time derived from such measures may be a primary indicator 

of incipient nonlinear change (Carpenter and Brock, 2004). These measures should be 

coupled with ground-based measures at mesoscale to patch scales. Mesoscale monitoring 

often requires widely distributed observations across a landscape (or ocean) acquired 

with rapid methodologies including sensor networks. Such widely distributed monitoring 

94 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

is necessary in some situations because incipient changes may materialize in locations 

that are difficult to predict in advance (such as with tsunami warning systems). In other 

cases, however, more targeted monitoring is necessary to detect mesoscale discontinuities 

in smaller areas that are likely to first register broad-scale change, such as at ecotone 

boundaries (Neilson, 1993). Finally, patch-scale monitoring can feature methodologies 

that focus on pattern-and-process linkages that scale up to produce systemwide threshold 

changes, such as when vegetation patches degrade and bare patches coalesce to result in 

desertification (Rietkerk et al., 2004; Ludwig et al., 2005). The involvement of land users 

is particularly important at this scale because recognition of processes that degrade 

resilience may be used to mitigate climate-driven thresholds by way of local management 

decisions. Consequently, technically sophisticated approaches should be balanced with 

techniques suitable for the public at large (for example, Carpenter et al., 1999; Pyke et al.,

2002). 

Monitoring data across scales must then be integrated, and interpretations 

generated for key strata. Ground-based monitoring, for example, may reveal key changes 

not detected through remote sensing, or conversely, remote sensing may explain 

apparently idiosyncratic patterns in ground-based data to reveal key vulnerabilities. 

Multiagency institutions and a “network of networks” could be organized with such 

efforts in mind and could periodically review data gathered across scales and from 

different partners (Parr et al., 2003; Betancourt et al., 2007; Peters et al., 2008). 

Monitoring key ecosystem indicators which integrate across ecosystem processes 

and scales are essential in developing observations for threshold changes. For example, 

nutrient export via streamflow is a sensitive metric for identifying changes in ecosystem 

95 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

structure and function at the watershed scale that may be difficult to detect on complex 

and spatially heterogeneous systems. For example, nitrate concentration in streams has 

been used as a sensitive indicator of forest nitrogen saturation (Stoddard, 1994; Swank 

and Vose, 1997; Lovett et al. 2000; Aber et al., 2003), effects of insect pest outbreaks 

(Eshleman et al. 1998), and effects of short-term climate perturbations (Mitchell et al. 

1996; Aber et al. 2002). Stream chemistry monitoring, particularly at gauged sites where 

discharge is also monitored, can provide sensitive signals of changes in ecosystem 

biogeochemical cycles. 

5.2.2 Role of Experiments 

A key component of adaptive management strategies is the role of 

experimentation. A critical component of designing appropriate experiments is to identify 

the conditions or systems that are susceptible to threshold behavior and interactions 

across scales that include transport processes at intermediate scales. Observations and 

experiments to evaluate the sensitivity of these processes and interconnections to 

anticipated perturbations provide insight to management strategies to enhance resilience 

or to mitigate threshold changes. One approach is to measure responses at multiple scales 

simultaneously and then test for significant effects of variables at each scale (for 

example, Smithwick et al. 2005; Stoffels et al. 2005). Experimental manipulations can 

also be used to examine processes at fine and intermediate scales and to isolate and 

measure impacts of broad-scale drivers under controlled conditions (for example, Palmer 

et al. 1996; King et al. 2004). Stratified-cluster experimental designs are methods for 

considering multiple scales in spatial variables and for accounting for distance as related 

to transport processes in the design (Fortin et al., 1989; King et al., 2004). Regression 

96 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

(gradient)-based experimental designs may be superior to analysis of variance 

(ANOVA)-type designs for predicting thresholds in ecological response to linear or 

gradual changes in climate or other drivers. 

Quantitative approaches also show promise in identifying key processes related to 

threshold behavior. Statistical analyses based on nonstationarity (Rodó et al., 2002) and 

nonlinear time series analysis (Pascual et al. 2000) are useful for identifying key 

processes at different scales. Spatial analyses that combine traditional data layers for fineand broad-scale patterns with data layers that use surrogates for transfer processes at 

intermediate scales (for example, seed dispersal) can isolate individual processes and 

combinations of processes that influence dynamics in both space and time (for example, 

Yao et al. 2006). Simulation models that use fine-scale models to inform a broad-scale 

model can be used to examine the relative importance of processes and drivers at 

different scales to system dynamics as well as interactions of processes and drivers 

(Moorcroft et al. 2001; Urban, 2005). Coupled biological and physical models that 

include population processes and connectivity among populations as well as broad-scale 

drivers have been used to show the conditions when connectivity is important, and to 

identify the locations that are more susceptible or resilient to management decisions 

(Cowen et al. 2006). 

5.3 Management by Coping 

If there is a high potential for abrupt or threshold-type changes in ecosystems in 

response to climate change, existing management models, premises, and practices must 

be modified in order to manage these systems in a sustainable, resilient manner (Millar et 

al. 2007). Existing management paradigms may have some limited value because of the 

97 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

assumption that the future will be similar to the past. This assumption, however, fails to 

take into account the underlying uncertainty of the trajectories of ecological succession in 

the face of climate change. Managers can instead take a dynamic approach to natural 

resource management, emphasizing processes rather than composition, to best maintain, 

restore, and enhance ecological functions (Walker et al., 2002). The following sections 

address some of the mechanisms that can be used to plan for future ecosystem resilience 

and achieve a balance of positive and negative feedbacks (Millar et al. 2007). 

5.3.1 Reducing Multiple Stressors 

The key to reducing stressors is to identify the factors that influence resilience. In 

many cases, management practices that increase resilience can be designed from existing 

knowledge; in other cases, however, it is not clear what management practices will 

enhance resilience (Millar et al. 2007). For example, connectivity in a fragmented 

landscape can be restored by creating corridors for species movement between suitable 

habitat patches (Gustafson, 1998). Alternatively, inadvertent connectivity that has been 

established and utilized by invasive species can be removed to reduce stress on the native 

populations remaining. 

To potentially mitigate for threshold crossing, it is likely that a variety of 

approaches, including both long-term and short-term strategies based on new information 

for natural resource management, will need to focus on increasing ecosystem resilience 

and resistance as well as assisting ecosystems to adapt to the inevitable changes as 

climates and environments continue to shift (Millar et al. 2007; Parker et al. 2000). 

Increasing management adaptive capacity is the operative action taken to increase 

resilience in ecosystems. For instance, increasing water storage capacity can provide a 

98 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

buffer against reaching the trigger point for a drought-induced threshold crossing that 

would permanently change an arid land ecosystem. The concept of critical loads for 

organisms is well established but can be productively applied to ecosystems. 

Based on gaps in the literature identified through the development process for this 

assessment (SAP 4.2) and the synthesis team’s expertise, tools to analyze and detect 

nonlinearity and thresholds from monitoring data will need to be developed. Increases in 

the variance of an important ecosystem metric have been suggested as an early sign of 

system instability. As negative feedbacks weaken and positive feedbacks strengthen, the 

likelihood that a threshold will be reached and crossed increases. As identified by the 

synthesis team in producing this assessment, there is a need for more nonlinear modeling 

and statistics to be applied to the threshold issue to identify the point at which positive 

feedbacks dominate. 

5.3.2 Triage 

Scientific evidence shows that climate change in the 21st century will most likely 

result in new vegetation successions, water regimes, wildlife habitat and survival 

conditions, permafrost and surface-ice conditions, coastal erosion and sea-level change, 

and human responses (Welch 2005). Triage is a process in which things are ranked in 

terms of importance or priority. The term environmental or ecological triage has been 

used to describe the prioritization process used by policymakers and decisionmakers to 

determine targets and approaches to dealing with resource allocation (for example, health 

of ecosystems) that are in high demand and rapidly changing. In the planning process, 

resource managers can address ecological triage under three different priorities: 1) status 

quo or do nothing; 2) reaction after disturbance; or 3) proactive intervention (Holt and 

99 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Viney 2001). Triage is a useful tool to prioritize actions, especially in cases where highly 

valued resources are at stake, conditions are changing rapidly, and decisions are urgent. 

The approaches to apply after triage are adaptive management, and mitigation and 

adaptation strategies. Enabling ecosystems to respond to climate change will help to ease 

the transition from current to future stable and resilient states and to minimize threshold 

changes (Fitzgerald 2000; Holt and Viney 2001; Millar et al. 2007; Millar in press). 

5.3.3 System-Level Planning and Policy 

Expanding management to regional levels is also key, because climate change 

may be pushing ecosystems to regional synchrony. In order to better understand and to 

manage thresholds, developing a regional perspective may prove to be more effective 

than using a local perspective. This regional approach would take into account large scale 

changes in climate regimes while still incorporating local scale resource issues. An 

example is that wildland fire is synchronously increasing throughout the western United 

States and could lead to major recruitment events for species such as lodgepole pine or 

trigger beetle outbreaks at unprecedented scales. These recruitment events could lead to 

supercohorts that develop with succession following subcontinental-scale disturbance. 

There is little management precedent for these types of outcomes that are threshold 

events on a continental scale, even if they are common on local scales. 

Adaptive management and structured decisionmaking will almost certainly be 

required to deal with increased temperature effects on threshold crossings and the 

different trajectories of succession that follow in the western United States. Natural 

systems are out of sync with climate, leading to the greatest potential for new species 

combinations in many centuries. Therefore, new actions may be considered, such as 

100 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

planting different tree genotypes after large-scale fires, with appropriate followup 

monitoring to learn from the results. 

5.3.4 Capacity Building and Awareness 

There is, and will be, an urgent need to adapt where climate-change-induced 

thresholds are crossed and a new ecosystem state will be a reality for the foreseeable 

future. Capacity building basically increases the resilience of the socioeconomic system 

to tolerate different states of natural resources and ecosystem functioning (Scott and 

Lemieux, 2005). If ecosystems become more variable in providing essential ecosystem 

services, greater flexibility is needed on the human side. An example is the need to add 

storage capacity for capturing mountain ecosystem water if a threshold in snow 

persistence is crossed, leading to smaller and more variable snowpacks. Building 

stakeholder tolerance for change is part of the adaptation that will be necessary (Scott and 

Lemieux, 2005). 

Adaptation can take many forms. Scenario planning provides descriptions of 

plausible future conditions. Scenario planning, done at the local level, makes stakeholders 

aware of the scope of uncertainty, facilitates tolerance for change, and motivates the 

desire to build capacity to better handle threshold changes. Multiscenario approaches 

used with ecosystem modeling can also be used to develop a range of possible postthreshold conditions to better inform strategic decisionmaking and planning for natural 

resource managers (Lemieux and Scott 2005). Impact assessments on specific resources 

(for example, population viability of individual species) can be expanded to examine the 

underlying viability of protected areas designed to maintain ecosystems (Scott and 

101 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Suffling 2000). These assessments can prepare managers by broadening the scope of 

planning and ensuring that institutional action plans remain flexible. 

5.4 Summary 

As this synthesis makes clear, climate change increases the likelihood that 

ecosystems will undergo threshold changes. The underlying mix of interacting feedback 

mechanisms that drive these thresholds is poorly understood. Monitoring of ecosystems 

to detect early indicators, such as increasing variability in system behavior, is generally 

inadequate even when it is known what aspect of the system to monitor. Based on gaps in 

the literature identified by the synthesis team, there is little scientific or natural resource 

management experience in dealing with ecosystems undergoing threshold changes. The 

degree to which we can reverse a threshold change is largely unknown. These knowledge 

gaps present scientists and resource managers with severe challenges in anticipating and 

coping with threshold changes to the natural systems. 

The gaps identified include the need to increase the resilience of ecosystems and 

reduce multiple stressors to avoid threshold crossing. Both of these challenges are 

difficult to plan for but also are consistent with managing ecosystems under conditions of 

uncertainty such as climate change. After a threshold crossing occurs, viable options are 

to increase coping mechanisms, adaptive capacity, and stakeholder tolerance. The 

publication of this assessment (SAP 4.2) will bring the state of scientific understanding to 

the forefront of the natural resource management paradigm, identifying a need for greater 

scientific research on thresholds and ecosystem response to adequately manage natural 

resources for the future. 

102 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Chapter 6—Summary and Science Recommendations 

This document reviews and summarizes much of what is understood about 

thresholds of ecological change. This is a nascent field of inquiry and even the definition 

of thresholds remains somewhat fluid. The discussion in chapter 2 clarifies what is meant 

by “threshold” and is intended to help focus future research on this topic. 

Summary 

Because of the enormous role they are believed to play in the tolerance of 

ecosystems to climate change, the existence of thresholds should be a key concern of 

scientists, Federal land managers, and other natural resource professionals responsible for 

the state of natural resources and the ecological services these resources provide. Sudden 

large-scale changes in ecosystems may present new challenges to resource managers 

because the capacity to predict, manage, and adapt to threshold crossings is currently 

limited. One goal of resource management is to minimize the risk of declines and 

uncertainty in the delivery of ecological goods and services but, as discussed in chapter 3, 

thresholds can precipitate such sudden declines and greatly increase management risks. 

Indeed, efforts by resource managers to reduce variance in the production of particular 

goods and services lead to a reduction in ecosystem resilience and increase the 

probability of threshold change. Current regulatory and legal frameworks do not account 

for threshold behavior of ecosystems. For this reason and because the social and 

economic costs of these precipitous collapses are potentially high (for example, the 

collapse of Atlantic cod population), we recommend the following possible actions be 

considered as a national priority. 

103 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Science Recommendations 

Given the knowledge that ecological thresholds exist and the lack of tools to 

predict them, scientists need to develop better predictive capabilities, and managers must 

make adjustments to increase their capacity to cope with surprises. If climate change is 

pushing more ecosystems toward thresholds, what can be done at the national level? In 

the development of SAP 4.2 the following potential actions were identified. The actions 

(or approaches) are organized according to those that can be taken before, during, and 

after thresholds of ecological change are crossed. 

Before 

Support Research To Identify Thresholds.—Although the existence of thresholds 

of ecological change is widely acknowledged, further advancement and agreement on the 

nature and effects of thresholds is limited by the small number of empirical studies that 

address this topic. Further advancement will depend on the development and use of 

rigorous tests to identify thresholds reliably across different systems. 

Enhance Adaptive Capacity.—Given that threshold changes are increasingly 

likely to occur, a “no-regrets” policy to prepare for them would enhance the capacity of 

the socioecological system to cope with change—that is, it would increase its resilience. 

To implement management changes that could reduce the likelihood of threshold 

changes, resource managers must first determine the factors that influence the resilience 

of the systems they manage. These determinations should consider the importance both 

of ecological diversity at patch and landscape scales and of economic diversity and 

innovation. The key components of diversity and adaptive capacity and resilience would 

need to be determined on a system-by-system basis and should include consideration of 

104 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

soil, plant, and animal disturbance, socioecological factors, and cross-scale interactions. 

A key assumption is that management plans that minimize diversity to maximize the 

provision of one particular ecosystem good or service are likely to increase the 

susceptibility of the system to threshold changes. 

Monitor and Adjust Multiple Factors and Drivers.—Once the key factors that 

control the adaptive capacity and resilience of a system have been identified, monitoring 

programs may be altered to include these factors, as well as the resources and ecological 

services of management interest. For example, monitoring the effects of increased 

salinity and (or) inundation from sea level rise on vegetation in coastal wetlands may 

make it possible to predict what degree of stress vegetation can endure before it goes 

beyond the ability to recover (Burkett et al., 2005). Monitoring soil conditions in areas 

that are susceptible to nonnative species invasions may make it possible to predict when 

invasive species may appear in a stressed ecosystem and push it beyond its threshold. It 

might also be useful to monitor the variability rather than mean values of an ecological 

service, because an increase in the amplitude of variability is sometimes an indication of 

system instability before a threshold is crossed. Another potential indicator is a slowing 

in response time (recovery time) to local perturbations; in certain theoretical scenarios, 

perturbations may grow larger in amplitude with an ever-increasing period of recovery as 

a threshold is approached (Van Nes and Scheffer, 2007). 

Current understanding suggests that thresholds are likely to be triggered when 

resource use pressures interact with gradual changes in climate that are associated with 

extreme climatic events, such as extended drought periods or hurricanes. Adjusting 

resource use provides one of the few near-term means available to mitigate thresholds. To 

105 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

enable rapid adjustments in resource use in at-risk places and time periods, it would be 

useful to put in place finer-grained climate and ecosystem monitoring systems coupled 

with administrative mechanisms to expedite policy modifications. 

Develop Scenarios To Explore Alternative Management Options for Dealing With 

Potential Changes.—The types of changes that cause threshold changes often are well 

known in advance (for example, hurricanes, wildfire, or invasive species). Scenario 

analysis with well-characterized dynamics can explore the potential consequences of 

taking actions either to reduce the likelihood of threshold change or minimize the impact 

of changes that occur. In this way, scenarios can provide managers with tools for action 

before the crisis occurs. 

Collate and Integrate Information Better at Different Scales.—Cross-scale 

interaction, where change in a large-scale variable, such as climate, alters a local-scale 

driver of threshold change, such as fire, is a great challenge in assessing and preventing 

threshold change. Greater efficiency and use of information is likely to result from 

coordinating and pooling information from adjoining jurisdictions and different agencies. 

For example, trends that are not significant or noticeable at small scales may be clear at 

larger scales. These and other observations argue for much better integration and 

coordination of monitoring information, not necessarily more monitoring. Although 

considerable investment would be needed to make monitoring “smarter” initially, the 

payoff would be the ability to detect early indicators of ecosystem change that could 

result in a threshold crossing. 

Reduce Other Stressors.—The points that may trigger an abrupt change in an 

ecosystem that is responding to climate change are rarely known because human 

106 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

civilizations have not witnessed climate change of this magnitude. However, the 

likelihood of crossing a threshold is most likely lessened by reducing other stressors on 

the ecosystem (Scott and Lemieux, 2005; Julius et al., 2008). These other stressors might 

include air and water pollution, regional landscape fragmentation, and control of invasive 

plants. To help reduce stressors, decisions could be made to allow larger or more 

extensive buffers when considering carrying capacity of habitats, minimum habitat sizes 

for species of interest, or use of ecological services, such as water. 

During 

Manage Threshold Shifts.—There may be constraints to reducing or reversing 

climate-change-induced stresses to components of an ecosystem. If a threshold seems 

likely to occur but the uncertainties remain high as to when it will occur, contingency 

plans can be created (Julius et al., 2008). These plans can be implemented when the 

threshold shift begins to occur or they can be carried out in advance if the onset of the 

threshold crossing is imminent. Take, for example, an Alpine area in which trees have 

begun to grow at higher elevations than the current tree line because reduced snowpack 

has lengthened the growing season. If this tree invasion of formerly open areas reduces 

animal movement between adjoining mountain areas, movement corridors can be kept 

open by mechanical clearing of trees. 

Project Impacts to Natural Resources.—Many efforts are underway to project 

climate change (for example, Global Climate Models) and ecosystem responses to 

climate change (for example, mapped atmosphere-plant-soil systems) using simulation 

models and other tools. These models generally project ecosystem trends and shifts, but 

they do not explicitly consider the possibility of thresholds as part of the system 

107 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

dynamics. To project impacts to natural resources accurately, it is necessary to 

understand, model, and project ecosystem responses to climate change with explicit 

acknowledgment of thresholds. An example of how the inclusion of thresholds in 

modeling would be beneficial is the bark beetle outbreak now occurring in Western 

forests where one threshold was passed when warmer winters allowed two lifecycles of 

beetle reproduction per year rather than one and where a second threshold may be passed 

by the expansion of the forests northward to connect with boreal forests that provide a 

corridor eastward. Such a scenario could lead to continental-scale beetle infestation 

(Logan et al., 1998). 

Recognize Need for Decisionmaking at Multiple Scales.—Climate change often 

expresses itself across regional boundaries which transcend local jurisdiction and 

management boundaries. The scale of some threshold crossings, such as the bark beetle 

example above, is likely to require coordinated decisions on larger scales than in the past. 

Because of different agency management mandates, levels of resources, or geographic 

scope, the potential exists for agencies to work at cross-purposes when coping with 

threshold effects at large scales. Also, the effectiveness of response can be enhanced 

through economies of scale if several agencies work on the problem simultaneously.

Instigate Institutional Change To Increase Adaptive Capacity.—The capacity for 

synthesis is a critical component of identifying potential thresholds in ecosystem 

processes on multiple scales. Institutional changes that promote greater interdisciplinary 

and interagency scientific and information exchange are likely to increase adaptive 

capacity in general. Such institutional changes would be especially helpful when 

108 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

implementing comprehensive monitoring to detect and document responses to thresholds 

in ecosystems. 

Identify Research Needs and Priorities To Address Thresholds.—Identifying 

research needs in general can help when evaluating calls for specific threshold research. 

The ubiquity of threshold problems across so many fields suggests the possibility of 

finding common principles at work. The cross-cutting nature of the problem of largescale system change suggests an unusual opportunity to leverage effort from other fields 

and apply it to investigating the systemic risk of crossing thresholds. Ecological and 

economic systems share common elements as complex adaptive systems. To the extent 

that the analogy holds, these two disciplines have potential for mutual leverage. Beyond 

the specific analogy between ecology and economics, certain dynamic behaviors and 

structural (topological network) constraints are common to broad classes of systems. 

Leverage can also occur by sharing methods across disciplines. Such diverse fields as 

engineering risk analysis, epidemiology, and ecology employ similar methods and 

research styles. The aim is not to replace conventional approaches but to explore 

complementary approaches. Exploiting commonalities is one way that leverage is 

achieved. 

As a further reality check on investments in research and development, 

management agencies can expand on efforts to examine their bottom-line performance as 

a normal part of the feedback and evaluation process. For many agencies, this will 

involve evaluating actual forecast skill as a measure of merit, rather than post-hoc fitting 

and correlation (the products of which may fit an existing paradigm but lack any 

predictive skill). Obtaining ground truth on this level can validate whether classical 

109 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

management concepts, such as maximum sustained yield in fisheries and other 

equilibrium concepts and models, are sufficiently useful to be predictive. A periodic 

evaluation process based on actual (real time) predictive power should indicate whether 

the model paradigm currently in use is an adequate representation of real systems, and 

whether the current direction of investments in research and development are on track. 

This level of verification is essential for effective management of threshold transitions. 

After 

Although many of the management responses to thresholds should be continued 

after thresholds have been crossed (for example, monitoring and building ecosystem 

resilience), human society will largely be faced with adjusting to different ecosystems. 

These adaptations may be expensive, requiring significant new physical and 

administrative infrastructures. Capacity building, scenario planning, and adaptive 

management must all be applied to quickly improve the ability of management to cope 

with a different ecosystem and for stakeholders to adjust their expectations of ecosystem 

services. 

Conclusion 

There is a need to develop a deeper understanding of thresholds of ecological 

change, especially given our current relative inability to predict when and where they will 

occur. There have, however, been enough occurrences with significant economic and 

social costs to warrant consideration of thresholds in natural resource planning and 

management. Threshold threats to many ecosystems are threats to long-term 

sustainability of human users as well as biodiversity and biological adaptive capacity. 

This document has summarized much of what is known about thresholds and has 

110 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

suggested approaches to improve understanding of thresholds, to reduce the chances of 

threshold crossing, and to enhance the ability to cope with thresholds that have occurred. 

Given the magnitude of climate change effects on ecosystems, the added factor of sudden 

threshold changes complicates societal responses and underscores the importance of 

continued integration of research and management to develop appropriate strategies for 

coping with thresholds. 

111 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

References Cited 

Aber, J.D., S.V. Ollinger, C.T. Driscoll, G.E. Likens, R.T. Holmes, R.J. Freuder, and 

C.L. Goodale, 2002. Inorganic nitrogen losses from a forested ecosystem in 

response to physical, chemical, biotic, and climatic perturbations. Ecosystems, 5, 

648–658. 

Aber, J.D., C.L. Goodale, S.V. Ollinger, M.-L. Smith, A.H. Magill, M.E. Martin, R.A. 

Hallett, and J.L. Stoddard, 2003. Is nitrogen deposition altering the nitrogen status 

of northeastern forests? BioScience, 53, 375–389. 

Abrego, David, K.E. Ulstrup, B.L. Willis, and M.J.H. van Oppen, 2008. Species-specific 

interactions between algal endosymbionts and coral hosts define their bleaching 

response to heat and light stress. Proceedings of the Royal Society, 275(1648), 

2273–2282. 

Adger, W.N., 2006. Vulnerability. Global Environmental Change, 16(3), 268–281. 

Adger, W.N., N.W. Arnell, and E.L. Tompkins, 2005. Successful adaptation to climate 

change across scales. Global Environmental Change, 15, 77–86. 

Albertson, F.W., and J.E. Weaver, 1942. History of the native vegetation of Western 

Kansas during seven years of continuous drought. Ecological Monographs, 12(1), 

23–51. 

Albertson, F.W., and J.E. Weaver, 1945. Injury and death or recovery of trees in prairie 

climate. Ecological Monographs, 15(4), 393–433. 

Allan, J.D., 2004. Landscapes and riverscapes: The influence of land use on stream 

ecosystems. Annual Review of Ecology, Evolution, and Systematics, 35, 257–284. 

112 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Allen, C.D., 2007. Interactions across spatial scales among forest dieback, fire, and 

erosion in northern New Mexico landscapes. Ecosystems, 10(5), 797–808. 

doi:10.1007/s10021–007–9057–4. 

Allen, C.D., and D.D. Breshears, 1998. Drought-induced shift of a forest/woodland 

ecotone: rapid landscape response to climate variation. Proceedings of the 

National Academy of Sciences, 95, 14839–14842. 

Allen, C.D., and D.D. Breshears, 2007. OOS Climate-induced forest dieback as an 

emergent global phenomenon: patterns, mechanisms, and projections. Eos, 

Transactions, AGU, 88(47), 504. 

Allen, C.R. and C.S. Holling, 2002. Adaptive inference for distinguishing credible from 

incredible patterns in nature. Ecosystems, 5(4), 319–328. 

Anderson, J.T., D. Van Holliday, Rudy Kloser, D.G. Reid, and Yvan Simard, 2008. 

Acoustic seabed classification: Current practice and future directions. Journal of 

Marine Science, 65(6), 1004–1011. 

Anderson, P.J., and J.F. Piatt, 1999. Community reorganization in the Gulf of Alaska 

following ocean climate regime shift. Marine Ecology Progress Series, 189, 117–

123. 

Arft, A.M., M.D. Walker, J. Gurevitch, J.M. Alatalo, M.S. Bret-Harte, M. Dale, M. 

Diemer, F. Gugerli, G.H.R. Henry, M.H. Jones, R. Hollister, I.S. Jónsdóttir, K. 

Laine, E. Lévesque, G.M. Marion, U. Molau, P. Mølgaard, Nordenhäll, V. 

Raszhivin, C.H. Robinson, G. Starr, A. Stenström, M. Stenström, Ø. Totland, L. 

Turner, L. Walker, P. Webber, J.M. Welker, and P.A. Wookey, 1999. Response 

113 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

patterns of tundra plant species to experimental warming: a meta-analysis of the 

International Tundra Experiment. Ecological Monographs, 69, 491–511. 

Baker, A.C., 2003. Flexibility and specificity in coral-algal symbiosis: diversity, ecology, 

and biogeography of Symbiodinium. Annual Review of Ecology, Evolution, and 

Systematics, 34, 661–689. 

Baker, A.C., 2004. Symbiont diversity on coral reefs and its relationship to bleaching 

resistance and resilience. In: Coral Health and Disease [E. Rosenberg and Y. 

Loya (eds.)]. Springer-Verlag, Berlin, pp. 177–191. 

Baker A.C., C.J. Starger, T.R. McClanahan, and P.W. Glynn, 2004. Coral reefs: corals' 

adaptive response to climate change. Nature, 430, 741. 

Balshi, M.S., A.D. McGuire, Paul Duffy, Mike Flannigan, John Walsh, and Jerry Melillo, 

2008. Assessing the response of area burned in western boreal North America 

using a multivariate adaptive regression splines (MARS) approach. Global 

Change Biology, doi:10.1111/j.1365-2486.2008.01679.x. 

Barber, V.A., G.P. Juday, and E. Berg, 2002. Assessment of recent and possible future 

forest responses to climate in boreal Alaska. In: Workshop on Northern 

Timberline Forests: Environmental and Socio-economic Issues and Concerns. 

Finnish Forest Research Institute. Kolari Research Station, pp. 102–105, 288–289. 

Barnett, T.P., D.W. Pierce, H.G. Hidalgo, C. Bonfils, B.D. Santer, T. Das, G. Bala, A.W. 

Wood, T. Nozawa, A.A. Mirin, D.R. Cayan, and M.D. Dettinger, 2008. Humaninduced changes in the hydrology of the western United States. Science, 319, 

1080–1083. 

114 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Barnett, T.P., J.C. Adam, and D.P. Lettenmaier, 2005. Potential impacts of a warming 

climate on water availability in snow-dominated regions. Nature, 438, 303–309. 

Beckage, Brian, Ben Osborne, D.G. Gavin, Carolyn Pucko, Thomas Siccama, and 

Timonthy Perkins, 2008. A rapid upward shift of a forest ecotone during 40 years 

of warming in the Green Mountains of Vermont. Proceedings of the National 

Academy of Sciences, 105, 4197–4202. 

Berkelmans, R., and M.J.H. van Oppen, 2006. The role of zooxanthellae in the thermal 

tolerance of corals: a "nugget of hope" for coral reefs in an era of climate change. 

Proceedings of the Royal Society of London, Series B: Biological Sciences, 273, 

2305–2312. 

Bestelmeyer, B.T, 2006. Threshold concepts and their use in rangeland management and 

restoration: The good, the bad, and the insidious. Restoration Ecology, 14(3), 

325–329. 

Betancourt, J.L., M.D. Schwartz, D.D. Breshears, C.A. Brewer, G. Frazer, J.E. Gross, S.J. 

Mazer, B.C. Reed, and B.E. Wilson, 2007. Evolving plans for the USA National 

Phenology Network. Eos, Transactions, AGU, 88(19), 211. 

Bigler, Christof, Dominik Kulakowski, and T.T. Veblen, 2005. Multiple disturbance 

interactions and drought influence fire severity in Rocky Mountain subalpine 

forests. Ecology, 86(11), 3018–3029. 

Birkeland, Charles, 2004. Ratcheting down the coral reefs. BioScience, 54, 1021–1027. 

Birkeland, Charles, Peter Craig, Douglas Fenner, Lance Smith, W.E. Kiene, and B.M. 

Riegl, 2008. Geologic setting and ecological functioning of coral reefs in 

115 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

American Samoa. In: Coral Reefs of the USA [Riegl, B.M., and R.E. Dodge 

(eds.)]. Springer, v. 1, pp. 741–766. 

Boulton, A.J., 2003. Parallels and contrasts in the effects of drought on stream 

macroinvertebrate assemblages. Freshwater Biology, 48, 1173–1185. 

Boulton, A.J., C.J. Peterson, N.B. Grimm, and S.G. Fisher, 1992. Stability of an aquatic 

macroinvertebrate community in a multiyear hydrologic disturbance regime. 

Ecology, 73, 2192–2207. 

Breshears, D.D., 2006. The grassland-forest continuum: trends in ecosystem properties 

for woody plant mosaics? Frontiers in Ecology and the Environment, 4(2), 96–

104. 

Breshears, D.D., 2007. Drought-induced vegetation mortality and associated ecosystem 

responses: examples from semiarid woodlands and forests. In: Understanding 

Multiple Environmental Stresses. National Academy Press, Washington, D.C., pp. 

89–95. 

Breshears, D.D., and C.D. Allen, 2002. The importance of rapid, disturbance-induced 

losses in carbon management and sequestration. Global Ecology and 

Biogeography Letters, 11, 1–15. 

Breshears, D.D., and F.J. Barnes, 1999. Interrelationships between plant functional types 

and soil moisture heterogeneity for semiarid landscapes within the 

grassland/forest continuum: a unified conceptual model. Landscape Ecology, 14, 

465–478. 

Breshears, D.D., N.S. Cobb, P.M. Rich, K.P. Price, C.D. Allen, R.G. Balice, W.H. 

Romme, J.H. Kastens, M.L. Floyd, Jayne Belnap, J.J. Anderson, O.B. Myers, and 

116 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

C.W. Meyer, 2005. Regional vegetation die-off in response to global-change-type 

drought. Proceedings of the National Academy of Sciences, 102, 15144–15148. 

Bruno, J.E., and E.R. Selig, 2007. Regional decline of coral cover in the Indo-Pacific: 

timing, extent, and subregional comparisons. PLoS ONE, 8, 711–718. 

Buddemeier, R.W., A.C. Baker, D.G. Fautin, and J.R. Jacobs, 2004. The adaptive 

hypothesis of bleaching. In: Coral Health and Disease [Rosenberg, Eugene, and 

Yossi Loya (eds.)]. Springer-Verlag, Berlin, p. 427–444. 

Burkett, V.R., D.A. Wilcox, Robert Stottlemyer, Wylie Barrow, Dan Fagre, Jill Baron, 

Jeff Price, J.L. Nielsen, C.D. Allen, D.L. Peterson, Greg Ruggerone, and Thomas 

Doyle, 2005. Nonlinear dynamics in ecosystem response to climatic change: case 

studies and policy implications. Ecological Complexity, 2, 357–394. 

Byrd, G.V., W.J. Sydeman, H.M. Renner, and Shoshiro Minobe, 2008. Contrasting 

responses of piscivorous seabirds at the Pribilof Islands to ocean climate. Deep 

Sea Research Part II: Topical Studies in Oceanography, 55(16–17), 1856-1867. 

Carpenter, S.R., 2002. Ecological futures: Building an ecology of the long now. Ecology, 

83, 2069–2083. 

Carpenter, S.R., and W.A. Brock, 2004. Spatial complexity, resilience, and policy 

diversity: fishing on lake-rich landscapes. Ecology and Society, 9(1), 8. 

Carpenter, S.R. and Monica Turner, 2000. Opening the black boxes: ecosystem science 

and economic valuation. Ecosystems, 3(1), 1–3. 

Carpenter, Stephen, William Brock, and Paul Hanson, 1999. Ecological and social 

dynamics in simple models of ecosystem management. Ecology and Society, 3(2), 

4. www.consecol.org/vol3/iss2/art4. 

117 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Chan, F., J.A. Barth, J. Lubchenco, A. Kirincich, H. Weeks, W.T. Peterson, and B.A. 

Menge, 2008. Emergence of anoxia in the California current large marine 

ecosystem. Science, 319, 920. 

Chapin, F.S., III, G.R. Shaver, A.E. Giblin, K.J. Nadelhoffer, and J.A. Laundre, 1995. 

Responses of arctic tundra to experimental and observed changes in climate. 

Ecology, 76(3), 694–711. 

Chapin, F.S., III , M.S. Bret-Harte, S.E. Hobbie, and Hailin Zhong, 1996. Plant functional 

types as predictors of transient responses of arctic vegetation to global change. 

Journal of Vegetation Science, 7(3), 347–358. 

Chapin, F.S., III, M. Sturm, M.C. Serreze, J.P. McFadden, J.R. Key, A.H. Lloyd, A.D. 

McGuire, T.S. Rupp, A.H. Lynch, J.P. Schimel, J. Beringer, W.L. Chapman, H.E. 

Epstein, E.S. Euskirchen, L.D. Hinzman, G. Jia, C.-L. Ping, K.D. Tape, C.D.C. 

Thompson, D.A. Walker, and J.M. Welker, 2005. Role of land-surface changes in 

arctic summer warming. Science, 310, 657–660. 

Chapin, F.S., III, S.F. Trainor, O. Huntington, A.L. Lovecraft, E. Zavaleta, D.C. Natcher, 

A.D. McGuire, J.L. Nelson, L. Ray, M. Calef, N. Fresco, H. Huntington, T.S. 

Rupp, L. DeWilde, and R.A. Naylor, 2008. Increasing wildfire in Alaska’s boreal 

forest: causes, consequences, and pathways to potential solutions of a wicked 

problem. BioScience, 58(6), 531–540. 

Claussen, Martin, Claudia Kubatzki, Victor Brovkin, Andrey Ganopolski, Phillipp 

Hoelzmann, Hans-Joachim Pachur, 1999. Simulation of an abrupt change in 

Saharan vegetation in the mid-Holocene. Geophysical Research Letters, 26(14), 

2037–2040. 

118 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Davenport, D.W., D.D. Breshears, B.P. Wilcox, and C.D. Allen.1998. Viewpoint: 

Sustainability of piñon-juniper ecosystems—A unifying perspective of soil 

erosion thresholds. Journal of Range Management, 51(2), 229–238. 

Denoël, Mathieu, and G.F. Ficetola, 2007. Landscape-level thresholds, and newt 

conservation. Ecological Applications, 17(1), 302–309. 

Dennis, R.L.H., and T.G. Shreeve, 1991. Climatic change and the British butterfly fauna: 

Opportunities and constraints. Biological Conservation, 55, 1–16. 

Diffenbaugh, N.S., J.S. Pal, R.J. Trapp, and Filippo Giorgi, 2005. Fine-scale processes 

regulate the response of extreme events to global climate change. Proceedings of 

the National Academy of Sciences, 102, 15,774–15,778. 

Dixon, P.A., M.J. Milicich, and George Sugihara, 1999. Episodic fluctuations in larval 

supply. Science, 283(5407), 1528–1530. 

Dobbertin, Matthias, Beat Wermelinger, Christof Bigler, Matthias Bϋrgi, Mathias Carron, 

Beat Forster, Urs Gimmi, and Andreas Rigling, 2007. Linking increasing drought 

stress to scots pine mortality and bark beetle infestations. Proceedings: Impacts of 

Air Pollution and Climate Chang eon Forest Ecosystems. 

TheScientificWorldJOURNAL, 7(S1), 231–239. 

Dodds, W.K., Keith Gido, M.R. Whiles, K.M. Fritz, and W.J. Matthews, 2004. Life on 

the edge: the ecology of Great Plains prairie streams. BioScience, 54(3), 205–216. 

Done, Terry, Lyndon Devantier, Emre Turak, Mary Wakeford, Abbi McDonald, and 

Craig Johnson, [in press]. Great Barrier Reef coral communities: resilient in the 

1980s but struggling in the 2000s. In: 11th International Coral Reef Symposium, 

Fort Lauderdale, Florida, July 7–11, 2008, abstract, pp. 106. 

119 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Douglas, M.R., P.C. Brunner, and M.E. Douglas, 2003. Drought in an evolutionary 

context: Molecular variability in flannelmouth sucker (Catostomus latipinnis) 

from the Colorado River basin of western North America. Freshwater Biology, 

48, 1254–1273. 

Dublin, H.T., A.R.E. Sinclair, J. McGlade, 1990. Elephants and fire as causes of multiple 

stable states in the Serengeti-Mara woodlands. The Journal of Animal Ecology,

59(3), 1147–1164. 

Dye, D.G., and C.J. Tucker, 2003. Seasonality and trends of snow-cover, vegetation 

index, and temperature in northern Eurasia. Geophysical Research Letters, 30, 45, 

doi:10.1029/GL016384. 

Dye, D.G., 2002. Variability and trends in the annual snow-cover cycle in Northern 

Hemisphere land areas, 1972–2000. Hydrological Processes, 16, 3065–3077. 

Easterling, D.R., G.A. Meehl, C. Parmesan, S.A. Changnon, T.R. Karl, and L.O. Mearns, 

2000. Climate extremes: observations, modeling, and impacts. Science, 289, 

2068–2074. 

EPRI, 2003. A Survey on Water Use and Sustainability in the United States With a Focus 

on Power Generation. Electric Power Research Institute, Palo Alto, CA, Topical 

Report 1005474, 86 pp. 

Eshleman, K.N., R.P. Morgan, J.R. Webb, F.A. Deviney, and J.N. Galloway, 1998. 

Temporal patterns of nitrogen leakage from mid-Appalachian forested 

watersheds: Role of forest disturbance. Water Resourcources Research, 34, 2005–

2116. 

120 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Euskirchen, E.S., A.D. McGuire, D.W. Kicklighter, Q. Zhuang, J.S. Clein, R.J. 

Dargaville, D.G. Dye, J.S. Kimball, K.C. McDonald, J.M. Melillo, V.E. 

Romanovsky, and N.V. Smith, 2006. Importance of recent shifts in soil thermal 

dynamics on growing season length, productivity, and carbon sequestration in 

terrestrial high-latitude ecosystems. Global Change Biology, 12(4), 731–750. 

Euskirchen, E.S., A.D. McGuire, and F.S. Chapin III, 2007. Energy feedbacks of 

northern high-latitude ecosystems to the climate system due to reduced snow 

cover during 20th century warming. Global Change Biology, 13(11), 2425–2438. 

Feely, R.A., C.L. Sabine, J. M. Hernandez-Ayon, Debby Lanson, and Burke Hales, 2008. 

Evidence for upwelling of corrosive “acidified” water onto the continental shelf. 

Science, 320(5882), 1490–1492. 

Fensham R.J., and J.E. Holman, 1999. Temporal and spatial patterns in drought-related 

tree dieback in Australian savanna. Journal of Applied Ecology, 36, 1035–1060. 

Fisher, M., 1997. Decline in the juniper woodlands of Raydah reserve in southwestern 

Saudi Arabia: A response to climate changes? Global Ecology and Biogeography 

Letters, 6, 379–386. 

Fitzgerald, G., 2000. Triage. In: Textbook of Adult Emergency Medicine [P. Cameron, G. 

Jelinke, A. M. Kelly, L. Murray, and J. Heyworth (eds.)]. Churchill Livingston, 

Sydney, pp. 584–588. 

Fleming, R.A., and W.J.A. Volney, 1995. Effects of climate change on insect defoliator 

population processes in Canada’s boreal forest: Some plausible scenarios. Water, 

Air, & Soil Pollution, 82(1–3), 445–454. 

121 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Foley, J.A., M.T. Coe, Marten Scheffer, and Guiling Wang, 2003. Regime shifts in the 

Sahara and Sahel: interactions between ecological and climatic systems in 

Northern Africa. Ecosystems, 6(6), 524–532. 

Folke, Carl., 2006. Resilience: the emergence of a perspective for social-ecological 

systems analyses. Global Environmental Change, 16(3), 253–267. 

Folke, Carl, Johan Colding, and Fikret Berkes, 2003. Building resilience and adaptive 

capacity in social-ecological systems. In: Navigating Social-Ecological Systems

[Berkes, Fikret, Johan Colding, and Carl Folke (eds.)]. Cambridge University 

Press, pp. 352–387. 

Fortin, M.-J., Pierre Drapeau, and Pierre Legendre, 1989. Spatial autocorrelation and 

sampling design in plant ecology. Plant Ecology, 83(1–2), 209–222. 

Gardner, T.A., I.M. Côté, J.A. Gill, Alastair Grant, and A.R. Watkinson, 2003. Longterm region-wide declines in Caribbean corals. Science, 301, 958–960. 

Garrison, V.H., E.A. Shinn, W.T. Foreman, D.W. Griffin, C.W. Holmes, C.A. Kellogg, 

M.S. Majewski, L.L. Richardson, K.B. Ritchie, and G.W. Smith, 2003. African 

and Asian dust: From desert soils to coral reefs. BioScience, 53, 469–480. 

Gibson, C.A., J.L. Meyer, N.L. Poff, L.E. Hay, and A. Georgakakos, 2005. Flow regime 

alterations under changing climate in two river basins: Implications for freshwater 

ecosystems. River Research and Applications, 21, 849–864. 

Gitlin A.R., C.M. Stultz, M.A. Bowker, Stacy Stumpf, K.L. Paxton, Karla Kennedy, 

Axhel Muñoz, J.K. Bailey, and T.G. Whitham, 2006. Mortality gradients within 

and among dominant plant populations as barometers of ecosystem change during 

extreme drought. Conservation Biology, 20(5), 1477–1486. 

122 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Goetz, S.J., A.G. Bunn, G.J. Fiske, and R.A. Houghton, 2005. Satellite-observed 

photosynthetic trends across boreal North America associated with climate and 

fire disturbance. Proceedings of the National Academy of Sciences, 102(38), 

13521–13525. 

Gonzalez, P., 2001. Desertification and a shift of forest species in the West African 

Sahel. Climate Research, 17, 217–228. 

Grebmeier, J.M., J.E. Overland, S.E. Moore, E.V. Farley, E.C. Carmack, L.W. Cooper, 

K.E. Frey, J.H. Helle, F.A. McLaughlin, and S.L. McNutt, 2006. A major 

ecosystem shift in the northern Bering Sea. Science, 311, 1461–1464. 

Grenfell, B.T., 1992. Chance and chaos in measles dynamics. Journal of the Royal 

Statistical Society, series B (methodological,) 54(2), 383–398. 

Groffman, P.M., J.S. Baron, Tamara Blett, A.J. Gold, Iris Goodman, L.H. Gunderson, 

B.M. Levinson, M.A. Palmer, H.W. Paerl, G.D. Peterson, N.L. Poff, D.W. 

Rejeski, J.F. Reynolds, M.G. Turner, K.C. Weathers, and John Wiens, 2006. 

Ecological thresholds: The key to successful environmental management or an 

important concept with no practical application? Ecosystems, 9, 1–13. 

Gu, L., P.J. Hanson, W.M. Post, D.P. Kaiser, B. Yang, R. Nemani, S.G. Pallardy, and T. 

Meyers, 2008. The 2007 Easter U.S. spring freeze: Increased cold damage in a 

warming world?. BioScience, 58, 253–262. 

Gunderson, Lance, and C.S. Holling (eds.), 2002. Panarchy: Understanding 

Transformations in Human and Natural Systems. Island Press, Washington, DC, 

508 pp. 

123 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Gustafson, E.J., 1998. Quantifying landscape spatial pattern: what is the state of the art? 

Ecosystems, 1, 143–156. 

Hallock, P., F.E. Müller-Karger, and J.C. Halas, 1993. Anthropogenic nutrients and the 

degradation of Caribbean coral reefs. National Geographic Research and 

Explorations, 9(3), 358–378. 

Hansell, R.I.C., J.R. Malcolm, Harold Welch, R.L. Jeffries, and P.A. Scott, 1998. 

Atmospheric change and biodiversity in the Arctic. Environmental Monitoring 

and Assessment, 49(2–3), 303–325. 

Hansen, James, Makiko Sato, Reto Ruedy, Ken Lo, D.W. Lea, and Martin MedinaElizade, 2006. Global temperature change. Proceedings of the National Academy 

of Sciences, 103(30), 14288–14293. 

Hanski, Ilkka, Peter Turchin, Erkki Korpimaki, and Heikki Henttonen, 1993. Population 

oscillations of boreal rodents: regulation by mustelid predators leads to chaos. 

Nature, 364, 232–235. 

Hare, S.R., and N.J. Mantua, 2000. Empirical evidence for North Pacific regime shifts in 

1977 and 1989. Progress in Oceanography, 47(2–4), 103–145. 

Hatfield, J.L., K.J. Boote, B.A. Kimball, D.W. Wolfe, D.R. Ort, R.C. Izaurralde, A.M. 

Thomson, J.A. Morgan, H.W. Polley, P.A. Fay, T.L. Mader, G.L. Hahn, 2008. 

Agriculture. In: The Effects of Climate Change on Agriculture, Land Resources, 

Water Resources, and Biodiversity. Synthesis and assessment product 4.3 by the 

U.S. climate change science program and the subcommittee on global change 

research, Washington, D.C. 

124 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Herrick, J.E., B.T. Bestelmeyer, S. Archer, A.J. Tugel, and J.R. Brown, 2006. An 

integrated framework for science-based arid land management. Journal of Arid 

Environments, 65, 319–335. 

Higgins, P. A. T., M. D. Mastrandrea, and S. H. Schneider, 2002. Dynamics of climate 

and ecosystem coupling: Abrupt changes and multiple equilibria. Philosophical 

Transactions of the Royal Society of London Series B–Biological Sciences, 357, 

647–655. 

Hinzman L.D., N.D. Bettez, W.R. Bolton, F.S. Chapin, M.B. Dyurgerov, C.L. Fastie, 

Brad Griffith, R.D. Hollister, Allen Hope, H.P. Huntington, A.M. Jensen, G.J. Jia, 

Torre Jorgenson, D.L. Kane, D.R. Klein, Gary Kofinas, A.H. Lynch, A.H. Lloyd, 

A.D. McGuire, F.E. Nelson, W.C. Oechel, T.E. Osterkamp, C.H. Racine, V.E. 

Romanovsky, R.S. Stone, D.A. Stow, Matthew Sturm, C.E. Tweedie, G.L. 

Vourlitis, M.D. Walker, D.A. Walker, P.J. Webber, J.M. Welker, K.S. Winker, 

and Kenji Yoshikawa, 2005. Evidence and implications of recent climate change 

in terrestrial regions of the Arctic. Climatic Change, 72(3), 251–298. 

Hobbs, R.J., S. Arico, J. Aronson, J.S. Barron, P. Bridgewater, V.A. Cramer, P.R. 

Epstein, J.J. Ewel, C.A. Klink, A.E. Lugo, D. Norton, D. Ojima, D.M. 

Richardson, E.W. Sanderson, F. Valladares, M. Vila, R. Zamora, M. Zobel, 2006. 

Novel ecosystems: theoretical and management aspects of the new ecological 

world order. Global Ecology and Biogeography, 15, 1–7. 

Hoegh-Guldberg, O., P.J. Mumby, A.J. Hooten, R.S. Steneck, P. Greenfield, E. Gomez, 

C.D. Harvell, P.F. Sale, A.J. Edwards, K. Caldeira, N. Knowlton, C.M. Eakin, R. 

Iglesias-Prieto, N. Muthiga, R.H. Bradbury, A. Dubi, and M.E. Hatziolos, 2007. 

125 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Coral reefs under rapid change and ocean acidification. Science, 318(5857), 

1737–1742. 

Holland, M., C. Blitz, and B. Tremblay, 2006. Future abrupt reductions in the summer 

Arctic sea ice. Geophysical Research Letters, 33, L23503. 

Holling, C.S., 1973: Resilience and stability of ecological systems. Annual Review of 

Ecology, Evolution, and Systematics, 4, 1–23. 

Holling, C.S., 1986. The resilience of terrestrial ecosystems; local surprise and global 

change. In: Sustainable Development of the Biosphere [Clark, W.C., and R.E. 

Munn (eds.)]. Cambridge University Press, pp. 292–317. 

Holling, C.S., 1992: Cross-scale morphology, geometry, and dynamics of ecosystems. 

Ecological Monographs, 62(4) 447–502. 

Holling, C.S., ed., 1978. Adaptive environmental assessment and management. John 

Wiley & Sons, London. 

Holt, D. and H. Viney, 2001. Targeting environmental improvements through ecological 

triage. Eco-Management and Auditing, 8, 154–164. 

Houghton, J.T., L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, K. Maskell, 

1996. Climate change 1995: the science of climate change. Cambridge University 

press. 

Hsieh, C.H., C.S. Reiss, J.R. Hunter, J.R. Beddington, R.M. May, and George Sugihara, 

2006: Fishing elevates variability in the abundance of exploited species. Nature, 

443, 859–862. 

Hsieh, Chih-hao, Christian Reiss, William Watson, M.J. Allen, J.R. Hunter, R.N. Lea, 

R.H. Rosenblatt, P.E. Smith, and George Sugihara, 2005. A comparison of long126 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

term trends and variability in populations of larvae of exploited and unexploited 

fishes in the Southern California region: a community approach. Progress in 

Oceanography, 67(1–2), 160–185. 

Huggett, A.J., 2005. The concept and utility of ‘ecological thresholds’ in biodiversity 

conservation. Biological Conservation, 124(3), 301–310. 

Hunt, G.L., Jr., and P.J. Stabeno, 2002. Climate change and the control of energy flow in 

the southeastern Bering Sea. Progress in Oceanography, 55(1–2), 5–22. 

Hunt, G.L., Jr., Phyllis Stabeno, Gary Walters, Elizabeth Sinclair, R.D. Brodeur, J.M. 

Napp, and N.A. Bond, 2002: Climate change and the control of the southeastern 

Bering Sea pelagic ecosystem. Deep Sea Research II, 49(26), 5821–5853. 

IPCC, 1996. Climate Change 1995: The Science of Climate Change. Cambridge 

University Press. 

IPCC, 2007a: Climate Change 2007: The Physical Science Basis. Contribution of 

Working Group I to the Fourth Assessment Report of the Intergovernmental Panel 

on Climate Change [Core Writing Teanm, R.K. Pachauri, and Andy Reisinger 

(eds.)]. Intergovernmental Panel on Climate Change, Geneva, Switzerland, 104 

pp. 

IPCC, 2007b. htpp://www.ipcc.ch, accessed February 10, 2007. 

ISRS, 2007. Coral Reefs and Ocean Acidification. International Society for Reef Studies, 

Briefing Paper 5, 8 pp. 

Jia, G.J., H.E. Epstein, and D.A. Walker, 2003. Greening of arctic Alaska, 1981–2001. 

Geophysical Research Letters, 30, 2067, doi:10.1029/2003GL018268,2003. 

127 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Johnson, W.C., B.V. Millett, Tagir Gilmanov, R.A. Voldseth, G.R. Guntenspergen, and 

D.E. Naugle, 2005. Vulnerability of northern prairie wetlands to climate change. 

BioScience, 55(10), 863–872. 

Jorgenson, M. Torre, C.H. Racine, J.C. Walters, T.E. Osterkamp, 2001. Permafrost 

degradation and ecological changes associated with a warming climate in central 

Alaska. Climatic Change, 48(4), 551–579. 

Juday, G.P., V. Barber, P. Duffy, H. Linderholm, T.S. Rupp, S. Sparrow, E. Vaganov, 

and J. Yarie, 2005. Forests, land management, and agriculture (chapter 14). In: 

Arctic Climate Impact Assessment, Cambridge University Press, pp. 781–862. 

Julius, S., J. West, J. Baron, L. Joyce, P. Kareiva, B. Keller, M. Palmer, C. Peterson, and 

J.M. Scott, 2008. Preliminary review of adaptation options for climate-sensitive 

ecosystems and resources. Synthesis and assessment product 4.4 by the U.S. 

climate change science program and the subcommittee on global change research, 

Washington, D.C., 873 p. 

Kasischke, E.S., and M.R. Turetsky, 2006. Recent changes in the fire regime across the 

North American boreal region—Spatial and temporal patterns of burning across 

Canada and Alaska. Geophysical Research Letters, 33, L09703, 

doi:09710.01029/02006GL025677. 

Kasischke, E.S., David Williams, and Donald Barry, 2002. Analysis of the patterns of 

large fires in the boreal forest region of Alaska. Intlernational Journal of 

Wildland Fire, 11(2), 131–144. 

Kasischke, E.S., T.S. Rupp, and D.L. Verbyla, 2006. Fire trends in the Alaskan boreal 

forest. In: Alaska’s Changing Boreal Forest [Chapin, F.S., III, M.W. Oswood, K. 

128 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Van Cleve, L.A. Viereck, and D.L. Verbyla (eds.)]. Oxford University Press, New 

York, pp. 285–301. 

Keeley, J.E., 2006. Fire management impacts on invasive plants in the western United 

States. Conservation Biology 20, 375–384. 

King, R.S., C.J. Richardson, D.L. Urban, and E.A. Romanowicz, 2004. Spatial 

dependency of vegetation-environment linkages in an anthropogenetically 

influenced wetland ecosystem. Ecosystems, 7(1), 75–97. 

Kinzig, A.P., P. Ryan, M. Etienne, H. Allison, T. Elmqvist, and B.H. Walker, 2006. 

Resilience and regime shifts: assessing cascading effects. Ecology and Society,

11(1), 20. 

Klein, Eric, E.E. Berg, and Roman Dial, 2005. Wetland drying and succession across the 

Kenai Peninsula lowlands, south-central Alaska. Canadian Journal of Forest 

Research, 35, 1931–1942. 

Kleypas, J.A., J.W. McManus, and L.A.B. Meñez, 1999. Environmental limits to coral 

reef development: where do we draw the line? American Zoologist, 39, 146–159. 

Kleypas, J.A., R.W. Buddemeier, and J.-P. Gattuso, 2001. The future of coral reefs in an 

age of global change. International Journal of Earth Sciences, 90, 426–437. 

Knowlton, Nancy, 1992. Thresholds and multiple stable states in coral reef community 

dynamics. American Zoologist, 32(6), 674–682. 

Kurz, W.A., and M.J. Apps, 1999. A 70-year retrospective analysis of carbon fluxes in 

the Canadian forest sector. Journal of Applied Ecology, 9(2), 526–547. 

Kurz, Werner A., Graham Stinson, Gregory J. Rampley, Caren C. Dymond, and Eric T. 

Nelson, 2008. Risk of natural disturbances makes future contribution of Canada’s 

129 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

forests to the global carbon cycle highly uncertain. Proceedings of the National 

Academy of Sciences, 105(5), 1551–1555. 

Laird, K.R. and B.F. Cumming, 1998. Tracing droughts into the past. Science Spectra, 

11, 50–57. 

Lake, P.S., 2003: Ecological effects of perturbation by drought in flowing waters. 

Freshwater Biology, 48, 1161–1172. 

Lee, K.N., 1993. Compass and Gyroscope: Integrating Science and Politics for the 

Environment. Island Press, Washington, D.C. 

Lemieux, C.J., and D.J. Scott, 2005. Climate change, biodiversity conservation and 

protected area planning in Canada. Canadian Geographer, 49(4), 384–397. 

Lessios, H.A., D.R. Robertson, and J.D. Cubit, 1984. Spread of Diadema mass mortality 

through the Caribbean. Science, 226, 335–337. 

Lewontin, R.C., 1969. The meaning of stability. Symposia in Biology, 22, 13–24. 

Light, H.M., M.R. Darst, and J.W. Grubbs, 1998. Aquatic Habitats in Relation to River 

Flow in the Apalachicola River Floodplain, Florida. U.S. Geological Survey 

Professional Paper 1594, 77 pp., 3 pl. 

Lite, S.J., and J.C. Stromberg, 2005. Surface water and ground-water thresholds for 

maintaining Populus-Salix forests, San Pedro River, Arizona. Biological 

Conservation, 125, 153–167. 

Litzow, M.A., J.D. Urban, and B.J. Laurel, 2008. Increased spatial variance accompanies 

reorganization of two continental shelf ecosystems. Ecological Applications, 18, 

1331–1337. 

130 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Lloyd, A.H., and C.L. Fastie, 2003. Recent changes in tree line forest distribution and 

structure in interior Alaska. Ecoscience, 10(2), 176–185. 

Logan, J.A., Peter White, Barbara Bentz, and J.A. Powell, 1998. Model analysis of 

spatial patterns in mountain pine beetle outbreaks. Theoretical Population 

Biology, 53(3), 236–255. 

Loughlin, T.R., I.N. Sukhanova, E.H. Sinclair, and R.C. Ferrero, 1999. Summary of 

biology and ecosystem dynamics in the Bering Sea. In: Dynamics of the Bering 

Sea [Loughlin, T.R., and K. Ohtani (eds.)]. University of Alaska sea grant AK–

SG–99–03, pp. 387–407. 

Lovett, G.M., K.C. Weathers, and W.V. Sobczak, 2000. Nitrogen saturation and retention 

in forested watersheds of the Catskill Mountains, New York. Journal of Applied 

Ecology, 10, 73–84. 

Lovett, G.M., L.M. Christenson, P.M. Groffman, C.G. Jones, J.E. Hart, and M.J. 

Mitchell, 2002. Insect defoliation and nitrogen cycling in forests. BioScience, 52, 

335–341. 

J.R. Lovvorn, S.E. Richman, J.M. Grebmeier, and L.W. Cooper, 2003. Diet and body 

condition of spectacled eiders wintering in pack ice of the Bering Sea. Polar 

Biology, 26, 259–267. 

Ludwig, D., D.D. Jones, and C.S. Holling, 1978. Qualitative analysis of insect outbreak 

systems: the spruce budworm and forest. Journal of Animal Ecology, 47(1), 315–

332. 

131 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Ludwig, J.A., B.P. Wilcox, D.D. Breshears, D.J. Tongway, and A.C. Imeson, 2005. 

Vegetation patches and runoff-erosion as interacting ecohydrological processes in 

semiarid landscapes. Ecology, 86(2), 288–297. 

Ludwig, J.A., Wiens, J.A., Tongway, D.J, 2000. A scaling rule for landscape patches and 

how it applies to conserving soil resources in savannas. Ecosystems, 3, 84–97. 

Macklin, S.A., and G.L. Hunt, Jr. (eds.), 2004. The Southeast Bering Sea Ecosystem: 

Implications for Marine Resource Management. National Oceanographic and 

Atmospheric Administration Coastal Ocean Program Decision Analysis Series 

No. 24, 192 pp. 

Margalef, R., 1963. On certain unifying principles in ecology. American Naturalist, 

97(897), 357–374. 

Matthews, W.J., and Edie Marsh-Matthews, 2003. Effects of drought on fish across axes 

of space, time and ecological complexity. Freshwater Biology, 48(7), 1232–1253. 

May, R.M., 1973. Stability and Complexity in Model Ecosystems. Princeton University 

Press, 292 pp. 

May, R.M., 1977. Thresholds and breakpoints in ecosystems with a multiplicity of stable 

states. Nature, 269(5628), 471–477. 

May, R.M., S.A. Levin, and George Sugihara, 2008. Complex systems: ecology for 

bankers. Nature, 451, 893–895. 

McCann, K.S., 2000. The diversity-stability debate. Nature, 405, 228–233. 

McClanahan, T.R., Maina, J., Moothien-Pillay, R., and A.C. Baker, 2005. Effects of 

geography, taxa, water flow, and temperature variation on coral bleaching 

intensity in Mauritius. Marine Ecology Progress Series, 298, 131–142. 

132 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

McDowell, N., W.T. Pockman, C.D. Allen, D.D. Breshears, N. Cobb, T. Kolb, J. Sperry, 

A. West, D. Williams, E.A. Yepez, 2008. Mechanisms of plant survival and 

mortality during drought: why do some plants survive while others succumb to 

drought? Tansley Review, New Phytologist, doi:10.1111/j.1469–

8137.2008.02436.x. 

McKenzie, D., and C.D. Allen, 2007. Meetings: climate change and disturbance 

interactions: workshop on climate change and disturbance interactions in western 

North America, Tucson, Ariz., 12–15 February 2007. Eos, Transactions, AGU,

88(21), 227. 

Michener, W.K., T.J. Baerwald, Penelope Firth, M.A. Palmer, J.L. Rosenberger, E.A. 

Sandlin, and Herman Zimmerman, 2001. Defining and unraveling biocomplexity. 

BioScience, 51(12), 1018–1023. 

Miles, E.L., A.K. Snover, A.F. Hamlet, B. Callahan, and D. Fluharty, 2007. Pacific 

Northwest Regional Assessment: The impacts of climate variability and climate 

change on the water resources of the Columbia River Basin. Journal of American 

Water Resources Association, 36, 399–420. 

Millar, C.I., N.L. Stephenson, and S.L. Stephens, 2007. Climate change and forests of the 

future: managing in the face of uncertainty. Ecological Applications, 17, 2145–

2151. 

Millenium Ecosystem Assessment, 2005. Ecosystems and Human Well-Being: 

Biodiversity Synthesis. World Resources Institute, Washington, D.C., 100 pp. 

Miller, W.D., S.C. Neubauer, and I.C. Andereson, 2001. Effects of sea level induced 

disturbances on high salt marsh metabolism. Estuaries, 24(3), 357–367. 

133 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Milne, B.T., 1998. Motivation and benefits of complex systems approaches in ecology. 

Ecosystems, 1(5), 449–456. 

Mitchell, M.J., C.T. Driscoll, J.S. Khal, G.E. Likens, P.S. Murdoch, and L.H. Pardo, 

1996. Climatic control of nitrate loss from forested watersheds in the northeast 

United States. Environmental Science and Technology, 30(8), 2609–2612. 

Moody, J.A., and D.A. Martin, [in press]. Synthesis of sediment yields after wildland fire 

in different rainfall regimes in the Western United States. International Journal of 

Wildland Fire. 

Moorcroft, P.R., G.C. Hurtt, and S.W. Pacala, 2001. A method for scaling vegetation 

dynamics: the ecosystem demography model (ED). Ecological Monographs,

71(4), 557–586. 

Moore, S.E., J.M. Grebmeier, and J.R. Davis, 2003. Gray whale distribution relative to 

forage habitat in the northern Bering Sea: current conditions and retrospective 

summary. Canadian Journal of Zoology, 81, 734–742. 

Mueter, F.J., and M.A. Litzow, 2008. Sea-ice retreat alters the biogeography of the 

Bering Sea continental shelf. Ecological Applications, 18, 309–320. 

Mulholland, P.J., B.J. Roberts, W.R. Hill, and J.G. Smith, [in press]. Stream ecosystem 

responses to the 2007 spring freeze in the southeastern United States: unexpected 

effects of climate change. Global Change Biology. 

Muradian, Roldan, 2001. Ecological thresholds: a survey. Ecological Economics, 38(1), 

7–24. 

Murdoch, Thaddeus, Annie Glasspool, and Mike Colella, 2008. The results of the first 

comprehensive benthic assessment of the coral reef habitats of Bermuda. In: 11th 

134 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

International Coral Reef Symposium, Fort Lauderdale, Florida, July 7–11, 2008, 

abstract, pp. 451. 

NRC, 2002. Interim report from the Committee on Endangered and Threatened Fishes in 

the Klamath River Basin: scientific evaluation of biological opinions on 

endangered and threatened fishes in the Klamath River Basin. National Academy 

Press, Washington, D.C., 33 pp. 

Neilson, R.P., 1993. Transient ecotone response to climatic change: some conceptual and 

modeling approaches. Ecological Applications, 3(3), 385–395. 

Neitzel, D.A., M.J. Scott, S.A. Shankle and J.C. Chatters, 1991. The effect of climate 

change on stream environments: the salmonid resource of the Columbia River 

Basin. Northwest Environmental Journal, 7, 271–293. 

Nelson, K.C., and M.A. Palmer, 2007. Stream temperature surges under urbanization and 

climate change: Data, models, and responses. Journal of the American Water 

Resources Association, 43, 440–452. 

Newmaster, S.G., W.C. Parker, Wayne Bell, and J.M. Paterson, 2007. Effects of forest 

floor disturbances by mechanical site preparation on floristic diversity in a central 

Ontario clearcut. Forest Ecology and Management, 246(2–3), 196–207. 

Nkendirim, Lawrence, and Lena Weber, 1999. Comparison between the drought of the 

1930’s and the 1980’s in the southern prairies of Canada. Journal of Climate,

12(8), 2434–3450. 

Obst, B.S., R.W. Russell, G.L. Hunt, Z.A. Eppley, and N.M. Harrison, 1995. Foraging 

radii and energetics of least auklets (Aethia pusilla) breeding on three Bering Sea 

islands. Physiological Zoology, 68, 647–672. 

135 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Odum, E.P., 1969. The strategy of ecosystem development. Science, 164(3877), 262–

270. 

Ojima, D.S., and J.M. Lackett, and Central Great Plains Steering Committee and 

Assessment Team, 2002. Preparing for a Changing Climate: The Potential 

Consequences of Climate Variability and Change—Central Great Plains. Report 

for the US Global Change Research Program. Colorado State University, 103 pp. 

O'Neill, R.V., D.L. DeAngelis, J.B. Waide, and T.F.H. Allen, 1986. A Hierarchical 

Concept of Ecosystems. Princeton University Press, 262 p. 

Osterkamp, T.E., 2007. Characteristics of the recent warming of permafrost in Alaska. 

Journal of Geophysical Research, 112, F02S02, doi:10.1029/2006JF000578. 

Osterkamp, T.E., 2007. Characteristics of the recent warming of permafrost in Alaska. 

Journal of Geophysical Research, 112, F02S02. 

Osterkamp, T.E., and V.E. Romanovsky, 1999. Evidence for warming and thawing of 

discontinuous permafrost in Alaska. Permafrost and Perglacial Processes, 10, 

17–37. 

Osterkamp, T.E., D.C. Esch, and V.E. Romanovsky, 1997. Infrastructure: effects of 

climatic warming on planning, construction and maintenance. In: Proceedings of 

the BESIS Workshop. University of Alaska, Fairbanks, AK, pp. 115–127. 

Osterkamp, T.E., L. Viereck, Y. Shur, M.T. Jorgenson, C.H. Racine, A.P. Doyle, and 

R.D. Boone, 2000. Observations of thermokarst and its impact on boreal forests in 

Alaska. Arctic, Antarctic, and AlpineResearch, 32(3), 303–315. 

Overland, J.E., and P.J. Stabeno, 2004. Is the climate of the Bering Sea warming and 

affecting the ecosystem? Eos, Transactions, AGU, 85, 309–316. 

136 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Paulay, G., 1997. Diversity and distribution of reef organisms. In: Life and Death of 

Reefs [C. Birkeland (ed.)]. Chapman and Hall, pp. 298–353. 

Palmer, M.A., C.A. Reidy, Christer Nilsson, Martina Flörke, Joseph Alcamo, P.S. Lake, 

and Nick Bond, 2007. Climate change and the world’s river basins: anticipating 

management options. Frontiers in Ecology and the Environment, 6(2), 81–89. 

Palmer, M.A., J.D. Allan, and C.A. Butman, 1996. Dispersal as a regional process 

affecting the local dynamics of marine and stream benthic invertebrates. Trends in 

Ecology and Evolution, 11(8), 322–326. 

Parker, W.C., S.J. Colombo, M.L. Cherry, M.D. Flannigan, Sylvia Greifenhagen, Chris 

Papadopol, M.D. Flannigan, R.S. McAlpine, and Taylor Scarr, 2000. Third 

millennium forestry: what climate change might mean to forests and forest 

management in Ontario. Forestry Chronicle, 76(3), 445–463. 

Parmesan, Camille, and Hector Galbraith, 2004. Observed impacts of global climate 

change in the United States. Pew Center on Global Climate Change, Washington 

D.C., 56 p. 

Parr, T.W., A.R.J. Sier, R.W. Battarbee, A. Mackay, and J. Burgess, 2003. Detecting 

environmental change: science and society—Perspectives and long-term research 

and monitoring in the 21st century. Science of the Total Environment, 310(1–3), 

1–8.

Pascual, Mercedes, and S.P. Ellner, 2000. Linking ecological patterns to environmental 

forcing via nonlinear time series models. Ecology, 81(10), 2767–2780. 

Paul, M.J., and J.L. Meyer, 2001. Streams in the urban landscape. Annual Review of 

Ecology, Evolution, and Systematics, 32, 333–365. 

137 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Payne, J.T., A.W. Wood, A.F. Hamlet, R.N. Palmer and D.P. Lettenmaier, 2004. 

Mitigating the effects of climate change on the water resources of the Columbia 

River Basin. Climatic Change, 62, 233–256. 

Peters, D.P.C., B.T. Bestelmeyer, and M.G. Turner, 2007. Cross-scale interactions and 

changing pattern-process relationships: consequences for system dynamics. 

Ecosystems, 10, 790–796. 

Peters, D.P.C., B.T. Bestelmeyer, J.E. Herrick, E.L. Fredrickson, H.C. Monger, and K.M. 

Havstad, 2006. Disentangling complex landscapes: new insights into arid and 

semiarid system dynamics. BioScience, 56, 491–501. 

Peters, D.P.C., R.A. Pielke, Sr., B.T. Bestelmeyer, C.D. Allen, Stuart Munson-McGee, 

and K.M. Havstad, 2004. Cross-scale interactions, nonlinearities, and forecasting 

catastrophic events. Proceedings of the National Academy of Sciences, 101(42), 

15130–15135. 

Peters, D.P.C., P.M Groffman, K.J. Nadelhoffer, N.B. Grimm, S.L. Collins, W.K. 

Michener, and M.A. Huston, 2008. Living in an increasingly connected world: a 

framework for continental-scale environmental science. Frontiers in Ecology and 

the Environment, 6(5), 229–237. 

Piatt, J.F., and A.M. Springer, 2003. Advection, pelagic food webs and the biogeography 

of seabirds in Beringia. Marine Ornithology, 31, 141–154. 

Piatt, J.F., S.A. Hatch, B.D. Roberts, W.W. Lidster, J.L. Wells, and J.C. Haney, 1988. 

Populations, Productivity, and Feeding Habits of Seabirds on St. Lawrence, 

Alaskat. U.S. Fish and Wildlife Service OCS Study MMS 88–0022, 235 pp. 

138 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Pielke, R.A., Sr., T.J. Lee, J.H. Copeland, J.L. Eastman, C.L. Ziegler, and C.A. Finley, 

1997. Use of USGS-provided data to improve weather and climate simulations. 

Journal of Applied Ecology, 7(1), 3–21. 

Pimm, S.L., 1984. The complexity and stability of ecosystems. Nature, 307, 321–326. 

Poff, N.L., J.D. Allan, M.B. Bain, J.R. Karr, K.L. Prestegaard, B.D. Richetr, R.E. Sparks, 

and J.C. Stromberg, 1997. The natural flow regime. BioScience, 47(11), 769–784. 

Poiani, K.A., and W.C. Johnson, 1991. Global warming and prairie wetlands. BioScience,

41(9), 611–618. 

Poiani, K.A., and W.C. Johnson, 1993. A spatial simulation model of hydrology and 

vegetation dynamics in semi-permanent prairie wetlands. Ecological 

Applications, 3(2), 279–293. 

Poiani, K.A., and W.C. Johnson, 1993. Potential effects of climate change on a semipermanent prairie wetland. Climatic Change, 24(3), 213–232. 

Poiani, K.A., W.C. Johnson, and T.G.F. Kittel, 1995. Sensitivity of a prairie wetland to 

increased temperature and seasonal precipitation changes. Journal of the 

American Water Resources Association, 31(2), 283–294. 

Poiani, K.A., W.C. Johnson, G.A. Swanson, and T.C. Winter, 1996. Climate change and 

northern prairie wetlands: simulations of long-term dynamics. Limnology and 

Oceanography, 41(5), 871–881. 

Prato, Tony, 2004. Alleviating multiple threats to protected areas with adaptive 

ecosystem management. The George Wright Forum, 20(4), 41–52. 

Prato, Tony and Dan Fagre, 2007. Achieving ecosystem sustainability. In: Sustaining 

Rocky Mountain Landscapes: Science, Policy and Management of the Crown of 

139 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

the Continent Ecosystem [Prato, Tony, and Dan Fagre (eds.)]. Resources for the 

Future Press, Washington, D.C., pp. 302–311. 

Pulwarty, R.S., K. Jacobs, and R. Dole, 2005. The hardest working river: drought and 

critical water problems on the Colorado. In: Drought and Water Crises: Science, 

Technology, and Management Issues [D.A. Wilhite (ed.)]. Taylor and Francis 

Press, pp. 249–285. 

Purves, Drew, and Stephen Pacala, 2008. Predictive models of forest dynamics. Science,

320, 1452–1453. 

Pyke, D.A., J.E. Herrick, Patrick Shaver, and Mike Pellant, 2002. Rangeland health 

attributes and indicators for qualitative assessment. Journal of Range 

Management, 55(6), 584–597. 

Radford, J.Q., A.F. Bennett, and G.J. Cheers, 2005. Landscape-level thresholds of habitat 

cover for woodland-dependent birds. Biological Conservation, 124(3), 317–337. 

Redman, C.L., and A.P. Kinzig, 2003. Resilience of past landscapes: resilience theory, 

society, and the longue durée. Conservation Ecology, 7(1), 14. 

Reynolds, J.F., and D.M. Stafford Smith, eds., 2002. Global desertification: Do humans 

cause deserts?. Dahlem University Press, Berlin, Workshop Report 88, 437 pp. 

Rich, P.M., D.D. Breshears, and A.B. White, 2008. Phenology of mixed woodyherbaceous ecosystems following extreme events: net and differential responses. 

Ecology, 89(2), 342–352. 

Richmond, Robert, et al., 2002. Status of the coral reefs in Micronesia and American 

Samoa: US affiliated and freely associated islands in the Pacific. In: Status of 

140 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Coral Reefs of the World: 2002 [Wilkinson, Clive (ed.)]. Australian Institute of 

Marine Science, Townsville, Queensland, Australia, pp. 217–236. 

Rietkerk, M., and J. van de Koppel, 1997. Alternate stable states and threshold effects in 

semi-arid grazing systems. Oikos, 79(1), 69–76. 

Rietkerk, Max, S.C. Dekker, P.C. de Ruiter, and Johan van de Koppel, 2004. Selforganized patchiness and catastrophic shifts in ecosystems. Science, 305(5692), 

1926–1929. 

Riordan, Brian, David Verbyla, and A.D. McGuire, 2006. Shrinking ponds in subarctic 

Alaska based on 1950–2002 remotely sensed images. Journal of Geophysical 

Research Biogeosciences, 111, G04002, doi:10.1029/2005JG000150. 

Roach, A.T., K. Aagaard, C.H. Pease, S.A. Salo, T. Weingartner, V. Pavlov, and M. 

Kulakov, 1995. Direct measurements of transport and water properties through 

the Bering Strait. Journal of Geophysical Research, 100(C9), 18443–18457. 

Rodó, Xavier, and F.A. Comín, 2002. Global Climate: Current Research and 

Uncertainties in the Climate System. Springer, New York, 286 pp. 

Rosenzweig, Cynthia, and Daniel Hillel, 1993. The dust bowl of the 1930’s: analog of 

greenhouse effect in the Great Plains? Journal of Environmental Quality, 22, 9–

22. 

Rosenzweig, M.L., 1971. Paradox of enrichment: destabilization of exploitation 

ecosystems in ecological time. Science, 171(3969), 385–387. 

Rowan, Rob, 2004. Coral bleaching: thermal adaptation in reef coral symbionts. Nature, 

430, 742. 

141 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Rowan, Rob, and Nancy Knowlton, 1995. Intraspecific diversity and ecological zonation 

in coral-algal symbiosis. Proceedings of the National Academy of Science, 92, 

2850–2853. 

Roy, A.H., A.D. Rosemond, M.J. Paul, D.S. Leigh, and J.B. Wallace, 2003. Stream 

macroinvertebrate response to catchment urbanization (Georgia, U.S.A.). 

Freshwater Biology, 48(2), 329–346. 

Russell, R.W., N.M. Harrison, and G.L. Hunt, Jr., 1999. Foraging at a front: 

hydrography, zooplankton, and avian planktivory in the northern Bering Sea. 

Marine Ecology Progress Series, 182, 77–93. 

Sabine, S.L., R.A. Feely, Nicolas Gruber, R.M. Key, Kitack Lee, J.L. Bullister, Rick 

Wanninkhof, C.S. Wong, D.W.R. Wallace, Bronte Tilbrook, F.J. Millero, T.-H. 

Peng, Alexander Kozyr, Tsueno Ono, and A.F. Rios, 2004. The oceanic sink for 

anthropogenic CO2. Science, 305(5682), 367–371. 

Sala, Enric, and George Sugihara, 2005. Food-web theory provides guidelines for marine 

ecosystems. In: Aquatic Food Webs: An Ecosystem Approach [Belgrano, Andrea, 

U.M. Scharler, Jennifer Dunne, and R.E. Ulanowicz (eds.)]. Oxford University 

Press, pp. 170–183. 

Sala, Enric, and M.H. Graham, 2002. Community-wide distribution of predator-prey 

interaction strength in kelp forests. Proceedings of the National Academy of 

Science, 99(6), 3678–3683. 

Savage M., and J.N. Mast, 2005. How resilient are southwestern ponderosa pine forests 

after crown fires? Canadian Journal of Forest Research, 35, 967–977. 

142 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Scheffer, Marten, and S.R. Carpenter, 2003. Catastrophic regime shifts in ecosystems: 

Linking theory to observation. Trends in Ecological Evolution, 18(12), 648–656. 

Scheffer, Marten, Steve Carpenter, J.A. Foley, Carl Folke, and Brian Walker, 2001. 

Catastrophic shifts in ecosystems. Nature, 413, 591–596. 

Schlesinger, W.H., J.F. Reynolds, G.L. Cunningham, L.F. Huenneke, W.M. Jarrell, R.A. 

Virginia, and W.G. Whitford, 1990. Biological feedbacks in global 

desertification. Science, 247(4946), 1043–1048. 

Schmitz, O.J., Eric Post, C.E. Burns, and K.M. Johnston, 2003. Ecosystem responses to 

global climate change: moving beyond color mapping. BioScience, 53(12), 1199–

1205. 

Schneider, S.H., 2004. Abrupt non-linear climate change, irreversibility and surprise. 

Global Environmental Change Part A, 14(3), 245–258. 

Scholze, Marco, Wolfgang Knorr, N.W. Arnell, and I.C. Prentice, 2006. A climatechange risk analysis for world ecosystems. Proceedings of the National Academy 

of Sciences, 103, 13116–13120. 

Scott, T.A., 2000. The selection and design of multiple-species habitat preserves.

Environmental Management, 26, 37–53. 

Scott, D.J., and R. Suffling, 2000. Climate change and Canada’s national parks. 

Environment Canada, Toronto, ON. 

Scott, Daniel, and Christopher Lemieux, 2005. Climate change and protected area policy 

and planning in Canada. The Forestry Chronicle, 81(5), 696–703. 

Shapley, M.D., W.C. Johnson, D.R. Engstrom, and W.R. Osterkamp, 2005. Lateholocene flooding and drought in the northern Great Plains, USA, reconstructed 

143 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

from tree rings, lake sediments, and ancient shorelines. The Holocene, 5(1), 29–

41. 

Skaggs, Richard H., 1975. Drought in the United States, 1931–40. Annals of the

Association of American Geographer, 65(3), 391–402. 

Smith, L.C., Y. Sheng, G.M. MacDonald, and L.D. Hinzman, 2005. Disappearing arctic 

lakes. Science, 308(5727), 1429. 

Smith, L.W., and Charles Birkeland, 2007. Effects of intermittent flow and irradiance 

level on back reef Porites corals at elevated seawater temperatures. Journal of 

Experimental Marine Biology and Ecology, 341(2), 282–294. 

Smithwick, E.A.H, M.G. Turner, M.C. Mack, and F.S. Chapin III, 2005. Postfire soil N 

cycling in northern conifer forests affected by severe, stand-replacing wildfires. 

Ecosystems, 8, 163–181. 

Soule, P.T., and P.A. Knapp, 1999. Western juniper expansion on adjacent disturbed and 

near-relict sites. Journal of Range Management, 52, 525–533. 

Springer, A.M., C.P. McRoy, and K.R. Turco, 1989. The paradox of pelagic food webs in 

the northern Bering Sea—II. Zooplankton communities. Continental Shelf 

Research, 9, 359–386. 

Stabeno, P.J., and J.E. Overland, 2001. Bering Sea shift towards an earlier spring 

transition. Eos, Transactions, AGU, 82, 317–321. 

Stabeno, P.J., G.L. Hunt, Jr., J.M. Napp, and J.D. Schumacher, 2006. Physical forcing of 

ecosystem dynamics on the Bering Sea shelf. In: The Sea [Robinson, A.R., and 

K.H. Brink (eds.)]. The Global Coastal Ocean: Interdisciplinary Regional Studies 

144 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

and Syntheses, Part B. Harvard University Press, John Wiley and Sons, New 

York, v. 14, pp. 1177–1212. 

Stabeno, P.J., N.A. Bond, and S.A. Salo, 2007. On the recent warming of the 

southeastern Bering Sea shelf. Deep Sea Research II, 54(23–26), 2599–2618. 

Stabeno, P.J., N.A., Bond, N.B. Kachel, S.A. Salo, and J.D. Schumacher, 2001. On the 

temporal variability of the physical environment over the south-eastern Bering 

Sea. Fisheries Oceanography, 10(1), 81–98. 

Steele, J.H., 1996. Regime shifts in fisheries management. Fisheries Research, 25(1), 19–

23. 

Steele, J.H., and E.W. Henderson, 1984. Modeling long-term fluctuations in fish stocks. 

Science, 224(4652), 985–987. 

Stephenson, N.L., and P.J. van Mantgem, 2005. Forest turnover rates follow global and 

regional patterns of productivity. Ecology Letters, 8, 524–531. 

Stewart, I.T., D.R. Cayan, and M.D. Dettinger, 2004. Changes in snowmelt runoff timing 

in western North America under a `business as usual' climate change scenario. 

Climatic Change, 62(1–3), 217–232. 

Stoddard, J.L., 1994. Long-term changes in watershed retention of N. In: Chemistry of 

Lakes and Reservoirs [Baker L.A. (ed.)]. Advances in Chemistry Society Series 

No. 237, Washington, D.C., pp. 223–284. 

Stoddard, J.L., D.S. Jeffries, A. Lükewille, T.A. Clair, P.J. Dillon, C.T. Driscoll, M. 

Frosius, M. Johannessen, J.S. Kahl, J.H. Kellogg, A. Kemp, J. Mannio, D.T. 

Monteith, P.S. Murdoch, S. Patrick, A. Rebsdorf, B.L. Skjelvåle, M.P. Stainton, 

T. Traaen, H. van Dam, K.E. Webster, J. Wieting, and A. Wilander, 1999. 

145 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Regional trends in aquatic recovery from acidification in North America and 

Europe. Nature, 401, 575–578. 

Stoffels, R.J., K.R. Clarke, and G.P. Closs, 2005. Spatial scale and benthic community 

organization in the littoral zones of large oligotrophic lakes: potential for crossscale interactions. Freshwater Biology, 50(7), 1131–1145. 

Stone, R.S., E.G. Dutton, J.M. Harris, and D. Longnecker, 2002. Earlier spring snowmelt 

in northern Alaska as an indicator of climate change. Geophysical Research, 107, 

4089. 

Stringham, T.K., W.C. Krueger, and P.L. Shaver, 2003. State and transition modeling: 

An ecological process approach. Journal of Range Management, 56, 106–113. 

Stroeve, J., M.C. Serreze, F. Fetterer, T. Arbetter, W. Meier, J. Maslanik, and K. 

Knowles, 2005. Tracking the Arctic’s shrinking ice cover; another extreme 

September sea-ice minimum in 2004. Geophysical Research Letters, 32(L04501), 

3, 5, doi:10.1029/2004GL021810. 

Stromberg, J.C., K.J. Bagstad, J.M. Leenhouts, S.J. Lite, and Elizabeth Makings, 2005. 

Effects of stream flow intermittency on riparian vegetation of a semiarid region 

river (San Pedro River, Arizona). River Research and Applications, 21(8), 925–

938. 

Stromberg, J.C., V.B. Beauchamp, M.D. Dixon, S.J. Lite, and C. Paradzick, 2007. 

Importance of low-flow and high-flow characteristics to restoration of riparian 

vegetation along rivers in arid south-western United States. Freshwater Biology, 

52(4), 651–679. 

146 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Sturm, Matthew., Charles Racine, and Kenneth Tape, 2001. Increasing shrub abundance 

in the Arctic. Nature, 411, 546–547. 

Suarez, M.L., Luciana Ghermandi, and Thomas Kitzberger, 2004. Factors predisposing 

episodic drought-induced tree mortality in Nothofagus—site, climatic sensitivity 

and growth trends. Journal of Ecology, 92(6), 954–966. 

Suding, K.N., K.L. Gross, and G.R. Houseman, 2004. Alternative states and positive 

feedbacks in restoration ecology. Trends in Ecology and Evolution, 19(1), 46–53. 

Sugihara, George, 1984. Graph theory, homology, and food webs. Proceedings of 

Symposia in Applied Mathematics, 30, 83–89. 

Sugihara, George, 1994. Nonlinear forecasting for the classification of natural time 

series. Philosophical Transactions: Physical Sciences and Engineering,

348(1688), 477–495. 

Sugihara, George, and R.M. May, 1990. Nonlinear forecasting as a way of distinguishing 

chaos from measurement error in time series. Nature, 344, 737–741. 

Sutherland, J.P., 1974. Multiple stable points in natural communities. American 

Naturalist, 108, 859. pp. 

Swank, W.T., and J.M. Vose, 1997. Long-term nitrogen dynamics of Coweeta forested 

watersheds in the southeastern United States of America. Global Biogeochemical 

Cycles, 11(4), 657–671. 

Swetnam, T.W., and J.L. Betancourt, 1998. Mesoscale disturbance and ecological 

response to decadal climatic variability in the American Southwest. Journal of 

Climate, 11, 3128–3147. 

147 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Tallmon, D.A., E.S. Jules, N.J. Radke, and L.S. Mills, 2003. Of mice and men and 

trillium: cascading effects of forest fragmentation. Ecological Applications, 13(5), 

1193–1203. 

Tape Ken, Matthew Sturm, and Charles Racine, 2006. The evidence for shrub expansion 

in Northern Alaska and the Pan-Arctic. Global Change Biology, 12, 686–702. 

Thom, Rene, 1975. Structural Theory and Morphogenesis: An Outline of a General 

Theory of Models. W.A. Benjamin, Reading, MA, 348 pp. 

Thom, Rene, 1971. Modern mathematics’: an educational and philosophical error? 

American Scientist, 59(6), 695–699. 

Thuiller, Wilfried, Sandra Lavorel, M.B. Araujo, M.T. Sykes, and I.C. Prentice, 2005. 

Climate change threats to plant diversity in Europe. Proceedings of the National 

Academy of Sciences, 102, 8245–8250. 

Tilman, David, and David Wedin, 1991. Oscillations and chaos in the dynamics of a 

perennial grass. Nature, 353, 653–655. 

Turner, B.L., II, R.E. Kasperson, P.A. Matson, J.J. McCarthy, R.W. Corell, Lindsey 

Christenses, Noelle Eckley, J.X. Kasperson, Amy Luers, M.L. Martello, Colin 

Polsky, Alexander Pulsipher, and Andrew Schiller, 2003. A framework for 

vulnerability analysis in sustainable science. Proceedings of the National 

Academy of Sciences, 100(14), 8074–8079. 

U.S. Forest Service, 2006. Forest insect and disease conditions in the southwestern 

region, 2005. U.S. Forest Service, publication PR–R3–16–1, 34 pp. 

Urban, D.L., 2005. Modeling ecological processes across scales. Ecology, 86(8), 1996–

2006. 

148 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

van der Valk, A.G. (ed.), 1989. Northern Prairie Wetlands. Iowa State University Press, 

Ames, IA, 400 pp. 

van Mantgem, P.J., N.L. Stephenson, J.C. Byrne, L.D. Daniels, J.F. Franklin, P.Z. Fulé, 

M.E. Harmon, J.M. Smith, A.H. Taylor, and T.T. Veblen, [in review]. 

Widespread increase of tree mortality rates in the western United States. Science. 

van Mantgem, P.J., and N.L. Stephenson, 2007. Apparent climatically-induced increase 

of tree mortality rates in a temperate forest. Ecology Letters, 10, 909–916. 

Van Nes, E.H., and M. Scheffer, 2007. Slow recovery from perturbations as a generic 

indicator of a nearby catastrophic shift. The American Naturalist, 169(6), 738. 

Veenhuis, J., 2002. Effects of wildfire on the hydrology of Capulin and Rito de los 

Frijoles Canyons, Bandelier National Monument, New Mexico. U.S. Geological 

Survey Water Resources Investigations Report 02–4152, 39 pp. 

Vitousek, P.M., 1994. Beyond global warming: ecology and global change. Ecology, 

75(7), 1861–1876. 

Voldseth, R.A., W.C. Johnson, T. Gilmanov, G.R. Guntenspergen, and B. Millett, 2007. 

Model estimation of land-use effects on water levels of northern prairie wetlands. 

Ecological Applications, 17(2), 527–540. 

Vorosmarty, C.J., Pamela Green, Joseph Salisbury, and R.B. Lammers, 2000. Global 

water resources: vulnerability from climate change and population growth. 

Science, 289(5477), 284–288. 

Walker, Brian, and J.A. Meyers, 2004. Thresholds in ecological and social-ecological 

systems: a developing database. Ecology and Society, 9(2), 3. 

149 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Walker, Brian, C.S. Holling, S.R. Carpenter, and Ann Kinzig, 2004. Resilience, 

adaptability and transformability in social-ecological systems. Ecology and 

Society, 9(2), 5. 

Walker, Brian, Stephen Carpenter, John Anderies, Nick Abel, G.S. Cumming, Marco 

Janssen, Louis Lebel, Jon Norberg, G.D. Peterson, and Rusty Pritchard, 2002. 

Resilience management in social-ecological systems: a working hypothesis for a 

participatory approach. Conservation Ecology, 6(1), 14. 

Walker, M.D., C.H. Wahren, R.D. Hollister, G.H.R. Henry, L.E. Ahlquist, J.M. Alatalo, 

M.S. Bret-Harte, M.P. Calef, T.V. Callaghan, A.B. Carroll, H.E. Epstein, I.S. 

Jónsdóttir, J.A. Klein, Borgþór Magnússon, Ulf Molau, S.F. Oberbauer, S.P. 

Rewa, C.H. Robinson, G.R. Shaver, K.N. Suding, C.C. Thompson, Anne 

Tolvanen, Ørjan Totland, P.L. Turner, C.E. Tweedie, P.J. Webber, and P.A. 

Wookey, 2006. Plant community responses to experimental warming across the 

tundra biome. Proceedings of the National Acamemy of Sciences, 103(5), 1342–

1346. 

Walters, Carl, 1986. Adaptive Management of Renewable Resources. Macmillan and Co., 

New York, 374 pp. 

Walters, Carl, 1996. Adaptive Management of Renewable Resources. Macmillan and Co., 

New York, 374 pp. 

Wamelink, G.W.W., ter Braak, C.J.F., and van Dobben, H.F, 2003. Changes in largescale patterns of plant biodiversity predicted from environmental economic 

scenarios. Landscape Ecology, 18, 513–527. 

150 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Watson, R.T., et al., 1996. Summary for policymakers. In: Climate Change 1995: 

Impacts, Adaptations, and Mitigation of Climate Change: Scientific-Technical 

Analyses: Contribution of Working Group II to the Second Assessment Report of 

the Intergovernmental Panel on Climate Change [Watson, R.T., M.C. Zinyowera, 

and R.H. Moss (eds.)]. Cambridge University Press, Cambridge, pp. 1–18. 

Welch, David, 2005. What should protected areas managers do in the face of climate 

change? George Wright Forum, 22(1), 75–93. 

Weller, M.W., 1965. Chronology of pair formation in some nearctic Aythya (Anatidae). 

The Auk, 82(2), 227–235. 

Westerling, A.L., H.G. Hidalgo, D.R. Cayan, and T.W. Swetnam, 2006. Warming and 

earlier spring increase western U.S. forest wildfire activity. Science, 313, 940–

943. 

Wiens, J.A., 1989. Spatial scaling in ecology. Functional Ecology, 3(4), 385–397. 

Wilcox, B.P., D.D. Breshears, and C.D. Allen, 2003. Ecohydrology of a resourceconserving semiarid woodland: temporal and spatial scaling and disturbance. 

Ecological Monographs, 73(2), 223–239. 

Wilhere, G.F., 2002. Adaptive management in habitat conservation plans. Conservation 

Biology, 16(1), 20–29. 

Winter, T.C., and M.K. Woo, 1990. Hydrology of lakes and wetlands. In: Surface Water 

Hydrology [Wolman, M.G., and H.D. Riggs (eds.)]. Geological Society of 

America, Boulder, Colo., v. O–1, pp. 159–187. 

151 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Winter, T.C., 2000. The vulnerability of wetlands to climate change: a hydrologic 

landscape perspective. Journal of the American Water Resources Assoiation,

36(2), 305–311. 

Winter, T.C., and D.O. Rosenberry, 1998. Hydrology of prairie pothole wetlands during 

drought and deluge: a 17-year study of the cottonwood lake wetland complex in 

North Dakota in the perspective of longer term measured and proxy hydrological 

records. Climatic Change, 40(2), 189–209. 

Wissel, C., 1984. A universal law of the characteristic return time near thresholds. 

Oecologia, 65(1), 101–107. 

Woodhouse, C.A., and J.T. Overpeck, 1998. 200 years of drought variability in the 

central United States. Bulletin of the American Meteorological Society, 79, 2693–

2714. 

Worm, Boris, and R.A. Meyers, 2003. Meta-analysis of cod-shrimp interactions reveals 

top-down control in oceanic food webs. Ecology, 84, 162–173. 

Xenopoulos, M.A., D.M. Lodge, Joseph Alcamo, Michael Märker, Kerstin Schulze, and 

D.P. Van Vuuren, 2005. Scenarios of freshwater fish extinctions from climate 

change and water withdrawal. Global Change Biology, 11, 1557–1564. 

Yao, Jin, D.P.C. Peters, K.M. Havstad, R.P. Gibbens, and J.E. Herrick, 2006. Multi-scale 

factors and long-term responses of Chihuahuan desert grasses to drought. 

Landscape Ecology, 21(8), 1217–1231. 

Yoshikawa, Kenji, and L.D. Hinzman, 2003. Shrinking thermokarst ponds and 

groundwater dynamics in discontinuous permafrost near Council, Alaska. 

Permafrost and Perglacial Processes, 14(2), 151–160. 

152 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Zeller, Dirk, Shawn Booth, Peter Craig, and Daniel Pauly, 2006a. Reconstruction of coral 

reef fisheries catches in American Samoa, 1950–2002. Coral Reefs, 25, 144–152. 

Zeller, Dirk, Shawn Booth, and Daniel Pauly, 2006b. Fisheries contributions to the gross 

domestic product: Underestimating small-scale fisheries in the Pacific. Marine 

Resource Economics, 21(4), 355–374. 

Zou, C.B., G.A. Barron-Gafford, and D.D. Breshears, 2007. Effects of topography and 

woody plant canopy cover on near-ground solar radiation: relevant energy inputs 

for ecohydrology and hydropedology. Geophysical Research Letters, 34, L24S21, 

doi:10.1029/2007GL031484. 

153 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Appendix A—Glossary 

adaptive capacity 

the capacity of organisms, both individuals and groups, to respond to and change in the 

state of the system (Folke et al., 2003; Walker et al., 2004; Adger et al., 2005); depends 

on initial diversity and the capacity of component organisms to adjust and change 

bioerosion 

describes the erosion of hard ocean substrates by living organisms by physical 

mechanisms such as boring, drilling, rasping, and scraping or by chemical mechanisms 

for dissolution. 

degradation 

deterioration of a system to a less desirable state as a result of failure to actively adapt or 

transform 

degree heating week (DHW) 

the NOAA satellite-derived Degree Heating Week (DHW) is an experimental product 

designed to indicate the accumulated thermal stress that coral reefs experience. A DHW 

is equivalent to one week of sea surface temperature 1 deg C above the expected 

summertime maximum. For example, 2 DHWs indicate one week of 2 deg C above the 

expected summertime maximum 

ecosystem

all the organisms, including people, in an area and the nonbiological materials, such as 

water and soil minerals, with which they interact 

ecosystem services

154 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

benefits that people derive from ecosystems, including supporting, provisioning, 

regulating, and cultural services 

exogenous factor

factor external to the system being managed and which therefore is not incorporated into 

the management framework 

exposure

nature and degree to which the system experiences environmental or sociopolitical stress 

mitigation 

reduction in the exposure of a system to a stress or hazard 

negative feedbacks 

interaction in which the effects of two interacting components on one another have 

opposite signs; generally buffer against changes in the system; an important mechanism 

enhancing resilience 

positive feedback 

interaction in which the effects of two interacting components on one another have the 

same sign (both positive or both negative); tend to amplify changes in the system, leading 

to threshold changes in the system 

regime shift 

sudden shifts in biota that are driven by ocean climate events

resilience

capacity of a socioecological system to absorb a spectrum of shocks or perturbations and 

continue to develop with similar fundamental function, structure, identity, and feedbacks, 

that is, to remain within a given stability domain (Holling, 1973; Gunderson and Holling. 

155 
SAP 4.2: Thresholds of Climate Change in Ecosystems 

156 

2002; Walker et al., 2004; Folke, 2006a); includes adaptive capacity but also depends on 

legacies (for example, seed banks) and strong negative feedbacks that might balance 

positive feedbacks that might destabilize the system 

socioecological system 

system in which human activities depend on resources and services provided by 

ecosystems and ecosystem organization is influenced, to varying degrees, by human 

activities 

steady state 

condition of a system in which there is no net change in system structure or functioning 

over the time scale of study 

sustainability 

use of the environment and resources to meet the needs of the present without 

compromising the ability of future generations to meet their own needs 

threshold

As defined in this assessment, an ecological threshold is the point at which there is an 

abrupt change in an ecosystem quality, property, or phenomenon, or where small changes 

in one or more external conditions produce large and persistent responses in an 

ecosystem. 

vulnerability

the degree to which a system is likely to experience harm due to exposure to a specified 

hazard or stress (Turner et al., 2003; Adger, 2006)
SAP 4.2: Thresholds of Climate Change in Ecosystems 

Contact Information 

Global Change Research Information Office 

c/o Climate Change Science Program Office 

1717 Pennsylvania Avenue, NW 

Suite 250 

Washington, DC 20006 

202-223-6262 (voice) 

202-223-3065 (fax) 

The Climate Change Science Program incorporates 

the U.S. Global Change Research Program and the 

Climate Change Research Initiative. 

To obtain a copy of this document, place an order at 

the Global Change Research Information Office 

(GCRIO) web site http://www.gcrio.org/orders

Climate Change Science Program and the Subcommittee on 

Global Change Research 

William J. Brennan, Chair 

National Oceanic and Atmospheric Administration 

Director, Climate Change Science Program 

Jack Kaye, Vice Chair 

National Aeronautics and Space Administration 

Allen Dearry 

Department of Health and Human Services 

Anna Palmisano 

Department of Energy 

Mary Glackin 

National Oceanic and Atmospheric Administration 

Patricia Gruber 

Department of Defense 

William Hohenstein 

Department of Agriculture 

Linda Lawson 

Department of Transportation 

Mark Myers 

U.S. Geological Survey 

Tim Killeen 

National Science Foundation 

Patrick Neale 

Smithsonian Institution 

Jacqueline Schafer 

U.S. Agency for International Development 

Joel Scheraga 

Environmental Protection Agency 

Harlan Watson 

Department of State 

EXECUTIVE OFFICE AND 

OTHER LIAISONS 

Robert Marlay 

Climate Change Technology Program 

Katharine Gebbie 

National Institute of Standards & Technology 

Stuart Levenbach 

Office of Management and Budget 

Margaret McCalla 

Office of the Federal Coordinator for Meteorology 

Robert Rainey 

Council on Environmental Quality 

Daniel Walker 

Office of Science and Technology Policy 

157