United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical Report 

RMRS-GTR-309

September 2013

Review and Recommendations 

for Climate Change Vulnerability 

Assessment Approaches With 

Examples From the Southwest

Megan M. Friggens, Karen E. Bagne, Deborah M. Finch,

Donald Falk, Jack Triepke, Ann Lynch
Friggens, M.; Bagne, K.; Finch, D.; Falk, D.; Triepke, J.; Lynch, A. 2013. Review and 

recommendations for climate change vulnerability assessment approaches with 

examples from the Southwest. Gen. Tech. Rep. RMRS-GTR-309. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

106 p.

Abstract

Climate change creates new challenges for resource managers and decision-makers 

with broad and often complex effects that make it difficult to accurately predict and 

design management actions to minimize undesirable impacts. We review pertinent 

information regarding methods and approaches used to conduct climate change 

vulnerability assessments to reveal assumptions and appropriate application of results. 

Secondly, we provide managers with an updated summary of knowledge regarding 

vulnerability of species and habitats to climate change in the American Southwest. 

Overall, vulnerability assessments provided valuable information on climate change 

effects and possible management actions but were far from a comprehensive picture 

for the future of the Southwest. Scales, targets, and assessment approaches varied 

widely and focused on only a subset of resources. We recommend land managers 

critically examine methods when using assessment results; select scale, methods, and 

targets carefully when planning new assessments; and communicate assessment needs 

to researchers of climate change response.

Keywords: Resource management, wildlife, wildlands, conservation, global change

Authors

Megan Friggens is a Research Ecologist with the U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station.

Karen Bagne is an Ecologist and Wildlife Biologist with the USDA Forest Service, 

Rocky Mountain Research Station.

Deborah Finch is the Program Manager for the Grassland, Shrubland, and Desert 

program of the USDA Forest Service, Rocky Mountain Research Station.

Donald Falk is Associate Professor in the University of Arizona School of Natural 

Resources and the Environment, with joint appointments in the Laboratory of Tree-Ring 

Research and Institute of the Environment.

Jack Triepke is a Regional Ecologist for the USDA Forest Service Southwestern Region 

in Albuquerque, New Mexico. Jack is also a Ph.D. candidate at the University of New 

Mexico where he is studying the effects of climate change on Southwest ecosystems.

Ann Lynch is a Research Entomologist with the Rocky Mountain Research Station, 

stationed in Tucson, AZ.

Acknowledgments

Funding for this project was provided by the Western Wildlands Environmental Threats 

Assessment Center. We thank Aimee Roberson and Cathryn Dowd whose comments 

improved this manuscript.

Cover photo: Sevilleta National Wildlife Refuge LTER Grassland (photo by Megan 

Friggens); hurricane Ileana approaches the Baja Peninsula August 24, 2006 (NASAGoddard Space Flight Center, data from NOAA GOES); Waldo Canyon burn scar 

(brown) outside of Colorado Springs, Colorado, 2012 (NASA).
Executive Summary

The intent of this report is, first, to review pertinent information regarding methods 

and approaches used to conduct climate change vulnerability assessments and, 

second, to provide managers with an updated summary of knowledge regarding 

vulnerability to climate change of species and habitats in the American Southwest. We 

begin by defining and discussing climate change vulnerability assessment concepts 

and approaches and then examine major methods by which such assessments 

are conducted. Finally, we review and synthesize the information presented in 

vulnerability assessments conducted for the southwestern United States.

Climate change creates new challenges for resource managers and decision-makers, 

with broad and often complex effects that make it difficult to design management 

actions that can minimize undesirable impacts. A wide range of approaches have 

been used to predict how and why resources will change in response to climate 

change. Assessments can be useful in addressing climate change issues because they 

synthesize large amounts of complex information. Vulnerability assessments seek to 

synthesize available information on climate change for a region, sector, or resource 

of interest and glean recommendations for management actions. Vulnerability is a 

combination of exposure, sensitivity, and adaptive capacity, components that are not 

always distinct nor are they integrated into every vulnerability assessment (USGCRP 

2011). By specifically examining vulnerability to climate change, managers can 

rank needs as well as pinpoint the causes of vulnerability for valued resources. Most 

vulnerability assessments fit one of four broad categories: mathematical models, 

conceptual frameworks, indices, and syntheses.

A number of important elements must be considered when preparing to conduct 

a vulnerability assessment, including selecting targets, scales, and the measure of 

vulnerability. Vulnerability assessments that address questions that managers and 

stakeholders can act upon are more applicable than assessments that lack immediate 

relevance to on-the-ground actions. Depending upon the objective, identified 

assessment targets may include individuals, populations, species, landscapes, and 

ecosystem processes. Selection of appropriate biological, spatial, and temporal scales 

influences the relevance of biological analysis in any assessment of climate change 

vulnerability. Assessments also need to integrate considerations of critical thresholds 

and important ecosystem tipping points. Modeling and empirical studies suggest that 

changes due to climate alone are likely to be expressed at multi-annual to decadal 

time scales (Allen and Breshears 1998). In contrast, severe large-scale disturbances 

can reorganize mass and energy of ecosystems on much shorter time scales of days 

to months (Overpeck and others 1990). In some scenarios, combinations of gradual 

climate change and disturbance events will trigger abrupt ecosystem transitions into 

novel configurations, rather than either factor acting alone.

Methods for measuring climate change response are organized into four groups: 

hydrologic models, adaptive capacity models, distribution models, and indices. 

Hydrologic models are a critical component of assessments based within aquatic 

systems and often inform other modeling approaches aimed at species or biomes. 

Hydrologic models encompass a variety of mostly statistical approaches that estimate 

the impact of climate change on hydrological function and aquatic systems and can 

incorporate both ecological and economic aspects of system vulnerability. Various 

approaches, grouped together in this report in the Adaptive Capacity Models section, 

identify individual characteristics that may influence the capacity of species to survive 

changes to climate. Although diverse, adaptive capacity models include estimates 

of species survival (or, alternatively, risk of extinction) as well as mechanisms of 

local adaptation (for example, latent genetic variation that may confer resistance 

to emerging climate conditions). Distribution models, the most commonly used 

method to predict response, encompass a variety of statistical, simulation, and 

other approaches to estimate future effects of climate on the range distributions of 

species, communities, and biomes. These models may generate predictions of future 

habitat suitability for species or communities, habitat characteristics, or patterns 

of biodiversity. Finally, a variety of methods aim to prioritize the identification and 

management of species or habitats. Our discussion focuses on a subset of these 

i
methods, termed indices or index-based measures that identify relative vulnerability 

within a group of targeted study subjects through a quantitative comparison of traits 

or issues. Index-based measures have emerged within the literature only recently in 

response to limitations in directly measuring climate change response.

A vulnerability assessment examines the expected response of resources, how 

the resource becomes more or less in need of management, especially in relation 

to other resources or a critical level, and identifies management intervention 

points. A vulnerability assessment is a specific type of product that identifies how 

current management strategies may change under changing climate by combining 

information on the expected response of individual resources with information on 

how such impacts change relative management needs among multiple resources. 

Thus, vulnerability is often measured in relation to a reference level that is relevant 

to contemporary human societies. The specific measure of vulnerability used in an 

assessment is important to understanding the conclusions and possible limitations of 

the assessment. Six common measures of vulnerability to climate change are used in 

natural resource management:

(1) Direct observations of response to recent versus historic conditions.

(2) Departure of modeled future of climate, species distributions, biodiversity, and 

other measures from baseline observations.

(3) Observations from experiments or of past events equivalent to projected future 

conditions.

(4) Proximity to thresholds or probability of exceeding a critical threshold.

(5) Estimates of adaptive capacity to future conditions.

(6) Relative importance of modeled factors affecting response.

Projections for the United States indicate that the Southwest is likely to experience 

more extreme climate changes than other regions. These changes exacerbate existing 

problems associated with sustaining valued natural resources. For example, longer 

duration droughts and higher temperatures will tax the already scarce water sources 

critical to plants, fish, and wildlife as well as to the rapidly expanding human 

population that occupies the Southwest. For these reasons, we focus our review of 

climate change vulnerability assessments on those applicable to natural resource 

management in the Southwest. Several common themes are found among these 

assessments:

• Vulnerability of grasslands to invasive species is likely to increase under climate 

change (reviews by Morgan and others 2010; Chambers and Pellant 2008; Neilson 

and others 2010)

• Hydrological systems will be taxed by multiple impacts, including:

ο higher water temperatures with multiple effects for temperature dependent 

species (Johnson and others 2005; Eaton and Scheller 1996).

ο changes in precipitation events leading to less snowpack, changes to the timing 

of flood regimes, less flow, and reduced water tables (Theobald and others 

2009).

ο spread of invasive species in both aquatic and riparian habitats (Theobald and 

others 2009; Rood and Conrad 2008).

• Many studies show that temperature alone drives or is sufficient to lead to observed 

or predicted changes (see Williams and others 2010 for tree growth; Currie 2001 for 

biodiversity; Garfin and Lenart 2007 for loss of cool water fish species; Notaro and 

others 2012; Williams and others 2012 trees; Eaton and Scheller 1996 and Meyers 

and others 1999 describe many effects on aquatic systems,) but note exception to 

temperature-driven patterns for cheatgrass invasion (see Bradley 2009).

• Areas near ecotones are more vulnerable to change (e.g., Madrean habitats see 

Kupfer and others 2005).

Overall, vulnerability assessments reviewed in this synthesis provided valuable 

information on climate change effects and possible management actions, but were 

unable to provide a comprehensive picture for the future of the Southwest. Scales, 

ii
targets, and assessment approaches varied widely and focused on only a subset of 

resources. Assessments of animal species typically involved special status species 

and were primarily assessed using index-based approaches though reports from 

these assessments provide detailed information on sources and causes of species 

vulnerability. Community level assessments varied more and included statistical 

modeling methods. Plant assessments tended to cover a wider geographic range 

than those focused on animals. Distribution models were used more widely for plant 

species and functional groups but were biased towards forested ecosystems. From 

many of these assessments, researchers are able to provide spatially explicit estimates 

of future climate and habitat suitability for animal and plant species. The applicability 

of vulnerability assessments for the Southwest is limited by the degree to which they 

include accurate measures of future change. Measuring future vulnerability was 

difficult where projected climate change impacts in the region exceed historical 

events, emphasizing the need for process-based models and the inclusion of critical 

thresholds. In addition, there were few assessments that integrate multiple complex 

interactions and threats such as those posed by disease, pests, invasive species, and 

hydrological changes. These gaps are likely mirrored for assessments in other regions. 

We recommend land managers critically examine methods when using assessment 

results; select scale, methods, and targets carefully when planning new assessments; 

and communicate assessment needs to researchers of climate change response.

iii
Contents

Executive Summary.............................................................................................................i

Chapter 1. Vulnerability Assessments in Natural Resource 

Management: Overview and Application .......................................................................1

1.1 The Climate Change Challenge ..............................................................................................1

1.2 Climate Change Vulnerability Assessments.............................................................................1

1.3 Vulnerability Assessment Approaches.....................................................................................3

1.4 Steps for Conducting a Vulnerability Assessment ....................................................................3

1.5 Vulnerability Assessments in Natural Resource Management..................................................4

Literature Cited ............................................................................................................................7

Chapter 2. Targets, Scope, and Scale..................................................................................8

2.1 Assessment Targets .................................................................................................................8

2.2 Scope.....................................................................................................................................9

2.3 Biological Scale ...................................................................................................................11

2.3.1 Species Scale ................................................................................................................................ 11

2.3.2 Ecosystem Scale............................................................................................................................ 12

2.4 Case Study: Landscape Analysis ...........................................................................................12

2.4.1 Ecosystem Attributes ..................................................................................................................... 13

2.4.2 Cross-Scale Applications............................................................................................................... 14

Literature Cited ..........................................................................................................................14

Chapter 3. Predicting Response to Climate Change .........................................................18

3.1 Introduction .........................................................................................................................18

3.2 General Methods .................................................................................................................19

3.2.1 Hydrological Models .................................................................................................................... 19

3.2.2 Adaptive Capacity Models ............................................................................................................ 23

3.2.3 Distribution Models...................................................................................................................... 26

3.2.4 Prioritization Methods................................................................................................................... 30

3.3 Issues in Predicting Response ...............................................................................................34

3.3.1 Critical Thresholds, Ecosystem Tipping Points, and Other Considerations...................................... 34

3.3.2 Interactive Effects.......................................................................................................................... 36

3.3.3 Uncertainty................................................................................................................................... 37

Literature Cited ..........................................................................................................................38

Chapter 4. Translating Response into Vulnerability and Management 

Recommendations........................................................................................................43

4.1 Measures of Vulnerability .....................................................................................................43

4.1.1 Degree of Recent Change From Historic ....................................................................................... 43

4.1.2 Degree of Modeled Future Change From a Baseline...................................................................... 45

4.1.3 Observed Impacts Under Comparable Conditions ........................................................................ 47

4.1.4 Exceeding Thresholds.................................................................................................................... 49

4.1.5 Capacity to Adapt to Change ........................................................................................................ 50

4.1.6 Relative Degree of Response to Climate Variation ......................................................................... 51

4.2 Informing Natural Resource Management Recommendations...............................................52

4.2.1 Planning for the Future.................................................................................................................. 53

4.2.2 Promoting Resistance and Resilience ............................................................................................ 54

4.2.3 Landscape-Level Planning............................................................................................................. 55

4.2.4 Limitations.................................................................................................................................... 55

4.2.5 Assessment Tools and Guidance.................................................................................................... 55

Literature Cited ..........................................................................................................................56

iv
Chapter 5. Vulnerability of Southwestern Species and Ecosystems to 

Climate Change: A Review ...........................................................................................60

5.1 Introduction .........................................................................................................................60

5.2 Methods...............................................................................................................................61

5.3 Southwest Vulnerability Assessments....................................................................................67

5.3.1 Vulnerability of Southwestern Species and Communities............................................................... 69

5.3.2 Vulnerability of Southwestern Habitats.......................................................................................... 77

5.3.3 Vulnerability of Southwestern Water.............................................................................................. 81

5.3.4 Comprehensive Assessments......................................................................................................... 87

5.4 Future Assessment Needs.....................................................................................................88

5.5 Using Assessments to Develop Adaptive Management Strategies for the Southwest ..............90

5.6 Additional Information .........................................................................................................91

Literature Cited ..........................................................................................................................92

Appendix 1. ...................................................................................................................102

Appendix 2. ...................................................................................................................105

v
Tables

Table 2.1. Relevance of spatial scale for assessing vulnerability to climate change 

(from Peterson and others 2011). .......................................................................................... 8

Table 3.1. A general overview of models and methods used to evaluate vulnerability 

for climate change assessment............................................................................................ 20

Table 3.2. Best use, strengths, and weaknesses of analyses commonly used for 

predicting future conditions. All methods require climate inputs such as future 

temperature and precipitation but vary in how data is processed to determine 

vulnerability. At the most flexible, natural history data on species traits is sufficient 

for many index based measuring methods. More intensive analyses require detailed 

data on CO2 uptake and nutrient cycling obtained from experimental data. All 

methods are sensitive to bias and uncertainties in climate data........................................... 24

Table 3.3. Examples of methods used to provide relative measures of vulnerability 

or identify areas of vulnerability. Type refers to the method employed. Quantifications 

typically tally traits associated with vulnerability. Models typically combine some 

type of tallying method and extrapolate it to spatially explicit issues 

(e.g., landscape fragmentation). .......................................................................................... 31

Table 3.4. Examples of frameworks that can be used to create or organize climate 

change vulnerability assessments........................................................................................ 33

Table 4.1. Summary of common measures of vulnerability, applicable time scales, 

key limitations, and common applications.......................................................................... 52

Table 4.2. Resources, including data repositories, for exploring management options 

and for use in climate change vulnerability assessments. .................................................... 56

Table 5.1. Reviews and syntheses of climate change projections and impacts for 

species and habitat specific to the SW United States........................................................... 62

Table 5.2. Climate change vulnerability assessments that contain information for the 

SW United States. We indicate the scale and spatial focus of the assessment as 

well as identify the emphasis and primary methods for determining or measuring 

vulnerability. Climate parameters are listed as reported. “na” indicates not 

applicable or not available.................................................................................................. 63

Table 5.3. Number of assessments focused on animal, plant, or both types of 

organisms and the various scales presented in 36 assessments of climate change 

vulnerability for the SW. ..................................................................................................... 68

Table 5.4. Spatial and biological scale of 36 climate change vulnerability assessments 

conducted for the SW United States. Landscape here refers to studies that address 

composition of multiple communities across a large area (e.g., North America).................. 69

Figures

Figure 1.1. Conceptual model for assessing vulnerability, showing linkages among 

exposure, values, and system condition (sensitivity). The authors distinguish three 

components of sensitivity for their systems. “Buffers” and “stressors” are 

human-induced, whereas “intrinsic sensitivity” is based on inherent characteristics 

independent of human influence (Furniss and others 2012: Fig. 2). This diagram 

demonstrates the relationship between management and vulnerability. ................................ 2

Figure 1.2. An overview of assessment components and some key considerations for 

designing or applying a vulnerability assessment. An assessment will include all 

elements of scope (reviewed Chapter 2) and one or more measures of response 

(Chapter 3) and vulnerability (Chapter 4). ............................................................................ 4

Figure 2.1. Adapted from Poiani and others 2000. Biodiversity at various spatial 

scales. Levels of biological organization include ecosystems and species. 

Ecosystems and species are defined at four geographic scales: local, intermediate, 

coarse, and regional. The general range in hectares for each spatial scale is 

indicated (left of pyramid), as are common characteristics of ecosystems and 

species at each of the spatial scales (right of pyramid)......................................................... 10

vi
Figure 3.1. Top. Temporal and spatial variation among biological scales (light blue) 

and the impact of common disturbances (dark blue) for forested ecosystems 

(patterned after Bunnel and Huggard 1999). See Chapter 2 for a detailed discussion. 

Bottom. Spatial and temporal extent of target ouput for methods for assessing 

impact of climate change on various ecological parameters. Disturbance 

parameters (grey) are shown for reference. This figure represents the common 

geographic scale at which certain analyses are conducted. GCMs (dark blue) 

produce daily and monthly estimates of a of broad-scale climate parameters. 

BCE models (light green) are commonly used for species-level analysis and are 

often used to estimate distribution over a wide area. Process based models (light 

green) usually involve plant functional groups and use physiological processes to 

estimate distribution of groups across a wide spatial scale. Successional models 

(light green) are oriented toward patch-level interactions. The spatial extent of 

frameworks and indices (yellow) is typically defined by the user’s objectives. 

Hydrological models (blue) can focus on the entire reach of a target river or, more 

commonly, on a watershed of interest. Most methods focus on estimating conditions 

over the next century and few address seasonal variations. Exceptions include 

hydrological models, process-based models, and some index-based methods. .................. 22

Figure 3.2. Example of the Gallatin National Forest watershed vulnerability model 

(from Louie 2012)............................................................................................................... 25

Figure 3.3. From IPCC (2007: 2-2). Range of major uncertainties that are typical in 

impact assessments, showing the “uncertainty explosion” as these ranges are 

multiplied to encompass a comprehensive range of future consequences, including 

physical, economic, social, and political impacts and policy responses (modified 

after Jones 2000, and “cascading pyramid of uncertainties” in Schneider 1983).................. 36

Figure 4.1. Observed response of terrestrial wildlife to climate change effects from 

189 studies reviewed by Joyce and others (2008). These patterns can be translated 

into information that identifies which species might be most affected by future 

climate conditions. Adapted from Joyce and others (2008).................................................. 44

Figure 4.2. Richness of American tree species under current (A) and future (B and C) 

climate scenarios. Future (B) represents projections under increasing CO2 whereas 

future (C) represents conditions with future decrease in CO2. This study presents an 

estimate of future effects based upon current understood relationships between tree 

richness and climate variables. CE=Climate envelope richness. Adapted from 

McKenney and others 2007. ............................................................................................... 46

Figure 4.3. An example of how the degree of response (species richness) might be 

used to measure vulnerability. Here, darker colors represent areas that are expected 

to experience the greatest change and are therefore at greatest risk of negative 

impact, in species composition under a A1B greenhouse gas scenario. 

(A) represents projections for a 100 x 100-km grid and (B) represents 200 x 200-km 

grid projections. Adapted from Lawler and others (2009).................................................... 47

Figure 4.4. Observed changes in nesting attempts and fledglings per pair among four 

bird species in California due to extreme drought. Year 2002 was the driest year 

on record for the past 150 years. This study provides an example of how and to 

what degree future populations might be affected by particular climate trends. 

Adapted from Bolger and others (2005). ............................................................................. 48

Figure 4.5. Transitions between carbon (C) states: C4 to C3 (red) and C3 to C4 (blue) 

grass communities in response to increased temperatures and atmospheric pCO2

since the last glacial maximum (approximately 20,000 years before present). 

Adapted from Collatz and others (1998). ............................................................................ 48

Figure 4.6. Results of this study identified the critical thresholds that lead to widespread 

mortality in pine species in the Southwest. From Breshears and others (2005). 

Copyright 2005 National Academy of Sciences, USA. ....................................................... 49

Figure 4.7. Schematic of the relationship between adaptive capacity and species 

vulnerability. Adaptive capacity affects species vulnerability by modulating 

exposure and sensitivity. Adapted from Engle 2011............................................................. 50

vii
Figure 4.8. Effect of each driver on biodiversity change for each terrestrial biome and 

freshwater ecosystem type calculated as the product of the expected change of 

each driver times its impact for each terrestrial biome or freshwater ecosystem. 

Values are relative to the maximum possible value. Bars: 1, land use; 2, climate; 

3, nitrogen deposition; 4, biotic exchange; 5, atmospheric CO2. From Sala and 

others (2000). Reprinted with permission from American Association for the 

Advancement of Science (AAAS). ................................................................................................. 51

Figure 4.9. Steps for developing and implementing adaptation options. This diagram, 

first presented in Peterson and others (2011), represents an iterative process and 

includes repetitive review and ranking steps. ...................................................................... 53

Figure 4.10. Example of work-flow used to create a vulnerability assessment that is 

translated to on-the-ground management strategies. Endpoints are determined by 

the questions and objectives of managers and are largely influenced by management 

strategies to mitigate, improve, or facilitate response to climate change. Target 

selection depends upon the desired endpoint and influences the relevant measures 

used to gauge target vulnerability. Targets also influence the availability of relevant 

measures. ........................................................................................................................... 54

Figure 5.1. Figure from the U.S. Global Change Research Program’s 2009 report 

showing relative changes in precipitation. Predicted change in spring precipitation 

for 2080-2099 as compared to 1961-1979 for lower and higher emission scenarios. 

Hatched areas indicate greatest confidence. Report available at: http://nca2009.

globalchange.gov/southwest ............................................................................................... 61

Figure 5.2. Vulnerability of species from the Barry M. Goldwater Range (from Bagne 

and Finch 2013). This study used a vulnerability index tool that assessed exposure, 

sensitivity, and adaptive capacity through scenario building and a questionnaire 

that results in scoring system. Scores were generated for four categories as well 

as for the collective (overall) expected vulnerability. .......................................................... 72

Figure 5.3. Forest responses to two climate change scenarios (OSU, GFDL-Q at 

10-km (fine grid) and 50-km (coarse grid) scales. From Chapter 4 of Joyce and 

Birdsey (2000). ................................................................................................................... 78

Figure 5.4. Projected changes in median runoff for 2041-2060 as compared to 

1901-1970 based on modest emission scenario. Grey lines represent water-resource 

region. Hatched areas indicate greatest confidence in model projections. From 

the U.S. Global Change Research Program, 3rd National Climate Assessment. ................... 82

Figure 5.5. Vulnerability of watershed (blue outline) as estimated by combining 

scored estimates of water resource values, sensitivity, and exposure. .................................. 86

viii
USDA Forest Service RMRS-GTR-309. 2013. 1

Chapter 1. Vulnerability Assessments in Natural Resource 

Management: Overview and Application

1.1 The Climate Change Challenge

The impacts of climate change on global ecosystems are apparent, and future 

change is likely to be dramatic (Nicholls Cazenave 2010; Williams and others 

2010; Chen and others 2011; Notaro and others 2012). Climate change is emerging 

as a dominant challenge for resource managers and decision-makers. The breadth 

and complexity of climate change effects make it difficult to accurately detect, predict or understand the full range of impacts on ecosystems and, therefore, to design 

management actions that can minimize undesirable effects. Management based 

only on current or recent past conditions may not be adequate to address longterm changes in resources prompted by climate change (Millar and others 2007). 

Consequently, it is critical to plan ahead based on evidence and predictions of 

future climate and related impacts. Climate change vulnerability assessments seek 

to synthesize the available information on changes of and responses to climate for 

a region, sector, or resource of interest. Through this synthesis, assessments can 

be used to identify priority and effective management options that are essential 

considering the limitations of time, money, and personnel. The need for assessments accompanied by proactive and targeted management is great because a large 

number of stressors affect natural and human systems, the pace of environmental 

change is rapid, and management resources are limited. Vulnerability assessments 

cover a wide range of topics, methods, and scales. In this report, we outline key 

concepts, assessment methods, modeling approaches, and provide guidance for using assessments for natural resource management under climate change.

1.2 Climate Change Vulnerability Assessments

The Intergovernmental Panel on Climate Change (IPCC) defines vulnerability 

to climate change as “the degree to which a system is susceptible to, and unable to 

cope with, adverse effects of climate change, including climate variability and extremes” (Solomon and others 2007). Vulnerability assessments are a fundamental 

tool available to many disciplines and sectors but are particularly applicable to management of natural resources, which is the focus of this document. Vulnerability 

is a combination of exposure, sensitivity, and adaptive capacity, although these 

components are not always distinct nor are all three always integrated into every 

vulnerability assessment (USGCRP 2011; Fig. 1.1). Essentially, the response of 

a system, species, or individual to climate change will depend on: (1) the effects 

to which it is exposed, (2) how it responds to or is sensitive to those impacts, 

and (3) its capacity to ameliorate the effects it experiences. Vulnerability assessments generally differ from impact assessments, which identify and quantify the 

effects of climate change, by focusing on the causes of and variation in expected 

impacts with the specific intent to identify effective and efficient management actions that will reduce the negative impacts predicted (Füssel and Klein 2005; Glick 

and others 2011). Another approach similar to a vulnerability assessment is a risk 

assessment or decision analysis, which focuses on the probability of negative outcomes and their consequences. The negative outcome is a set impact threshold, and 
2 USDA Forest Service RMRS-GTR-309. 2013.

a risk assessment can directly incorporate the uncertainty surrounding predicted 

impacts (Harwood 2000; Jones 2001). If only the probability of negative outcomes 

are assessed, a risk assessment is essentially a vulnerability assessment, but often 

these risk-focused approaches take further steps to incorporate decision analysis, 

which can weigh the effect of different management options on the outcome and 

estimate the accompanying uncertainty (Harwood 2000).

Climate change issues are well addressed by vulnerability assessments, which 

can synthesize large amounts of complex information. By examining vulnerability 

to climate change, managers can rank needs as well as pinpoint the causes of vulnerability for valued resources. Assessing vulnerability can also identify species 

and systems that are resilient or even have a positive response to climate change, 

such as future increases in range or local abundance (Notaro and others 2012; 

Quijada-Mascaranas and others, submitted). Resources with predicted positive 

responses may also need to be managed if their responses negatively affect vulnerable or priority resources. Some highly vulnerable subjects may be low priority 

if no effective actions to reduce vulnerability can be taken, if the cost of action is 

excessive, or if the subject is deemed of less immediate value than other targets. In 

developing management strategies, managers will need to consider vulnerability to 

multiple disturbances, including climate change, in relation to other prioritization 

factors such as policy, feasibility, and economics.

Once practitioners have gathered and processed information on climate change 

impacts and vulnerabilities for their selected targets, the next step is to use that 

information to decide if actions are needed and to design those actions to be most 

effective and efficient. Applying vulnerability measures helps reduce complex information and identify expected impacts. However, vulnerability assessments are 

not the only tools for determining management actions. For example, management 

priorities and target sensitivities may already be known or set by other priorities 

unrelated to vulnerability. Multiple targets can be integrated into a single vulnerability assessment so that susceptibilities of multiple areas can be combined. These 

integrated assessments, however, are less common and require that some relationship among the targets be defined. Spatial approaches are conducive to assessing 

multiple sectors simultaneously, but not all targets of interest can always be represented spatially.

Figure 1.1. Conceptual model for assessing vulnerability, showing linkages among exposure, values, 

and system condition (sensitivity). The authors distinguish three components of sensitivity for their 

systems. “Buffers” and “stressors” are human-induced, whereas “intrinsic sensitivity” is based on 

inherent characteristics independent of human influence (Furniss and others 2012: Fig. 2). This 

diagram demonstrates the relationship between management and vulnerability.
USDA Forest Service RMRS-GTR-309. 2013. 3

1.3 Vulnerability Assessment Approaches

A wide range of approaches has been used to assess natural resources impacted by climate change and to glean management recommendations. Mathematical 

modeling approaches to estimate change or probability of change are common and 

can range from complex sets of equations representing multiple influencing factors 

or aspects of the resource to indices that distill response into ranks or classification 

representing vulnerability. Mathematical modeling covers a large number of methods, which are outlined in Table 3.1 and detailed in Appendix 1. All models are 

limited in their ability to represent all contributing processes and factors. Indices 

simplify response and are specifically designed to address vulnerability and transition easily to management recommendations. More complex models focus on 

improving predicted response, but their results often need to be translated into a 

measure of vulnerability (see Chapter 4). Conceptual frameworks focus on the relationships and linkages among factors, resource targets, and interacting elements. 

Conceptual frameworks can be used to clarify goals or identify key elements and 

are often used when vulnerability is assessed through a consensus of experts or 

stakeholders (Table 3.2). Frameworks are well suited to incorporating local issues and, like indices, can be effective even when information is sparse. Synthesis 

is a common assessment approach that collects available information on impacts 

and the resource of interest to compile knowledge and glean recommendations. 

Included information may be derived from any number of approaches, including 

modeling, experimental studies, or expert opinion. Because a synthesis gathers, reviews, and integrates information, there is no specific methodology for ranking or 

formally integrating information sources, but a large number of disparate aspects 

can be combined and factors other than vulnerability can be included (e.g., legal 

requirements, costs, and cultural values). Note that these approaches are not mutually exclusive and a single assessment may integrate multiple approaches.

1.4 Steps for Conducting a Vulnerability Assessment

Climate change vulnerability assessments are a mechanism for natural resource 

managers to incorporate potential future effects of climate on species and habitat 

into management plans. They allow complex information to be condensed into 

predictions relevant to natural resource managers. Before this can happen, managers and researchers must determine the structure and goal of the vulnerability 

assessment (Fig. 1.2). Managers can target an array of organisms or habitats depending on their specific need. For some, the implications of climate change in 

the immediate future are most relevant to ongoing activities, whereas long-term 

changes to structure and function of habitats may have relevance to future development and conservation plans. Assessments of vulnerability to climate change 

necessarily require some knowledge or prediction of response to expected conditions. These predictions, which can be obtained from a wide range of methods, 

then need to be translated into some measure of the vulnerability that informs 

management how and why a negative impact is expected to occur. The specific 

form of these elements depends on the goals of those designing the assessment. In 

summary, vulnerability assessment involves several steps:

(1) Select a target, scope, and scale.

(2) Predict climate change response.

(3) Translate response into vulnerability.

(4) Glean management implications.
4 USDA Forest Service RMRS-GTR-309. 2013.

These steps, whether or not they are highlighted explicitly, are key components 

of all vulnerability assessments. The chapters that follow discuss how assessments 

target appropriate subjects and scope (Chapter 2). In Chapter 3, we cover the tools 

and methods commonly used to predict climate change response and then discuss 

how to translate that response into vulnerability (Chapter 4). Finally, we synthesize 

the use and application of climate change vulnerability assessments conducted for 

the southwestern United States (Chapter 5).

1.5 Vulnerability Assessments in Natural Resource Management

Many natural resource agencies are conducting vulnerability assessments at various levels of sophistication and for different purposes, often in relation to agency 

strategic plans and requirements. As an example, the USDA Forest Service has 

developed a National Roadmap for Responding to Climate Change (USDA Forest 

Service 2011) based on its strategic framework (USDA Forest Service 2009). The 

Scorecard identifies vulnerability assessment and adaptation as 2 of 10 elements 

in four dimensions of accountability for responding to climate change (Box 1.1). 

Managers must be able to respond “yes” to 7 of the 10 elements, posed as questions, in the agency’s “performance scorecard.” The vulnerability assessment 

question, “Has the Unit engaged in developing relevant information about the vulnerability of key resources, such as human communities and ecosystem elements, 

to the impacts of climate change?” can be answered through a variety of approaches. Another element focused on adaptation is linked to vulnerability: “Does the 

Unit conduct management actions that reduce the vulnerability of resources and 

places to climate change?”

Figure 1.2. An overview of assessment components and some key considerations for designing or 

applying a vulnerability assessment. An assessment will include all elements of scope (reviewed 

Chapter 2) and one or more measures of response (Chapter 3) and vulnerability (Chapter 4). 
USDA Forest Service RMRS-GTR-309. 2013. 5

Forest Service units have addressed vulnerability in different ways. Some 

Forests have assessed vulnerability at the species level using such tools as the 

NatureServe’s Climate Change Index (Young and others 2010), the SAVS questionnaire (e.g., Bagne and others 2011) and more, while other forests have assessed 

vulnerability at the watershed or landscape level (e.g., Box 1.2). The species and 

landscape levels have been combined in some assessments (e.g., Box 1.3) to locate geographical areas where multiple species and the area itself are vulnerable. 

Following the four steps outlined in section 1.4 helps units get to “yes” in assessing 

vulnerability. These steps as well as our review of considerations, methods, and 

examples that follow in the next four chapters should be useful for any agency or 

stakeholder interested in learning about and performing vulnerability assessments. 

Box 1.1. Forest Service Climate Change Performance Scorecard (USDA Forest Service 2011).
6 USDA Forest Service RMRS-GTR-309. 2013.

Box 1.3. Reports showing applications of the SAVS tool at the species and landscape levels for the 

Coronado National Forest.

Spatial patterns of cross-species 

vulnerability were identified by 

applying SAVS scores to species 

habitat distribution models.

RMRS applied the SAVS tool to 

30 at risk species on the 

Coronado National Forest

SAVS, an RMRS tool, uses a 

questionnaire of 22 predictive 

criteria to create vulnerability 

scores for terrestrial vertebrate 

species

Vulnerability Assessments: What Are Forests Doing?

Vulnerability of forest tree 

species and non-forested habitat 

to climate change and proposed 

management actions under future 

climate scenarios in Washington.

Impacts and vulnerability of 

tree species and forest types 

to climate change in northern 

Wisconsin.

Assesses past climate history, 

future climate scenarios and 

natural resource vulnerabilities 

in Wyoming

Box 1.2. Examples of vulnerability assessments conducted on forests.
USDA Forest Service RMRS-GTR-309. 2013. 7

Literature Cited

Bader, D.C., C. Covey, W.J. Gutowski, Jr., I.M. Held, K.E. Kunkel, R.L. Miller, 

R.T. Tokmakian, and M.H. Zhang. 2008. Climate Models: An Assessment 

of Strengths and Limitations. A Report by the U.S. Climate Change Science 

Program and the Subcommittee on Global Change Research. Department of 

Energy, Office of Biological and Environmental Research, Washington, DC, 

USA. 124 pp.

Füssel, H., and R. Klein. 2006. Climate Change Vulnerability Assessments: An 

Evolution of Conceptual Thinking. Climatic Change 75: 301-329.

Glick, P., B.A. Stein, and N.A. Edelson, eds. 2011. Scanning the conservation 

horizon: a guide to climate change vulnerability assessment. National Wildlife 

Federation, Washington, DC.

Harwood, J. 2000. Risk assessment and decision analysis in conservation. 

Biological Conservation 95: 219-226.

Iverson, Louis R., M.W. Schwartz, and Anantha M. Prasad. 2004. Potential 

colonization of newly available tree-species habitat under climate change: an 

analysis for five eastern US species. Landscape Ecology 19: 787-799.

Jenni, L., and M. Kery. 2003. Timing of autumn bird migration under climate 

change: advances in long–distance migrants, delays in short–distance migrants. 

Proceedings of the Royal Society, London 270: 1467-1471.

Jones, R. 2001. An Environmental Risk Assessment/Management Framework for 

Climate Change Impact Assessments. Natural Hazards 23: 197-230.

Parmesan, C. 2006. Ecological and Evolutionary Responses to Recent Climate 

Change. Annual Review of Ecology, Evolution, and Systematics 37: 637-669.

Peterson, D.L., C.I. Millar, L.A. Joyce, M.J. Furniss, J.E. Halofsky, R.P. Neilson, 

and T.L. Morelli. 2011. Responding to climate change in National Forests: a 

guidebook for developing adaptation options. USDA Forest Service, Pacific 

Northwest Research Station, Gen. Tech. Rep. PNW-GTR-855.

Schmitz, O.J., E. Post, C.E. Burns, and K.M. Johnston. 2003. Ecosystem Responses 

to Global Climate Change: Moving Beyond Color Mapping. BioScience, 

53(12):1199-1205.

Smith, J.B., S.H. Schneider, M. Oppenheimer, [and others]. 2009. Assessing 

dangerous climate change through an update of the Intergovernmental Panel 

on Climate Change (IPCC) “reasons for concern.” Proceedings of the National 

Academy of Sciences 106:4133-4137.

Solomon, S., D. Quin, M. Manning, eds. 2007. Climate change 2007: the physical 

science basis. Contribution of working group I to the fourth assessment report of 

the Intergovernmental Panel on Climate Change. Cambridge University Press, 

Cambridge, UK.

U.S. Global Change Research Program [USGCRP]. 2011. Uses of vulnerability 

assessments for the National Climate Assessment. U.S. National Climate 

Assessment, Report 9.

USDA Forest Service. 2007. Strategic Plan FY 2007-2012, United States 

Department of Agriculture, Forest Service, FS-880. Available at: http://www.

fs.fed.us/publications/strategic/fs-sp-fy07-12.pdf.

USDA Forest Service. 2011. Navigating the Climate Change Performance 

Scorecard. A Guide for National Forest and Grasslands, Version 2. Available 

at: http://www.fs.fed.us/climatechange/advisor/scorecard/scorecard-guidance08-2011.pdf.
8 USDA Forest Service RMRS-GTR-309. 2013.

Chapter 2. Targets, Scope, and Scale

Chapter 2 Talking Points

Considerations for evaluating or designing a vulnerability assessment:

(1) What is the target or targets of assessment?

(2) What aspect of the target was or can be measured to estimate vulnerability?

(3) What geographic region does this assessment apply to?

(4) What time scale applies to the predicted changes?

(5) How are interactions across scales addressed?

(6) How do the target, scope, and scale of the assessment affect its application to 

my management questions?

Managers must consider their objectives and goals in order to design an assessment that will fulfill information needs. Vulnerability assessments are diverse 

and selection of individual assessments presents a variety of tradeoffs for users 

(Table 2.1). Assessments are often limited by the type and form of climate change 

impacts they consider and apply only to limited targeted region areas and time 

periods. Planning timelines, mandates for resource management, and availability 

of information all contribute to the initial selection of targets, and the scope, and 

scale of an individual assessment.

2.1 Assessment Targets

Climate change has the potential to affect the entire range of human and natural 

systems, so a key aspect of a vulnerability assessment is selecting what population, 

species, functional group, process, or ecosystem will be addressed. Quantifiable 

Table 2.1. Relevance of spatial scale for assessing vulnerability to climate change (from Peterson and others 2011).

Spatial scale

Largea Intermediateb Smallc

Availability of information High for future climate Moderate for river systems, High for resource data, low for climate

on climate and climate and general effects on vegetation, and animals change

change effects vegetation and water

Accuracy of predictions of High Moderate to high High for temperature and water, low to

climate change effects moderate for other resources

Usefulness for specific Generally not relevant Relevant for forest density Can be useful if confident that

projects management, fuel treatment, information can be downscaled

wildlife, and fisheries accurately

Usefulness for planning High if collaboration High for a wide range Low to moderate

across management of applications

units is effective

a More than 10,000 km2 (e.g., basin, multiple National Forests) b 100 to 10,000 km2 (e.g., subbasin, National Forest, Ranger District) c Less than 100 km2 (e.g., watershed)
USDA Forest Service RMRS-GTR-309. 2013. 9

aspects of the target as they relate to management objectives will determine the 

variable upon which vulnerability measures are based. For example, population 

growth rates could be used to assess a group of frog species at risk of extinction and 

stream flow would be an appropriate variable for a target watershed that provides 

water to urban or agricultural areas. Vulnerability assessments are most useful 

when they address the critical needs of managers or conservationists. A wide range 

of assessment targets, from individuals or populations to landscapes and processes, 

can be evaluated for vulnerability. Targets represent the resource value of interest 

and will depend on management objectives, but targets will also be constrained by 

policies, budgets, and available information (see Chapter 4, Table 4.1). Important 

considerations for target selection include the available information regarding potential system or species to be assessed, the time line of desired outcomes, and the 

specific objectives of the user. The audience for which the vulnerability assessment 

is being prepared and the input of stakeholders can also be important considerations for selecting targets (Glick and others 2011). If the target is a single subject 

(e.g., one species, one watershed), the purpose of a vulnerability assessment is 

to dissect the nature of expected impacts to that target. When the target includes 

multiple subjects (e.g., plant functional groups, watersheds of Oregon, and endangered species), ranking or prioritization of the subjects is possible along with 

information on the particular vulnerabilities of the individual subjects. There are 

also new efforts to integrate vulnerability across multiple targets or sectors to get a 

more complete picture of vulnerability (USGCRP 2011). When using assessment 

results to generate management strategies, it is critical to consider how and why 

targets were selected to ensure that the information provided by the assessment is 

used appropriately.

Limitations in data availability influence the feasibility of assessing of particular targets. Data limitations reduce the applicability of many types of vulnerability 

assessments. For example, although species’ vulnerability can be assessed with 

minimal data in some situations (Bagne and others 2011), a relatively complete 

understanding of species biology provides better prediction of response and thus 

a better approximation of vulnerability. Response of broader plant functional groups or community types (e.g., mixed-conifer forest, semi-arid shrubland, 

and grasslands) can be very useful for managers because they encompass many 

whole-system properties that may be missed when single species are the focus of 

assessment. Similarly, estimates of climate change effects for ecosystem processes, 

which are very useful for identifying fundamental large-scale vulnerability, require 

a great deal of data and an understanding of complex dynamics among multiple 

contributing components. Though vulnerability assessments will be most useful 

and applicable when used on systems that have adequate data, assessments that 

focus on more general targets are possible and still valuable where data are limited.

2.2 Scope

Assessments are generally prepared for a specific geographic region and time 

period. The scope of the assessment considers both temporal and spatial scale, 

which will be determined by the availability of suitable input data, the management unit, selected assessment target(s), and timeline for management planning. 

For natural resource managers, management units and jurisdiction often dictate the 

focal region. Time scale is an important aspect of climate projections that affects 

application to management goals. Management strategies may focus on short-term 

goals relating to preserving or restoring current conditions or on long-term goals 
10 USDA Forest Service RMRS-GTR-309. 2013.

that aim to maintain ecosystem function and stability over time. These distinct 

temporal components naturally lead to different targets and objectives for a vulnerability assessment. Scope also applies to the range of stressors used (i.e., the source 

of vulnerability) in the assessment because climate change includes not just temperature and precipitation but also related phenomena such as stream flow, erosion, 

disturbance (fire and insect outbreaks), and extreme weather events. Therefore, the 

range of climate-related stressors considered can be quite broad and encompass 

multiple interrelated stressors or focus more narrowly on a single stressor of interest (e.g., drought, sea level rise) that has a strong effect on the target. Inclusion of 

non-climate change stressors can also broaden scope of the assessment.

Difficulties arise when the temporal and spatial scales of available data are limited and/or differ from the desired scope of the assessment. Available data such 

as outputs from climate models are scale limited and generally much larger than 

typical management units. To produce projections at finer scales, many downscaling methods are available for climate projections. The most commonly used 

approaches are dynamic (in which climate physics and chemistry are modeled at 

regional scales, in the same way used in General Circulation Models or GCMs), 

and statistical downscaling, which is accomplished by interpolating coarser resolution GCM data using a variety of spatial statistical methods. Downscaling brings 

climate projections to a spatial scale that can be very useful for managers (e.g., 

25 km2 grid cells). Downscaling can also correct regional bias found in many 

global climate projections and is inherent to results of efforts to produce projections that are averaged across multiple climate models (Bader and others 2008). 

However, these methods, along with the unknown progression of greenhouse gas 

inputs, add error, which contributes to variability and uncertainty in the predictions 

made by a vulnerability assessment.

Figure 2.1. Adapted from Poiani 

and others 2000. Biodiversity 

at various spatial scales. Levels 

of biological organization 

include ecosystems and species. 

Ecosystems and species are 

defined at four geographic 

scales: local, intermediate, 

coarse, and regional. The 

general range in hectares for 

each spatial scale is indicated 

(left of pyramid), as are 

common characteristics of 

ecosystems and species at each 

of the spatial scales (right of 

pyramid).
USDA Forest Service RMRS-GTR-309. 2013. 11

2.3 Biological Scale

Biological scales range from the levels of genomes and species (e.g., Durance 

and Ormerod 2007; Triepke and others 2012) to continent-scale ecological biomes 

(e.g., Rehfeldt and others 2012) (Fig. 2.1). The appropriate scale depends on the 

target defined for the vulnerability assessment, as mentioned in the previous section. Furthermore, assessments may include evaluation targets across multiple 

scales or cross-scale. Both spatial and temporal scales may be considered simultaneously with any given biological entity. Time scales vary from years (e.g., Allen 

and Breshears 1995) to a century or more (e.g., Parmesan and Yohe 2003), while 

spatial scales vary from individual niches and biotic communities (e.g., Hofstetter 

and others 2007) to intercontinental levels (Allen 2009). From the standpoint of 

conservation biology, biological scales are typically expressed simultaneously in 

terms of space and time. It is important to understand how biological processes 

operate across a range of spatial and temporal scales and how those processes are 

ultimately manifested in biological diversity.

Biological scales provide key concepts in linking temporal and spatial—local, regional, and biogeographical—scales where dynamics are driven by climate 

change. For example, the effects of landscape homogenization, as a result of warmer temperatures and uncharacteristic fires, are sometimes treated as static when, in 

reality, the spatial effects of changing landscape patterns on the distribution of 

specific species may be apparent only at the population level. Spatial responses of 

populations and metapopulations to disturbances must be understood and quantified at a range of spatial scales concurrently with the frequencies and intensities of 

disturbance.

Here, we provide a brief look at biological scale in respect to conservation issues and climate change. Biological scales are an initial response to management 

or research inquiries—for example, “how will climate change projections affect 

the willow flycatcher?” At broader scales, one might ask “where is pinyon die-off 

most likely?” At continental scales, “what is the potential range of suitable habitat 

for Douglas-fir 100 years from now?” While there is a considerable range of biological scales, we briefly consider two—species and ecosystems. We then present 

a review of the Forest Service landscape analysis, which provides an example of 

one way in which an assessment manages scope and scale.

2.3.1 Species Scale

Individual species are a common concern for managers and researchers in regard to climate change vulnerability, and their response will filter up to targets 

at broader scales. Species often reflect a familiar operational level and a suitable 

biological scale, given that species protection is fundamental to conservation and 

is embedded in core mandates of Federal agencies (e.g., 7 USC § 136, 16 U.S.C. 

§ 1531 et seq.). The rationale for these mandates is that those species that are 

sensitive to climate change can be identified, their locations and habitats can be 

catalogued and mapped, and species can be managed through protective habitat 

measures, including adaptation (Millar and others 2007). The Nature Conservancy 

identified approximately 120 plant and animal species in the Southwest that are 

at risk according to the habitats most vulnerable to climate change (Robles and 

Enquist 2011). Problems arise at the species scale because of sparse information 

on the vast majority of species.
12 USDA Forest Service RMRS-GTR-309. 2013.

2.3.2 Ecosystem Scale

Ecosystems are the relevant biological scale for application of coarse filter 

methodology, which estimates biodiversity based largely on environmental factors (Cushman and others 2008). Like species, ecosystem entities are familiar to 

managers and researchers alike, in regard to ecological analysis and conservation 

strategies. While definitions for ecosystem vary, in general, ecosystems consist of 

biota that share common habitat features, biogeography, and climate, making them 

a particularly relevant biological scale for the evaluation of climate change.

Ecosystems, however, are problematic to delineate. Ecosystems are far from 

homogenous, spatially or temporally, and are a dynamic and shifting mixture of 

various stages of ecological succession whose expression in time and space bear on 

biological development and disturbance patterns. Nevertheless, ecosystems are often mapped to facilitate vulnerability assessment and ecological analysis (Cleland 

and others 2007; Triepke and others 2008). Once mapped, key questions are posed 

for those evaluating the effects of climate change at the ecosystem scale: (1) to 

what extent are ecosystems affected by climate change in regards to their natural 

functioning; and (2) how can ecosystem function be accommodated through adaptation strategies for the persistence of the species that ecosystems contain (via 

coarse filter analysis). Landscape analysis, for which the ecosystem scale is most 

associated with, is discussed in the next section.

2.4 Case Study: Landscape Analysis

Following is a summary of landscape analysis in the context of climate change 

and vulnerability assessment. This is not an exhaustive overview, but rather a description of common features found in landscape analyses, particular those of the 

USDA Forest Service (e.g., USDA Forest Service 2006). The biological scale most 

easily adapted to landscape-scale analysis is the ecosystem level discussed in the 

previous section; however, a landscape analysis provides the requisite coarse-filter 

framework for the analysis of fine-filter elements (Cushman and others 2008), including individual species of concern and interest. Unlike biological scale, the 

scales associated with landscape analyses are usually spatially and temporally 

explicit. Within the Forest Service, landscape analysis normally includes three interdependent sustainability components, ecological, social, and economic, though 

the focus here is on the ecological component.

Box 2.1. Direction of change from 

historic to future range of variation
USDA Forest Service RMRS-GTR-309. 2013. 13

Another factor common to landscape analyses of the Forest Service is the application of a reference condition–a benchmark range of conditions that reflect 

ecological sustainability for a given attribute (FRCC 2010). Reference condition 

concepts, including their importance in evaluating sustainability, have not been 

lost on the Forest Service’s new Planning Rule (2012), though the definition of 

reference condition is shifting in light of climate change (see Box 2.1). The Forest 

Service recognizes that as ecosystem potentials shift with changing climate that the 

historic range of variation, often used to help describe the reference condition, may 

lose significance. Either way, in the course of landscape analysis, reference conditions are typically identified for key attributes of vegetation community structure 

and composition, disturbance regimes, and other attributes that collectively reflect 

ecosystem structure, function, and process.

In sum, landscape analysis usually involves: (1) the selection of appropriate 

attributes along with spatial and temporal scales for analysis; (2) describing the 

reference condition, current condition, and trends of ecosystem attributes; and 

(3) analyzing the status of those attributes, often as departure from reference conditions (USDA Forest Service 2006).

2.4.1 Ecosystem Attributes

Ecosystem attributes should be meaningful for the characterization of structure, function, and process, and meaningful to past, current, or future management. 

Ecosystem abundance and diversity, for instance, are often described by quantifying successional states, each state delineated by their differences in structure and 

composition—canopy cover class, size class, dominance type (Triepke and others 2005). The proportion of successional stages is compared among reference, 

current, and future conditions. Both reference and future conditions are often identified through landscape simulation models (Weisz and others 2009, 2010), using 

different parameterizations for the type, frequency, and severity of disturbance. 

The degree to which current and reference conditions differ, or to which future 

and reference conditions differ, is shown in tabular summaries and expressed in 

departure index values where lower departure reflects a greater degree of ecological sustainability.

Other ecosystem attributes involve major disturbances. For instance, the frequency of fire, both wildfire and planned ignitions, is quantified by each severity 

class (non-lethal, mixed severity, and stand replacement). Here again, comparisons 

are made between current and reference conditions, or future and reference conditions. Insect and disease agents are likewise quantified by frequency and severity 

for forest and woodland systems (e.g., Lynch and others 2008). Other major disturbance processes include herbivory, erosion, and flooding.

Spatial attributes are not often evaluated with landscape analyses, though we 

recognize the importance of evaluating landscape metrics such as patch size, connectivity, interior forest, and other spatial features significant to the biota of an 

area (Forman and Godron 1986). Though various geographic information systems (GIS) and spatial analysis tool exist for quantitative analysis (McGarigal and 

Marks 1995), the difficulty has often been in establishing reference conditions for 

each ecosystem from which to assess sustainability. Sometimes uncharacteristic 

levels of fragmentation are simply assumed so that analysis is relegated to a comparison of management scenarios and their ability to affect landscape connectivity.

To fully address climate change, much more sophisticated landscape simulation models are necessary, models that can project vegetation patterns based on 

future climate and along with growth and disturbance patterns in natural plant 
14 USDA Forest Service RMRS-GTR-309. 2013.

communities (Bachelet and others 2001). These models have has limited application in the Southwest but will be needed not only to project ecosystem conditions 

but to reestablish reference conditions. Reference conditions of the future will reflect shifting site potential patterns, biological migrations, and new disturbance 

potentials.

2.4.2 Cross-Scale Applications

Any one of the attributes mentioned above can be analyzed at multiple scales. As 

an example, Forest Plan revision analyses that were conducted in the Southwestern 

Region (USDA Forest Service 2006) focused on three nesting scales—ecological 

sections (Cleland and others 2007), Plan Unit (e.g., at the scale of a National Forest 

or National Grassland), and ecological subsections. These three scales have been 

used successfully to assess overall ecological sustainability at the scale of the Plan 

Unit, to identify diversity patterns within the Plan Unit (i.e., a comparison among 

subsections), and to assess the Plan Unit in reference to contiguous ecological 

sections. The analysis of ecological sections provides planners and managers a 

means to determine conservation burden, for instance where ecosystem conditions 

are degraded within other ownerships of the same section for a given ecosystem 

type. Multi-scalar analysis is likewise important for cross-scale interactions that 

can occur with climate change. The diversity within some plant communities, for 

example, may actually increase by the effects of climate change and subsequent 

invasion by novel plant and animal components, while the overall diversity of an 

area may be in decline at upward spatial scales.

While a particular biologic scale may be suited to the chosen target, it is important to simultaneously consider other scales when interpreting a vulnerability 

assessment (Table 2.1). Linking biological scales is necessary if a conservation 

concern occurs at a scale different from its solution. For example, climate change 

is occurring at scales of entire biomes, but the required adaptation strategies for 

fragmented landscapes are more likely to be applied at the scales of individual ecosystems and ecoregions (Cleland and others 2007). Research and analysis 

resulting from the application of different biological scales has shown different 

patterns of vulnerability. For instance, increases in diversity may occasionally occur at the population scale as driven by climate change (Bale and others 2002) but 

may contradict patterns at ecosystem or biome scales where diversity is in decline. 

While many vulnerability assessments consider the scale effect, its inclusion in 

practice is largely missing from the range of studies regarding ecological effect of 

climate change and results from multiple scales are seldom explicitly addressed. 

Others argue that landscape scales are requisite for fully determining cross-scale 

patterns (e.g., Stevens and others 2006), admittedly making assessment more complicated. Interactive effects and disturbance regimes are covered in greater detail 

in Chapter 3.

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18 USDA Forest Service RMRS-GTR-309. 2013.

Chapter 3. Predicting Response to Climate Change

Chapter 3 Talking Points

(1) All approaches are limited by the input data available.

(2) Like assessments, analysis of response is generally limited to a particular target, 

scope, and scale.

(3) Be aware of assumptions and limitations in predictive methodologies.

(4) Anticipate tipping points, interactive effects, and uncertainty even if not 

addressed by assessments.

(5) The future is always uncertain but methods vary in their capacity to address it.

3.1 Introduction

A variety of methods have been developed to explore how climate change will 

affect basic biological processes, ecological function, population dynamics, and/

or species composition. Although the focus of a vulnerability assessment is often negative or undesirable impacts to valued resources, response can cover the 

full range of impacts from negative to positive, and findings of positive effects 

or resilience can be equally important for informing management decisions (e.g., 

Bradley 2009, 2010). In this chapter, we review several of the approaches used 

to measure one or more aspects of response to climate change (Tables 3.1-3.3). 

We also briefly discuss how frameworks are used in the vulnerability assessment 

process (Table 3.4).

Climate change predictions are derived from one or more GCMs based on 

atmospheric physics and ocean-atmosphere interactions, under a multiple predefined greenhouse gas (GHG) scenarios, which estimate future greenhouse gas 

emissions (Nakićenović and others 2000). The models provide estimates of a wide 

range of climate variables. This approach naturally lends itself to studies that relate climate response directly to changes in precipitation, temperature, and other 

variables that are predicted across broad scales of space and time. Indeed, the 

great majority of analyses that address ecological effects of climate change focus on change in species, community type, or habitat distributions in response to 

changes in environmental variables. A smaller but important subset of modeling 

techniques uses aspects of bioenergetic and metapopulation theories to identify relationships between critical processes and climate variables as a measure of impact 

or vulnerability. Studies using these methods may focus on one element, combine 

bioenergetics and population analyses, or use them in conjunction with distribution 

analyses to generate spatially explicit predictions. A second, growing class of studies uses ranking or scoring processes to quantify relative vulnerabilities between 

groups of species, habitats, or even regions. Climate change impacts to hydrological systems and function are assessed through a variety of methods ranging from 

watershed models that simulate expected hydrological conditions to more comprehensive methods that include both biotic and abiotic components. Though both 

terrestrial and aquatic assessments may incorporate similar modeling methods for 

biological diversity, aquatic assessment methods typically contain an additional 

emphasis on the physics of natural systems and specific hydrological components. 
USDA Forest Service RMRS-GTR-309. 2013. 19

In the next chapter, we cover how these estimates or measures of response are 

translated into expected vulnerability and, ultimately, management application.

3.2 General Methods

The following discussion centers on the major approaches used in climate change 

analyses that inform vulnerability assessments (Tables 3.1, 3.2). As discussed here 

and in Chapter 1, assessments may use one or more of these approaches or summarize multiple studies to determine relative vulnerability of assessment targets 

(see Chapter 4). These approaches cover a diverse range of biological and geographic scales (Fig. 3.1) and have unique qualities for addressing management 

questions. In this chapter, we highlight studies that demonstrate the application 

of the methods described for estimating response to climate change. However, in 

most instances, these studies do not represent vulnerability assessments in and of 

themselves.

Hydrological models include approaches that predict changes in streamflow, 

water temperature, and quality or other features, such as flood regime and water 

source, that influence watershed resilience. Hydrological models encompass a variety of mostly statistical approaches that estimate the impact of climate change on 

water systems and can incorporate both ecological and economical aspects of system vulnerability. Hydrological models often inform other modeling approaches 

focused on species or biomes. A second group comprises a variety of approaches 

that model characteristics believed to influence the capacity of species or ecosystems to survive or absorb changes to climate; these are grouped together here 

under the heading of adaptive capacity models. Adaptive capacity measures are 

also commonly used in assessments of socio-economic vulnerability and, although 

relevant to natural resource management, are beyond the scope of this review. 

The largest group includes distribution models, which encompass a range of approaches to estimate future impact to species/communities/biome distributions 

(Appendix 1). These models may generate predictions of future habitat suitability 

for species or communities, future habitat characteristics, patterns of biodiversity, 

or physiological stress for a single species. Finally, a variety of methods aim to 

identify the relative vulnerability or priority of targets through a comparison of 

traits or issues among a group of study subjects. Within this group of tools and 

methods, we focus on those referred to as index-based measures, which have only 

recently emerged within the literature and arose in response to limitations in directly measuring vulnerability (Table 3.3). We briefly discuss some of the strengths 

and weaknesses of each of these approaches (Table 3.2).

3.2.1 Hydrological Models

Hydrological models are a distinct group of models that represent mostly physical processes within an ecosystem. However, this can also refer to models that 

incorporate other methods (bioenergetic models for instance) to present a comprehensive but water-based estimate of future impact. Hydrological models are 

considered a distinct group here due to their specialized nature and their application to the field of water related vulnerability assessments (Table 3.2; see Chapter 

5). Hydrological models are useful for exploring climate effects on a variety of 

aquatic and non-aquatic riparian areas. Most models rely on the understanding that 

many fundamental ecological processes are determined by or related to flow variation (Meyers and others 1999; Guertin and others 2000). Meyers and others (1999) 
20 USDA Forest Service RMRS-GTR-309. 2013. Table 3.1. A general overview of models and methods used to evaluate vulnerability for climate change assessment. Description Target Scope and Scale Examples 1. Hydrological Models Model changes in ground water, stream flow, Water flow, Individual streams to Christensen and others 2008 evaporation, and productivity and nutrient cycles under nutrient cycles, watersheds various climate scenarios. Can infer trends for both productivity abiotic and biotic systems associated with water. 2. Adaptive Capacity Models Models focus on traits important to species or system Animal or plant Species, has been applied to Peery and others 2012 persistence under changing conditions, in particular adaptive ecosystems climate change. This may include studies of landscape capacity connectivity, species dispersal, intraspecific genetic variation, or colonizing ability. Alternatively, methods might quantify observed changes to species ranges, reproductive success, or, for larger scales, ecosystem resilience. 3. Distribution Models A. Simulation/Process-based Simulate processes to predict behavior of model target Models Biogeographic Models Models simulate limiting conditions relating to potential Plant distribu- Vegetation type Neilson 1995; IPCC 1996 vegetation type using differing climate and hydrological tions/primary conditions and include measures of transpiration rates, productivity photosynthetic pathway, soils, disturbance, and CO2. Biogeochemical Models These models simulate ecosystem process such as NPP, carbon Vegetation type IPCC report (BGC) photosynthesis, transpiration, litter fall, soil moisture, storage NPP and nutrient cycling given a set of variables, including temperature, precipitation, solar radiation, soil texture, and CO2. Dynamic Global Combines biogeographic, biogeochemical models, and Plant species Vegetation type, multiple McMahon 2011; Notaro and Vegetation Models hydrology and/or disturbance variables to generate composition spatial and temporal scales others 2012 estimates on a wide variety of functions. These estimates across study (see text) are then used to create distribution maps of plants area (e.g., plant life form). GAP Models Also known as succession or patch models, these add Plants, Species composition. disturbance and explicitly incorporate demographic Composition, Multiple spatial scales; is a components (tree growth, competition, and death) to age class, temporally dynamic model predict probability of species presence. relative abundance
USDA Forest Service RMRS-GTR-309. 2013. 21 Description Target Scope and Scale Examples B. Dynamic Bioenergetic Models Refers to models that incorporate information on energy Animal Animal species/populations Buckley 2008 budgets to either predict species distribution using distribution assumptions regarding potential energy intake or to estimate individual or population persistence (i.e., extinction probability) under varying conditions when combined with population or metapopulation analysis. C. Niche Theory Models Bioclimate Envelope Estimates future distribution based upon current and Distribution of Species through ecosystems Notaro and others 2012 Models (BCE) historical relationships between species/community/ Animal, plant, life-form occurrence and climate variables. Can be habitat, and conducted by classification, regression, and other biodiversity methods (see Box 3.1). Statistical Species Predict potential distribution of species and colonization Animal or plant Species Thomas and others 2004 Distribution Models based on statistical relationships rather than empirical species (SSDM) data. Uses historical and current climate/site distribution characteristics to inform model. Example: Species distribution as a function of land cover type, scaled biodiversity patterns, species area relationships, etc. 4. Prioritization Methods A. Conceptual Models Qualitative descriptions and diagrams of attributes and Ecosystem and Flexible Heemskerk and others 2003 processes of concern. Priorities determined through economic perceived sensitivities, exposure risk, and desired resources outcome. B. Quantification Tools Also known as index or scoring tools. Typically quantify Relative Varies Bagne and others 2011; vulnerability through a tally of traits or characteristics vulnerability of Young and others 2011 associated with increased risk of negative impact. species Identifies priorities and specific vulnerabilities in study system. C. Risk Assessments Often used to consider both socioeconomic and Relative Flexible Jones 2001 ecological vulnerabilities to climate change. Vary in vulnerability of form from statistical approaches to frameworks. entity of interest
22 USDA Forest Service RMRS-GTR-309. 2013.

Figure 3.1. Top. Temporal and spatial variation among biological scales (light blue) and the impact of common disturbances 

(dark blue) for forested ecosystems (patterned after Bunnel and Huggard 1999). See Chapter 2 for a detailed discussion. 

Bottom. Spatial and temporal extent of target ouput for methods for assessing impact of climate change on various 

ecological parameters. Disturbance parameters (grey) are shown for reference. This figure represents the common 

geographic scale at which certain analyses are conducted. GCMs (dark blue) produce daily and monthly estimates of a of 

broad-scale climate parameters. BCE models (light green) are commonly used for species-level analysis and are often used 

to estimate distribution over a wide area. Process based models (light green) usually involve plant functional groups and 

use physiological processes to estimate distribution of groups across a wide spatial scale. Successional models (light green) 

are oriented toward patch-level interactions. The spatial extent of frameworks and indices (yellow) is typically defined 

by the user’s objectives. Hydrological models (blue) can focus on the entire reach of a target river or, more commonly, 

on a watershed of interest. Most methods focus on estimating conditions over the next century and few address seasonal 

variations. Exceptions include hydrological models, process-based models, and some index-based methods. 
USDA Forest Service RMRS-GTR-309. 2013. 23

provided a review of aquatic assessments as well as assessment modeling methods. 

In-stream flow modeling is a habitat-based method that describes a natural environmental regime, which can be used to assess the extent of hydrologic alteration 

resulting from climate change (Meyers and others 1999). Models that estimate nutrient uptake and hydrodynamic properties (nutrient spiraling model) quantify the 

simultaneous processes of nutrient cycling by biota and downstream transport of 

nutrients by water flow. This can be translated to measure of biological productivity or nutrient retentiveness through the use of bioenergetic models. Bioenergetic 

models are also used to estimate the growth rate of fish and other organisms as 

well as the effects of changing temperature regimes. Food web models allow user 

to analyze the indirect effects of climate change through observed changes in food 

web interactions. For instance, loss of fish because of extreme temperatures or of 

algae due to anthropogenic disturbance has cascading effects on river and lake systems (Meyers and others 1999). Watershed-level vulnerability assessments (e.g., 

Fig. 3.2), which integrate information from multiple sources, are very useful from 

a management perspective, and recent advances in methodologies have been published (USDA 2011; Furniss and others 2012).

3.2.2 Adaptive Capacity Models

This category covers a variety of methods that focus on aspects of adaptive 

capacity, which often can be directly translated into vulnerability (Tables 3.1, 

3.2). Adaptive capacity can apply to resources at a variety of scales. For example, 

plant communities at ecotones are often considered to be less resilient or to have 

limited adaptive capacity because of their occurrence in a narrow band of environmental conditions (Allen and Breshears 1998). Greater species diversity has been 

suggested as an ecosystem characteristic incurring greater resilience to climate 

change because of functional redundancy, overlap, and connectivity (Peterson and 

others 1998). Because adaptive capacity can directly tie to genetic diversity and 

adaptation through natural selection, species-level models of adaptive capacity are 

common applications. Species dispersal ability (e.g., Cushman and others 2011), 

species range (Thomas and others 2004), thermal tolerance (Sinervo and others 

2010), bioenergetics, and other variables have been used to measure the adaptive 

capacity of species to deal with changing conditions. Models within this category 

are useful in that they often consider density, extinction rates, and similar variables rather than focusing on presence-absence (Buckley 2008). However, these 

methods rely on a certain degree of information regarding species physiological 

processes and, in many cases, demographic data. Adaptive capacity models are often limited by available data; for example, some species may contain unexpressed 

genetic variation that would permit adaptation to novel climate conditions such as 

increased temperatures or dryness, but such information is often not available to 

researchers and managers (Davis and Shaw 2001).

Processes such as survival, growth, reproduction, and dispersal may change stochastically (e.g., weather-related fluctuations in survival rates) or deterministically 

(e.g., temporal trends in average survival rates because of climate change) and inform how species may be vulnerable to climate change. In one example of a study 

of an adaptive capacity, Kearney and others (2009) integrated biophysical models 

and evolutionary theory to predict climate affects for future habitat suitability of 

mosquitoes as well as traits such as egg resistance to desiccation, which is known 

to limit dispersal. Interestingly, the potential habitat of the mosquito was predicted 

to increase due to warming trends, although water availability might continue to 

be limited in some regions. The authors found evidence that the mosquito may 
24 USDA Forest Service RMRS-GTR-309. 2013.

Table 3.2. Best use, strengths, and weaknesses of analyses commonly used for predicting future conditions. All methods require climate inputs such as future temperature 

and precipitation but vary in how data is processed to determine vulnerability. At the most flexible, natural history data on species traits is sufficient for many index based measuring methods. More intensive analyses require detailed data on CO2 uptake and nutrient cycling obtained from experimental data. All methods are sensitive to bias and uncertainties in climate data.

Analysis type Input Output Best use Ease of use Strength WeaknessHydrological Climate data Flow volume Generate important Ranges from Highly quantitative, provides Sensitive to bias and uncertaintiesModels Observational Flow timing data on water moderate to highly estimate of future condition in climate data and model

data Evaporation availability intensive model assumptions

rates Watershed analysis application requiring 

trained users

Adaptive Climate data Survivorship Species-level Ranges from easily Good precision; able to Typically very specific to studyCapacity Experimental Extinction risk assessments applied to more estimate thresholds of organism; can require considerableModels data Energetic Biodiversity studies intensive methods tolerances; actual measures data input; often consider a limited

cost/output requiring trained of population change number of exposure sources

researcher (e.g., temperature only)

Bioclimate Climate data Estimate of Species/biome-level Requires knowledge Quick process; can be Best for large geographic areas;Envelope Species/habitat spatial assessments of and access to generated from wide range of cannot infer future novel(BCE) presence data distribution of Biodiversity statistical modeling data; can be applied to communities; not a stand-alone

future suitable programs multiple biological scales measure of vulnerabilityclimates

Successional Climate data Successional Individuals Requires knowledge Quantitative; estimates future Limited to one level of biologicalModels Physiological stage Plant functional of and access to conditions, includes multiple diversity; not a stand-alone measure

process data Biomass and groups specific software processes of vulnerabilityDisturbance structure Plant communities programsdata information

Integrated Climate data Future Typically higher Requires Information on multiple Requires considerable data inputModels Disturbance distribution of levels of highly interacting impacts; may

data target organization (e.g., specialized knowledge include some measures of

Future plant functional and access to specific sensitivity and adaptivecondition of groups) software programs. capacityvegetation Integrates multiplestructure and distinct modelingbiomass methods.Future disturbance regimes

Frameworks Stakeholder/ Relative Specific Minimal modeling Flexible and solution driven Data are qualitative and subject to

expert opinion vulnerability management targets skills needed. Often bias of userScientific data/ Priority areas (e.g., species or employs facilitatedliterature or tasks habitats of concern) workshops.

Indices Climate data Relative Species Can compare Flexible and directly relates Output is qualitative and subject to

Expert opinion vulnerability Habitats vulnerability of a to management intervention bias of stakeholderExperimental diversity of targets. points.data Individual or group

implementation.
USDA Forest Service RMRS-GTR-309. 2013. 25

develop increased resistance to egg desiccation under climate change, highlighting 

the relatively understudied impact of warming trends on accelerating evolutionary 

change in ectothermic animals.

The ability of species and natural communities to shift geographically in 

conjunction with shifting climates is an important aspect of adaptive capacity. 

Landscape analyses that identify species immigration patterns under future conditions give managers insight as to how individual species will respond to climate 

change, and are often used to generate estimates of species (or habitat) vulnerability. Cushman and others (2011) specifically address species’ sensitivity to landscape 

fragmentation and future expectations for landscape change due to warming trends 

to identify species and areas most in need of habitat restoration or relocation measures. In another example, the model SHIFT is used to simulate tree seed dispersal 

and potential colonization under climate change based on factors of distance and 

abundance (Iverson and others 2004).

In the absence of genotype changes or dispersal, the current adaptive capacity 

of a species or population depends on local factors related to survival and reproduction. Dynamic bioenergetic models link a bioenergetic or biophysical model 

with other models of species traits to estimate changes to survival or reproduction 

under climate change. Analyses that link demographic models of population and 

metapopulation dynamics can incorporate processes of survival, growth, reproduction and dispersal into estimates of extinction risk (Akçakaya 2009). For example, 

Sinervo and others (2010) analyzed extinction risk in lizards in Mexico by estimating activity time during reproduction and timing of breeding to assess adaptations 

that affect thermal extinctions. In their study, Sinervo and others (2010) describe 

widespread declines of lizards across four continents and within tropical, temperate, rainforest, and desert habitats. Their analysis, showed that climate change 

could compromise population growth rates to the degree that local extinction rates 

will approach 20% by 2080. Meyers (2001) discusses the application of these types 

of models to fish communities.

Figure 3.2. Example of the Gallatin National Forest watershed vulnerability model (from Louie 2012).
26 USDA Forest Service RMRS-GTR-309. 2013.

3.2.3 Distribution Models

Distribution models are used to estimate future habitat suitability or species 

distributions given projections for changes in climate conditions. These models 

are based on the relationship between species distribution and climate or other 

environmental variables (Pearson and others 2003). For this discussion, we group 

species distribution models into four primary categories: environmental envelope 

models, process or simulation based models, succession models, and integrated 

models (Appendix 1).

Environmental envelope and process based models are able to analyze individual species and higher levels of organization but do not necessarily provide 

estimates of abundance (as compared to adaptive capacity models). Succession or 

Forest gap models are able to provide estimates of abundance but primarily focus 

on changes to species assemblages for a particular landscape. Integrated models 

represent an expanding diversity of models that incorporate elements of the other 

three categories of models and sometimes include adaptive capacity. These methods differ in their use of temporal variability: where bioclimate envelopes tend to 

estimate future suitable habitat or conditions at predefined points in time, processbased models are often able incorporate and output rates of change or generate 

estimates of change over time.

There are a number of assumptions common to both environmental envelope 

and process-based models (from Hansen and others 2001 and others):

• Climate-species relationships will remain constant over time.

• Both correlative (climate envelope) and more mechanistic models tend to ignore 

dispersal and species interactions (McMahon and others 2011). Dispersal or 

tracking to new suitable climates will be unhindered by other factors. In reality, 

communities may experience barriers and transitions may lag where long-lived 

species persist many decades.

• Many modeling techniques use mean temperature or precipitation values, which 

assumes that changes in averages over time will be as or more important than 

changes in extreme weather events and short-term variability patterns (Gonzales 

and others 2010). Depending upon the organisms studied, this may not be a 

valid assumption.

• Many studies do not consider the timing or seasonality of changes, which may 

be more important than averages.

Environmental envelope models

The first group of distribution models, environmental envelope models, encompasses a variety of different statistical approaches to describe the relationship 

between species/biome/habitat presence and climate and, sometimes, other variables (Box 3.1; Appendix 1). This family of models is based on species niche 

theory, which views the niche as an evolved characteristic comprising the total 

range of multiple factors that determine where a species can maintain a positive 

population growth rate over time (Hutchinson 1957; Chase and Liebold 2003; 

Colwell and Rangel 2009). An example is bioclimate envelope (BCE) models, 

which are applied to both plant and animal species. A large proportion of studies 

used in vulnerability assessments incorporate BCE models to predict the effect of 

climate change on species distributions (Parmesan and Yohe 2003; Pearson and 

others 2003; Rehfeldt and others 2006; Chen and others 2011; Notaro and others 2012). Estimates of the degree or magnitude of shifts in distribution are used 

to identify species or habitats that are likely to experience the greatest negative 
USDA Forest Service RMRS-GTR-309. 2013. 27

impact due to projected climate effects. Because these analyses focus around climate mediated patterns of distribution, they are best used to predict changes in 

distribution over large scales where climate is a primary driver. However, within 

this, the physical range can be quite flexible (see Appendix 1; Table 3.2)

There are a number of advantages and disadvantages of BCE models for estimating response to climate change. BCE models are useful in that they can provide 

data on species presence or absence with relatively general input data (e.g., Notaro 

and others 2012; Table 3.2). However, BCE models are less effective for predicting 

species extinctions because they do not incorporate measures of actual population 

dynamics (Buckley 2008) or adaptive potential. They also cannot reliably predict 

Box 3.1. Statistical and non-statistical approaches for estimating the relationship between climate and species, habitats, or 

biodiversity.

Box 3.1. Statistical and non-statistical approaches for estimating the relationship between 

climate and species, habitats, or biodiversity.

• Regression Methods (General Linear Models (GLM), regression trees). Describes the influence of 

numerous predictors (climate conditions) on a variable of interest (presence, mortality). Results often 

form the basis of maps that represent spatial influence of predictors. Example: DISTRIB (Iverson and 

Prasad 1998 or Iverson and others 1999a). These models project distribution of tree species and 

aggregate results to present changes in community type. Projections are modeled from the current 

relationships between tree presence and climate and soil characteristics. Physiological data are not 

required, which allows for its application to a large number of species. However, these models are not 

able to integrate the effects of competition, CO2, or dispersal (though Iverson and others [1999] have 

developed a model that considers dispersal ability) and establishment capacities. Like many such 

approaches, these models also assume that the relationship between environment and species will 

remain constant over time.

o Bagging Trees—Similar to above but uses resampling technique to reduce error. Species that 

are not well described by predictors will show large variation in output trees, indicating 

uncertainty.

o Multivariate Adaptive Regression—Splines are used for analysis of continuous variables, 

which are effectively partitioned into intervals. Example: Rehfeldt and others 1999.

o Mahalanobis Distance (MD)—Multivariate technique. Defines perpendicular major and minor 

axes and then calculates distance from centroid in n-dimensional space based on covariance of 

axes lengths. Allows user to consider relative influence of predictor variables. Example: 

Bradley 2009.

o Random Forest—Classification system that produces robust estimates of species presence. 

Used in Rehfeldt and others 2006.

o Multiple regression models—Example: Energy theory (Biodiversity). Currie (2001) related 

biodiversity of a broad spectrum of organisms to spatial patterns of summer and winter 

precipitation and temperatures using multiple regression. Methods assume that the relationship 

between biodiversity and climate will remain constant. 

• Machine learning methods (non-statistical) (Artificial Neural Networks, genetic algorithms [GA], 

GARP, classification analysis and decision tree [CART])—A group of methods that use algorithms 

to allow computers to “learn” from experience and improve performance over time. Output is generally 

in the form of if-then and do not rely on assumptions of relationships between the environment and 

species suitability. An example is presented in Termansen and others 2006. 

• Cellular Automata—Used to predict succession, spread of invasive species, plant migration, and, in 

MigClim, species distribution. This analysis predicts the status of cells over time according to rules 

about influences of adjacent cells.
28 USDA Forest Service RMRS-GTR-309. 2013.

shifts in species distribution at small spatial scales. However, new methods are 

continually being developed to incorporate factors such as dispersal (e.g., Iverson 

and others 2008; Cushman and others 2010). There may also be problems where 

models assume species’ realized niche, the niche space that a species occupies 

when other factors such as competition and ability to reach suitable habitat are 

taken into account, can be adequately described by climate variables (Colwell and 

Rangel 2009). Similarly, BCE models assume species are in equilibrium with their 

environment, which may limit the capacity of some models to generate realistic 

predictions of future range shifts. Further, analyses may assume that current locations of species represent environmental conditions suitable for all life history 

stages (seedlings and adult trees for instance), which, if incorrect, may overestimate suitability when used to predict future site potential (Falk, in press). Many of 

these issues are being addressed by combining results generated from BCE models 

with other methods that can measure non-climate impacts (see Appendix 1 for 

examples). Indeed, one advantage of BCE models is that they appear to be readily 

integrated into synthetic approaches for estimating species response (e.g., Iverson 

and others 2004, 2008; Davison and others 2012).

By combining projections for several species, BCE analyses are also able to 

generate data regarding biodiversity. In some instances, BCE analysis has been 

applied directly to estimates of species richness (e.g., Currie 2001). However, relationships between species richness and temperature and precipitation tend to be 

nonlinear, and, given that predicted changes often fall outside of observed ranges, 

extrapolation of richness patterns or other diversity measures may not accurately 

predict future change (Currie 2001). Still, BCE models provide a powerful tool 

for estimating future loss or potential degree of changing in suitable habitats 

(Heikkinen and others 2006). Estimating the impact of global change on aggregate 

indices of biodiversity through analysis with BCE models has many applications 

for large scale assessments of climate impacts (Currie 2001; Notaro and others 

2012). Estimating biodiversity or species richness is particularly useful for identifying hotspots of vulnerability and potentially important areas for prioritized 

conservation action. Where information is limited regarding factors that influence 

species range, BCE models remain a good way to estimate climate change effects 

on biodiversity and may provide more precise estimates of future effects than species assemblage models (Heikkinen and others 2006).

Process-based or simulation models

The second group of models includes process-based or simulation models, 

which simulate ecosystem processes by creating mathematical representations 

of biological and physical processes. A large number of process-based or simulation type models focus on vegetation, which is often defined by a type or by 

dominant life forms. Thus, output of process-based analyses is commonly generated at higher levels of organization like biome or ecosystem. Since these types of 

models examine a much more fundamental aspect of ecosystems, they tend to be 

more responsive to environmental changes such as climate (Swantson and others 

2011). These models may also incorporate important elements that have influence 

at smaller scales. These models range in complexity from biogeographic (equilibrium) model that require only long-term average climate and use biogeographic 

rules to determine vegetation type and density, to the more complicated successional models that simulate dynamic changes based on biogeochemical processes. 

Biogeochemical models are valuable because they incorporate dynamic climate 

variables as well as a high degree of interaction among variables to simulate important processes over time and space (Feenstra 2009). They can provide results 
USDA Forest Service RMRS-GTR-309. 2013. 29

relevant to biodiversity-focused assessments, including the functional and structure characteristics of the modeled community. However, biogeochemical models 

do not directly determine what vegetation is likely to exist at a given location.

Dynamic global vegetation models (DGVMs) combine biogeographic and 

biogeochemical model methods to model the response of broader plant functional 

groups or community types (Bachelet and others 2001; Notaro and others 2012; 

Table 3.1, Appendix 1). DGVMs can incorporate disturbance, ecophysiological 

processes, soil and litter dynamics, and hydrologic variables to simulate vegetation 

response. Vegetation response is based upon the dynamic feedback of biochemical processes on vegetation distribution (Peng 2000) Importantly, DGVMs also 

include carbon fertilization effects, which might reduce the impact of warming 

on plant distribution change and is an issue not addressed by BCE models. Many 

DGVMs also model carbon cycling (sinks, sources, and fluxes), an important element in climate-biosystem feedbacks (Moritz and others 2012). Though they do 

not estimate distributions of individual species, DGVMs can incorporate competitive effects by simulating changes in cover (biomass and NPP) and distribution of 

plant functional types.

Successional models (e.g., JABOWA, Forest Gap Models) are a class of process based models (also called dynamic biogeographical model, Peng 2000) used 

for studying communities by simulating the influence of growth, ecophysiology, 

disturbance, and species interactions on composition, biomass, and structure over 

time (Tables 3.1, 3.2). Forest Gap models are typically individual based models, 

i.e., they simulate population and community dynamics based on the modeled response of individual organisms (e.g., trees or shrubs). Species distributions are 

determined at larger scales in modeling by the dominant influence of climate and 

at smaller scales by a variety of influences. Unlike other model methods, this group 

is able to output estimates of plant cover, composition, and relative abundance at 

relatively fine spatial scales (e.g., individual trees and stands). Like other models, 

successional models tend to focus on plant communities, although these results can 

be fed into habitat suitability models for animals and other biota.

An equivalent approach on the animal side is the ecophysical model, which 

estimates potential activity hours and food requirements to meet certain energetic 

costs (Buckley 2008). When data are available, these can be coupled with population dynamic models to create dynamic bioenergetic models (see adaptive capacity 

model discussion) from which species range can be predicted (e.g., Buckley 2008). 

However, these models, as with most process based studies, are only as good as the 

empirical data upon which they are based and are often computationally intense.

Integrated modeling methods

Analysis, especially those that attempt to inform multi-scale perspectives, must 

take into account both large-scale, climate-driven patterns in biodiversity as well 

as more local-scale processes relating to species interactions and disturbance regimes. In recognition of this and in an attempt to address shortcomings in species 

distribution models, a number of efforts have combined methods to improve the 

accuracy of predictions as well as generate more biologically meaningful data. 

This group, referred to here as integrated modeling methods, represents a growing body of analyses methods used to inform vulnerability or impact assessments 

(Table 3.1; Appendix 1). Some examples are:

• Buckley (2008) linked population dynamics and energetics/biophysical models to estimate population growth as a function of activity hours. The authors 

then integrate individual energetic and population dynamic models with spatial 
30 USDA Forest Service RMRS-GTR-309. 2013.

environmental data to predict distribution and abundance of vertebrate and plant 

species.

• MigClim (Engler and Guisan 2009; Appendix 1) is a recently developed program that couples predictive distribution maps, representing a species’ habitat 

suitability as a function of evolving climate, with a cellular automaton that 

simulates adaptive capacity relating to dispersal, colonization, growth, and extinction of the species in the landscape under differing conditions. This model 

runs in ArcGIS to simulate plant dispersal under climate change and landscape 

fragmentation scenarios and, used in conjunction with envelope type analysis 

(requires maps of current and future suitable habitat distributions), gives a spatially explicit prediction that can be used to assess species vulnerability.

• Iverson and others (2008) combined bioclimate distribution analyses with a 

model for species dispersal and a qualitative system for identifying the relative 

influence of species traits (modification factors or MODFAC) that accounts for 

climate and non-climate-related response variables for tree species in the eastern United States. This analysis produces an estimate of species ranges under 

future conditions.

• Barrio and others (2006) described a process that integrates four models in a 

scale-dependent hierarchical framework to study the impacts of climate and 

land use change scenarios on species’ distributions at fine resolutions. This 

method combines two distribution models: SPECIES, a continental scale bioclimatic envelope model and downscaled SPECIES, a regional-scale bioclimate 

and land use suitability model; a dispersal model; and ALCOR, a connectivity 

model. SPECIES is a model that employs an artificial neural network (ANN) 

to define BCE based on inputs generated through a climate-hydrological linked 

process model. Applied to systems in the United Kingdom and Spain, this 

analysis was able to attribute specific patterns of landscape change to climate 

change. Specifically, increased fragmentation in landscapes derived from the 

development of gaps within existing habitat structure rather than shifts in habitat zonal types.

• LANDFIRE uses spatially explicit modeling of dynamic interactions between 

vegetation, climate and disturbance to create probabilities regarding future conditions. These probabilities represent departure from preset reference conditions 

(see Chapter 4.1) and vulnerability is represented as future departure from norm 

(e.g., Baker 1989; Keane and others 2006; Mladenoff 2004; used in LandFire 

Project to compute FRCC).

• FireBGCv2 is a simulation modeling platform that merges a Gap model 

(FIRESUM); a mechanistic vegetation succession model (BIOME-BGC); a spatially explicit fire model (FARSITE) incorporating ignition, spread, and effects 

on ecosystem components; and a detailed fuel treatment module (FIRESUM, 

Keane and others 2011). FireBGCv2 dynamically simulates synergistic and interacting effects of weather and climatology, vegetation growth and succession, 

disturbance (e.g., wildfire and bark beetles), and land management (e.g., prescribed fire and thinning) on landscape structure and ecosystem processes.

3.2.4 Prioritization Methods

Indices

Indices are generally suitable for questions that explore biological diversity in the 

aggregate since they provide a ranking or relative measure of vulnerability among a 

group of species (Feenstra and others 1998) or in cases where multiple datasets are 
USDA Forest Service RMRS-GTR-309. 2013. 31 Table 3.3. Examples of methods used to provide relative measures of vulnerability or identify areas of vulnerability. Type refers to the method employed. Quantifications typically tally traits associated with vulnerability. Models typically combine some type of tallying method and extrapolate it to spatially explicit issues (e.g., landscape fragmentation). Name Scale Type Description Target Available from NatureServe’s Global-fixed; Quantification CCVI consists of ranking system that considers Animal and www.natureserve.org/prodServices/ Climate Change range of species spatial estimates of exposure and species traits plant species climatechange/ClimateChange.jsp Vulnerability Index or habitats indicative of sensitivity and adaptive capacity and habitats for species (CCVI) and categorizes species according to their level and habitats (HCCVI) of vulnerability. HCCVI uses results of CCVI as well as estimates of habitat change, including disturbance effects, to rank habitat vulnerability. System for Assessing Flexible Quantification Scoring system based upon 22 traits predictive Terrestrial http://www.fs.fed.us/rm/grassland Vulnerability of of species vulnerability to climate change. vertebrate shrubland-desert/products/species Species (SAVS) Produces value used to compare relative species vulnerability/ vulnerability among species. Environmental Flexible Quantification Framework for categorizing the relative Threatened and EPA/600/R-09/01 Protection Agency vulnerability of threatened and endangered endangered Framework species to climate change. Forms a matrix that species simultaneously considers species vulnerability to ongoing population declines and future negative impacts from climate change. Aims to focus management actions. Water Supply Stress Flexible Quantification Models local watershed stress by comparing Water http://www.fs.fed.us/ccrc/tools/wassi. Index Model(WaSSI) water supply and demand in a particular area. shtml Vulnerability Surface Flexible Model Uses a three-dimensional analytical surface to Animals Luers 2005; Luers and others 2003 determine relative vulnerability. Applicable to variety of systems. Amendable to targets that are not easily described by mathematics. Forest Tree Genetic Flexible Model/ Creates a vulnerability index from four different Plants Potter and Crane 2010 Risk Assessment Quantification measures of future expected change in species System (ForGRAS) distribution. Rates species according to intrinsic attributes and external threats that influence species vulnerability to climate change.
32 USDA Forest Service RMRS-GTR-309. 2013.

combined (Sullivan and Meigh 2005). Indices are often applied at scales ranging 

from management unit to regional or subregional levels (Table 3.3). Alternatively, 

an index might be applied across a species’ range (e.g., NatureServe). These methods are relatively easy to compute and are flexible to a diversity of input type 

but may reflect more generalized relationships at the cost of accurate predictions 

(Table 3.2). Variables used in such indices are often given equal weight, which 

can provide misleading results where individual species have particular overriding 

influences. Still, indices, unlike many of the mathematical modeling approaches 

discussed earlier, can be constructed to measure aspects of vulnerability directly 

and can be used alone to generate climate change vulnerability assessments (e.g., 

Coe and others 2012; Bagne and Finch 2012). Two primary approaches are used 

to generate scores: Quantification of traits considered important predictors of species sensitivity and adaptive capacity (e.g., SAVS, NatureServe’s Climate Change 

Vulnerability Index, and NCCVI) and methods that model vulnerability based 

upon predetermined schemes that categorize relative impact and response (e.g., 

Luers 2005).

Many of the systems listed in Table 3.3 were developed in direct response to 

the recognition of the need for specific measures of species and ecosystem vulnerability to climate change. As such, they are tailored to help managers and 

conservationists identify critical elements, whether species or species trait, most 

vulnerable to future, expected conditions. The specific nature of these systems, 

which relates directly to the goal of understanding climate change impacts, lends 

them considerable weight for addressing vulnerability assessment needs. In addition, many of these scoring systems use data generated from distribution models or 

otherwise build upon data regarding future expected conditions. Therefore, indices 

represent a synthetic product based on data from a variety of sources. However, as 

such, many of these systems are only as good as the data used to generate a score, 

and a well-developed application of a scoring system can be quite labor intensive. 

In addition, though very good at focusing the user’s attention toward climate impacts, these tools often do not incorporate other interacting stressors and require 

additional and separate analyses to create comprehensive management strategies. 

Future developments are likely to integrate additional methods into the scoring 

process to create new approaches that can simultaneously consider multiple stressors as well as distinguish vulnerability across a landscape.

Frameworks

Frameworks (Table 3.4) are similar to indices in that they provide a relative 

measure of vulnerability. However, they are often focused on targets that are not 

easily described by mathematical relationships and commonly have a socio-economic focus. Where indices use scores to quantify vulnerability, frameworks are 

generally based around the identification of targets and hazards and employ an 

algorithm to relate vulnerability to sensitivity and exposure divided by conditions 

that ameliorate negative impacts on the target (exceeds threshold, e.g., Leurs 2005; 

adaptive capacity, e.g., Fontaine and Steinemann 2009). Targets and hazards are 

often defined by consensus of opinions or responses generated by interviews or 

facilitated workshops. As such, frameworks are not necessarily a method for measuring vulnerability so much as a tool to identify vulnerabilities within a system 

of interest.

Fontaine and Steinemann (2009) presented a simple framework and algorithm 

to assess vulnerability for social sectors that could be applied to natural systems 

as well. In their system, each element, exposure, sensitivity, and adaptive capacity, is considered on a 1-5 Likert scale (psychometric commonly used in research 
USDA Forest Service RMRS-GTR-309. 2013. 33 Table 3.4. Examples of frameworks that can be used to create or organize climate change vulnerability assessments. Name/source Description Target Available from: Czúcz and others 2009 Framework consists of adaptive capacity indicators that account for Natural resources Czúcz and others 2009 primary coping options or mechanisms. These indicators are quantified using landscape indices based on quality and distribution of habitat patches, exposure estimates based on GCM outputs under various conditions, and estimates of sensitivity (four categories or types are identified). Produces climate vulnerability maps. Ford and Smit 2004 A framework for assessing the vulnerability of communities in the Canadian Arctic Ford and Smit 2004 Canadian Artic to risks associated with climate change. Region Environmental risk Though not explicitly a vulnerability assessment, this framework U.S. http://www.epa.gov/ncea/global/approaches.htm assessment/ addresses sensitivity, exposure, and adaptive capacity components. management framework Uses risk analysis methods identified in IPCC technical guidelines to for climate change assess climate impacts and adaptations within a framework that impact assessments incorporates stakeholders. Identifies thresholds that are then used for assessment. Vulnerability Assessment Acquires data from stakeholders (experts) to assess hazard and causes Social sector but Fontaine and Steinemann 2009 Method (VAM) of vulnerability by quantifying through a simple algorithm level of other applications effect of future natural hazard on exposure, sensitivity, and adaptive possible capacity. A multidisciplinary, Framework for combining several types and versions of assessments Ecosystems and Metzger and others 2005 multi-scale framework regarding problem target. Based upon comprehensive algorithm ecosystem services for assessing vulnerabil- framework that combines elements of traditional climate change ities to global change vulnerability assessments, with adaptation and impact assessments. (ATEAM project) Also has elements of risk assessment (risk of alternate scenario). Combines ecosystem, economic, and social sectors. EPA’s Regional Vulner- Designed to develop and demonstrate approaches that address Natural resources Smith and others 2005 ability Assessment phases of ecological risk assessment that focus on integrating and (ReVA) Program synthesizing information on the spatial patterns of multiple exposure to allow a comparison and prioritization of risks. Combines elements of vulnerability and risk assessments. Assessing Climate Created climate change scenarios based on the ATEAM project. Natural resources Roundsevell and others 2006 Change Effects on Land Integrates land use data, species distribution, and habitat Use and Ecosystems: fragmentation within a common scenario framework to synthesize from Regional Analysis impacts for different global change problems. to the European Scale (ACCELERATES)
34 USDA Forest Service RMRS-GTR-309. 2013.

questionnaires) representing very low to extreme likelihood of effect. In a case 

study of drought in Washington State, this scale corresponds to the frequency and 

severity of drought, where, for instance, the likelihood of frequent droughts could 

be considered very low, low, moderate, high, or extreme. Fontaine and Steinemann 

(2009) used stakeholder (various water users) interviews to populate their dataset. 

Vulnerability was calculated according to well recognized equation, V = (E+S)/A 

(IPCC 2007), where exposure and sensitivity are additive effects leading to vulnerability that is reduced to a lesser or greater degree by adaptive capacity. In 

this way, Fontaine and Steinmann use knowledge and values of stakeholders to 

measure vulnerability. Importantly, though this type of method may rely on human 

interpretation, it is able to quantify and integrate all three elements of vulnerability. 

To apply this to a natural setting, expert interviews might be substituted for stakeholder provided information.

A more biologically oriented system was presented by Czúcz and others (2009) 

who developed a framework that consists of adaptive capacity indicators that account for primary coping options or mechanisms. These indicators are quantified 

using landscape indices based on quality and distribution of habitat patches, exposure estimates based on GCM outputs under various conditions, and estimates 

of sensitivity (four categories or types are identified). Ultimately, this process produces climate vulnerability maps.

3.3 Issues in Predicting Response

It is critical to understand how response measures relate to vulnerability to best 

identify strategies that prepare for and cope with climate change (see Chapter 1, 

Figure 1.2). As part of the assessment process, we must understand how well measures of response represent the range of potential effects on species, habitats, and 

ecosystems. However, the complexity of biological systems prevents a perfect 

representation by any one or combination of methods. Only through a careful consideration of biological complexities can we identify strengths and weaknesses in 

various approaches as well as provide insight into best management practices.

3.3.1 Critical Thresholds, Ecosystem Tipping Points, and Other 

Considerations

As discussed in section 3.2, many studies predict changes in species distributions in response to changing climate (e.g., Rehfeldt and others 2006; Notaro and 

others 2012). At local scales, community composition reflects changes in the suitability of existing habitat for species persistence. Both modeling and empirical 

studies suggest that such changes due to climate alone are likely to be expressed 

at multi-annual to decadal time scales (Allen and Breshears 1998; Parmesan and 

Yohe 2003; Chen and others 2011). In contrast, severe, large-scale disturbances 

can reorganize ecosystems mass and energy on much shorter time scales of days 

to months (Overpeck and others 1990; Falk, in press). In many cases it may be 

combinations of climate change and severe disturbance that is most likely to trigger abrupt ecosystem transitions into novel configurations, rather than either factor 

acting separately.

Following major disturbances that occur during periods of climate stress, ecosystems may recover not into the pre-disturbance conditions but into novel community 

types. For example, conversion of pine-dominated communities to dominance by 

Gambel oak (Quercus gambellii), New Mexico locust (Robinia Neomexicana), 
USDA Forest Service RMRS-GTR-309. 2013. 35

and interior chaparral species is frequently observed in the Southwest following 

major fires (Savage and Mast 2005). These new configurations can be resilient in 

their new state and resistant to return to pre-disturbance conditions (Kitzberger 

and others 2011). In addition to ecological effects, these transitions also include 

potentially persistent alterations to geomorphic, soil, hydrological, and biogeochemical systems (Scheffer and others 2001). Such abrupt transitions are predicted 

to become more common under conditions of altered future climate and amplified disturbance regimes (Flannigan and others 2000; Westerling and others 2006; 

Zinck and others 2011).

Abrupt ecological change is generally defined as a threshold response in key 

biotic and abiotic parameters and ecosystem components (Anderson and others 

2008). Such changes may be expressed in shifts in dominant vegetation physiognomy (for example, from forest to shrub communities), especially if established 

individuals of dominant species do not survive a disturbance event. Altered soil, 

hydrologic, and biogeochemical conditions can reinforce shifts in vegetation types 

and disturbance regimes, such as a transition from a low-severity forest surface 

fire regime to a high-severity chaparral shrubland regime (Mayer 2011). Climate 

provides the envelope within which these dynamics occur; disturbance provides 

the trigger for abrupt system reorganization.

A variety of mechanisms may contribute to rapid or even abrupt post-fire ecosystem change under current and near-term climate. First, by definition, severe 

disturbances such as wildfires leave relatively few surviving established individuals, opening up niche and physical space for community level turnover. This 

mechanism operates by differential mortality among species, which may relate 

in turn to life histories adapted to low- or high-severity fire respectively. Second, 

early post-fire successional pathways may produce different post-fire communities 

by processes of community assembly, including dispersal distances, order of arrival (or, in the case of sprouting species, vegetative response), tolerance of harsh 

post-fire physical conditions, competitive ability, and other factors. Third, even 

where pre-fire community members are present, altered climate conditions may 

not include climate space suitable for regeneration of previously dominant species, 

even when these species were able to persist pre-fire as established individuals 

(Breshears and others 2005). The recruitment niche for many Southwestern species is concentrated in the cooler, wetter region of the species tolerance space as 

established adults, and during extended drought periods these climate conditions 

may not occur during recruitment season (Notaro and others 2012). Finally, altered 

landscape structure (such as anomalously large high-severity patches) following 

extreme disturbance events (such as anomalously large high-severity patches) may 

restrict establishment where distance to seed source exceeds the scale of propagule 

dispersal of previously dominant species, while favoring other species with rapid 

long-range dispersal.

The collective effect of these disturbance-climate interactions can be to rapidly 

and persistently change landscape patterns and processes including disturbance regimes, vegetation composition, carbon dynamics, and hydrologic balance (Lentile 

and others 2007). At broad spatial scales these changes may constitute a feedback 

mechanism to the climate system through changes in surface albedo and vegetation productivity. Together with an altered climate envelope, these mechanisms 

may limit recruitment of previously dominant species while favoring establishment of a different suite of species better adapted to new conditions (Bachelet and 

others 2001; Loehman and others 2011). Once established, the new community 

may support novel disturbance regimes that then further exclude prior dominants.
36 USDA Forest Service RMRS-GTR-309. 2013.

3.3.2 Interactive Effects

No species exists in complete isolation from others. These interactions can be 

adverse to a given organism (e.g., competitive exclusion, resource competition, 

disease interactions, and parasitism), leading to reduced population growth rates 

or local abundance. Species interactions may also be beneficial or even essential, 

as in the case of mycorrhizal associations between plants and fungi, or dispersal 

and pollination agents for plants. Thus, a complete assessment of species climate 

vulnerability needs to include the sensitivity and exposure to alterations in these 

critical species interactions.

An example of climate-driven vulnerability that is amplified by species interactions involves changes in the phenology (seasonal timing) of plants and their 

pollinators. Abundant evidence has been compiled of changes in phenology as 

one of the most pervasive ecological responses to climate change (Walther and 

others 2002; Weltzin and others 2001; Parmesan 2006).This includes the timing of 

migrational movements as well as timing of seasonal life cycles. Plants and their 

herbivores (e.g., insect larvae who use them as food plants) and pollinators rely on 

being present at the correct relative time in the annual cycle; for example, plants 

that produce nectar must be in flower to provide the energy source required by hummingbirds and for insects such as bees that gather pollen. Conversely, plant species 

that rely on particular agents for cross pollination must be ready with mature flowers when those agents are present. Several cases have now been documented of 

amplified vulnerability of a target species due to climate-driven asynchrony with 

essential symbionts or mutualists (e.g., Parmesan and others 2003).

A different form of interaction is the amplification of negative impacts of climate change with other ecological processes or agents. For example, in many 

forests throughout western North America, climate change is interacting with fire 

and biotic responses such as defoliating insects or bark beetles, in some cases leading to accelerated local extirpation of species (Smithwick 2008; Hicke and Logan 

2009). Betancourt and Swetnam (1993) documented the accelerated decline of 

pinyon (Pinus edulis)in New Mexico during a severe drought in the 1950s; while 

drought was the overall driver of mortality, bark beetles (Ips and Dendroctonus

spp.) were probably the immediate agents of mortality in many areas. Similar 

die-offs have been observed in other areas (Allen and Breshears 1998). Extensive 

mortality of Engelmann spruce (Picea engelmanii) has been documented in the 

Pinaleño Mountains of southeastern Arizona, the ecologically tallest of the Sky 

Figure 3.3. From IPCC (2007: 2-2). Range of 

major uncertainties that are typical in impact 

assessments, showing the “uncertainty 

explosion” as these ranges are multiplied 

to encompass a comprehensive range of 

future consequences, including physical, 

economic, social, and political impacts and 

policy responses (modified after Jones 2000, 

and “cascading pyramid of uncertainties” in 

Schneider 1983).
USDA Forest Service RMRS-GTR-309. 2013. 37

Islands, due to interactions of climate stress, insect outbreaks, and a series of highseverity fires (O’Connor and others 2011). Such interactions of stressors would 

predict enhanced vulnerability of many species when compared to the effects of 

climate change alone.

3.3.3 Uncertainty

Models can never perfectly describe the real world and the future can never be 

known. Thus vulnerability assessments reflect a degree of uncertainty (Fig. 3.3). 

The key for dealing with uncertainty is to recognize its sources, amplitude, and 

significance. We have highlighted a number of sources relating to unknown processes within the biological community, but we also need to highlight technical 

sources of uncertainty. For example, the variability among projections generated 

by different GCMs can be reduced, or at least bracketed, by using outputs from 

multiple models (Notaro and others 2012; Quijada-Mascarenas, in review). In one 

approach, model projections are weighted along a performance scale based upon 

estimates of past climate conditions (Notaro and others 2012). Variation among 

GCMs for a given emission scenario (the amount of greenhouse gases, or GHGs, 

including CO2 and CH4) tends to be small relative to the uncertainty associated 

with projecting future GHG emissions (Nakicenovic and others 2000 in IPCC 

2007). Future emissions will be affected by a wide variety of variables globally, 

including human population growth, regulations, and energy development, and 

thus are influenced strongly by political and economic forces as well as the degree 

of social consensus about reducing GHG emissions. Consequently, a best-practice 

approach for vulnerability assessments has traditionally been to include results under a range of possible emissions scenarios. However, recent studies show that the 

trajectory of GHG output since emissions scenarios began to be used has consistently exceeded the highest scenarios used by the IPCC, and recorded temperatures 

are correspondingly at the high end of the range of projections (Smith and others 

2009). Vulnerability assessments may be best served when based upon projections 

of models run under the higher range of emissions.

There is also inherent uncertainty in estimates of vulnerability that rely on incomplete knowledge. Management decisions are limited by available information 

and the feasibility of measuring vulnerability of the target of interest. For example, 

experimental observations can be made for sedentary organisms at a small scale 

(e.g., plants in a patch of prairie) but are less feasible for highly mobile organisms or landscape scale processes, thus, uncertainty is a function of the selected 

assessment targets. As discussed in previous sections, ecological thresholds and interactive effects are often undefined but likely to have strong influences on species 

or community response to climate change. Scenario planning is a common approach to coping with uncertainty in climate models where impacts on targets and 

their vulnerability are assessed across a range of possible climate futures so that robust management options can be identified (Peterson and others 2003). Similarly, 

multiple response measures (Chapter 4) may be used to estimate vulnerability for 

a target and identify common sources of sensitivity and exposure (e.g., VEMAP, 

Notaro and others 2012).
38 USDA Forest Service RMRS-GTR-309. 2013.

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USDA Forest Service RMRS-GTR-309. 2013. 43

Chapter 4. Translating Response into Vulnerability and 

Management Recommendations

Chapter 4 Talking Points

(1) Vulnerability is implicated from a variety of measures that have different 

assumptions.

(2) The vulnerability measure(s) used will determine applicability to specific 

management problems.

(3) All assessments contain uncertainty but that need not limit their ability to inform 

management.

Vulnerability or the susceptibility of a resource to negative impact is the key 

measure of a vulnerability assessment. It helps us to interpret climate change response for management purposes. In some cases, vulnerability may be equivalent 

to prediction of response or observed effects, but in others, further measurement 

and assumptions will be needed to translate expected response into vulnerability. 

Because management goals and negative impacts are shaped by human perceptions, climate change response needs to be translated into a measure relevant to 

contemporary society; this is often achieved by examining response in relationship 

to something else (e.g., historic baseline, response of other targets, or threshold of 

suitability). For example, consider models of future tree distribution (Iverson and 

others 2004, 2008). Vulnerability is not the distribution of a given species at a given time, but how different that projected distribution is from the distribution where 

the species is currently managed. The distance or amount of overlap between distributions provides a measure of the degree of negative impact or vulnerability and 

can be used to compare multiple tree species to give a sense of relative vulnerability. A wide variety of measures, both direct and inferred, have been used to 

estimate vulnerability of a given target to climate change. Assessments can use 

a single measure of vulnerability, combine several measures, or record identified 

vulnerabilities in a synthesis. Targets, scope, and scale will affect how vulnerability is quantified (Chapter 2).

4.1 Measures of Vulnerability

The measure of vulnerability is important to understanding assessment conclusions and possible limitations to applying the results. Six common measures of 

vulnerability to climate change used in natural resource management are outlined 

below (Box 4.1).

4.1.1 Degree of Recent Change From Historic

Because global and regional climates have been warming for several decades, 

vulnerability can be measured from recent departure of targets to known background variability (Fig. 4.1). Background or historic levels can be taken from 
44 USDA Forest Service RMRS-GTR-309. 2013.

Box 4.1. Summary of vulnerability measures, common applications, and considerations for use.

Vulnerability measure Considerations

Observed degree of recent change No models needed

Depends on sensitivity of target

Assumes continuation of observed trends

Modeled departure from baseline Greater response = greater vulnerability

Current distributions may not fully represent sensitivity and 

adaptive capacity

Inclusion of all key variables

Observations from experiments or past events Studied conditions correspond to future conditions

Experiments can only manipulate a few variables

Limited data available for the past

Proximity to thresholds Thresholds are difficult to set

Estimated adaptive capacity Needs to be integrated with exposure and sensitivity

Unknown relationships between adaptive capacity and response

Relative importance of modeled factors Accuracy of relationships among model variables

Inclusion of all key variables

Figure 4.1. Observed 

response of 

terrestrial wildlife 

to climate change 

effects from 189 

studies reviewed 

by Joyce and 

others (2008). 

These patterns 

can be translated 

into information 

that identifies 

which species 

might be most 

affected by future 

climate conditions. 

Adapted from Joyce 

and others (2008).
USDA Forest Service RMRS-GTR-309. 2013. 45

various sources and time periods as a baseline for comparison. This method is 

commonly used to assess vulnerability of individual species to climate change 

but has also been applied to communities. For example, phenological or timing 

responses to climate change are often based on recent observations, such as the 

difference in date over the past decade for initial spring bud burst or the arrival 

date of a migratory bird species. A study of recent shifts in bird migration timing 

in the fall found differences in the degree of timing change (i.e., vulnerability) by 

species, depending on migration distance and variability in annual brood number 

(Jenni and Kery 2003). Spatial measures can also be accommodated, such as the 

change in distance from the historic center of a species range to the current center. 

This is akin to methods that use historic conditions (such as the natural range of 

variability, see case study in Chapter 2) as a benchmark or reference to examine the 

degree of change in a system. The assumption of this group of approaches is that 

greater change is indicative of greater vulnerability.

Recent change is an appropriate measure for targets that are particularly sensitive to climate variables allowing for a measurable effect, less so for targets that 

are resistant at current levels of change. Because the degree of change is measured 

directly, interactions and multiple contributing factors to response are included, 

but this also makes it difficult to pinpoint the primary causes for the observed 

changes. This approach does not require projecting into the future but assumes that 

the degree of vulnerability in the future, and similarly the rate of climate change, 

will follow the observed trend, which is uncertain considering that climate effects 

and interactions are unlikely to be linear.

4.1.2 Degree of Modeled Future Change From a Baseline

The magnitude of departure of future projections of climate, species distributions, biodiversity, and other measures from baseline observations is a common 

measure of vulnerability (Figs. 4.2, 4.3). Modeling approaches are commonly used 

for the future projections so targets are limited to those that can be modeled or that 

can be correlated with modeled variables. Greater divergence between the baseline 

and the projected future conditions is equated with greater exposure and increasing 

vulnerability. This measure of vulnerability is useful for large-scale targets such as 

ecosystems or regions. For smaller scales, a correlative approach is often taken. 

Distribution models, a common approach, correlate species presence with climate and environmental variables, which can then be used to project distributions 

give future climate conditions. Vulnerability is the change in distance or overlap of current climate conditions to the projected location of similar conditions. 

Alternatively, vulnerability might relate to the estimated magnitude or velocity of 

change. Distribution approaches primarily focus on the exposure aspect of vulnerability, but some efforts include other aspects of vulnerability such as the potential 

for dispersal. For example, Iverson and others (2004) predicted potential habitat 

for five tree species in eastern North America by combining a species distribution 

model with a spatial model of migration potential to estimate the probability of the 

species colonizing new potential habitat; species with lower colonization potential 

would be more vulnerable to climate change.

Accuracy of these measures of vulnerability depends on the ability of the model 

to project future conditions, and considerable discussion has evolved about the 

limitations of these models and subsequent improvements (see Chapter 3.2). 

Modeling approaches are numerous, but they can be generally categorized as modeling the future based on observation of outcomes (empirical or statistical models) 

or based on the processes that determine outcomes (mechanistic or process-based 
46 USDA Forest Service RMRS-GTR-309. 2013.

Figure 4.2. Richness of 

American tree species under 

current (A) and future (B and 

C) climate scenarios. Future 

(B) represents projections 

under increasing CO2

whereas future (C) represents 

conditions with future 

decrease in CO2. This study 

presents an estimate of future 

effects based upon current 

understood relationships 

between tree richness 

and climate variables. 

CE=Climate envelope 

richness. Adapted from 

McKenney and others 2007.
USDA Forest Service RMRS-GTR-309. 2013. 47

Figure 4.3. An example of how the degree of response (species richness) might be used to measure 

vulnerability. Here, darker colors represent areas that are expected to experience the greatest 

change and are therefore at greatest risk of negative impact, in species composition under a mid 

A1B greenhouse gas scenario. (A) represents projections for a 100 x 100-km grid and (B) represents 

200 x 200-km grid projections. Adapted from Lawler and others (2009).

models). Correlative approaches assume that the modeled climate and environmental variables rather than sensitivity or adaptive capacity are the key drivers of 

distribution and that current distributions can capture ecological limitations of the 

target (Schmitz and others 2003).

4.1.3 Observed Impacts Under Comparable Conditions

Comparable climate conditions to those projected for the future can be found 

from the paleoclimate when carbon dioxide levels were much higher, from recent extreme events, or from experimental treatments. Response of the chosen 

target can be measured or estimated under those comparable conditions to predict 

climate change response (Figs. 4.4, 4.5). Episodic events, such as drought or El 

Niño, are expected to be more frequent under a warming climate and are good 

for identifying the vulnerability of targets because these events occur frequently 

enough to observe (Fig 4.5). Vulnerability is identified by comparing the target’s 

responses to the episodic event to responses during a period outside the event or 

from a control site for an experiment. An examination of recent vegetation dieoff in the Southwest due to drought and bark beetle infestation highlighted how 

extreme events and biotic interactions can lead to rapid and large-scale change in 

the distribution of pinyon (Pinus edulis) (Breshears and others 2005). Similarly, 

paleoclimatic conditions similar to those projected can offer insights into expected 

responses. Experiments can directly measure the response of a target to climate 

change by manipulating variables such as CO2 or temperature and can even be 

conducted in the field where the target is subject to important ecological processes.

Experiments are often limited to short time periods and only a few climate 

variables can be manipulated at relatively small scales. Data available from 

paleoclimatic conditions or extreme events are also limited in terms of spatial distribution, thus limiting the range of targets and responses that can be examined 

using this approach. While the inclusion of responses to extreme events is relevant 

given their absence in most models, inference is limited in that responses are generally only examined in the short term and extreme events under future climate 
48 USDA Forest Service RMRS-GTR-309. 2013.

Figure 4.4. Observed changes in nesting 

attempts and fledglings per pair among 

four bird species in California due to 

extreme drought. Year 2002 was the 

driest year on record for the past 150 

years. This study provides an example 

of how and to what degree future 

populations might be affected by 

particular climate trends. Adapted from 

Bolger and others (2005).

Figure 4.5. Transitions between carbon (C) states: C4 to C3 (red) and C3 to C4 (blue) grass communities in 

response to increased temperatures and atmospheric pCO2 since the last glacial maximum (approximately 

20,000 years before present). Adapted from Collatz and others (1998).
USDA Forest Service RMRS-GTR-309. 2013. 49

Figure 4.6. Results of this 

study identified the 

critical thresholds that 

lead to widespread 

mortality in pine 

species in the 

Southwest. From 

Breshears and others 

(2005). Copyright 2005 

National Academy of 

Sciences, USA. 

change will likely differ from historical events in important ways such as frequency or magnitude. An assessment will also be limited if measures of vulnerability 

rely on a single climate change stressor, such as drought (Enquist and Gori 2008).

4.1.4 Exceeding Thresholds

Many studies have noted levels or states of target measures at which resilience 

of individuals or systems is exceeded and a pre-disturbance configuration will not 

return (CCSP 2009). These are called thresholds or tipping points. Proximity to 

the identified threshold or to the probability of exceeding a threshold is a measure 

of vulnerability. Thresholds can be difficult to identify, but this approach recognizes that responses and impacts will not necessarily be linear. In the previous 

section, we provided a pinyon die-off example of the threshold approach; this 

study also identified thresholds of water stress that predict tree mortality, which 

can be used in conjunction with climate projections to assess vulnerability of this 

species (Breshears and others 2005; Fig. 4.6). The threshold approach also has 
50 USDA Forest Service RMRS-GTR-309. 2013.

implications for management in that actions are targeted to a specific level of impact rather than requiring a sustained effort to reduce impacts. Because there is 

non-linear change in vulnerability over time, the time scale of the assessment and 

the uncertainty surrounding the threshold level need to be considered carefully.

4.1.5 Capacity to Adapt to Change

Adaptive capacity is a key component of vulnerability, often thought of as a 

species-specific measure, but it can also apply to individuals or systems as well 

as to the ability of the managing organization to ameliorate negative impacts 

(Fig. 4.7). Flexibility, such as behavioral or morphological plasticity, can increase 

the ability of the target to cope with changing conditions, thus increasing resilience (i.e., reducing vulnerability). Where this plasticity arises from intraspecific 

genetic variation, it may be heritable and thus provide the basis for adaptation 

to novel conditions. Examination of population dynamics and variability in the 

heritable traits that control timing of budburst in the Scottish birch revealed that 

budburst, although advancing with warmer temperatures, was unlikely to keep up 

with warming of 2 °C in the next 60 years (Billington and Pellham 1991).

Resilience of ecosystems and communities has been related to level of biodiversity or functional diversity (Naeem 2006; Petchey and Gaston 2009). Dispersal 

ability, which can confer a species` capacity to track favorable environmental conditions, is an important adaptive strategy that can be measured by mobility, rates 

of movement, and dispersal vectors. Traits may also characterize adaptive capacity where they are associated with the ability to cope with the expected effects 

of climate change. For example, species that are habitat or resource generalists 

may be at an advantage under varying conditions, because they have greater 

flexibility in habitat use than a species that specializes in a particular habitat or 

resource (Parmesan 2006). Vulnerability may be measured by the strength of a 

given capacity measure for alleviating negative impacts, the presence or absence 

of a distinguishing trait, or as the number of traits possessed that limit the target’s 

Figure 4.7. Schematic of the relationship 

between adaptive capacity and 

species vulnerability. Adaptive 

capacity affects species vulnerability 

by modulating exposure and 

sensitivity. Adapted from Engle 2011.
USDA Forest Service RMRS-GTR-309. 2013. 51

response to change. Difficulty in addressing vulnerability through traits arises because the strength and nature of the relationship between the adaptive traits and the 

associated reduction of vulnerability is generally not well known.

4.1.6 Relative Degree of Response to Climate Variation

This measure of vulnerability is commonly conducted using a sensitivity analysis and examines quantitatively how much the target measure changes when model 

parameters are varied (Fig. 4.8). These analyses start with a modeled representation 

of climate-related variables (e.g., timing of peak runoff) and the expected response 

in the target (e.g., aquifer recharge). The relative response or degree of change 

in the target is determined by varying the climate inputs across a range of values. Sensitivity to the climate variables infers vulnerability. The rangze of climate 

variables inputted is often the range produced by different emissions scenarios or 

projections from different global climate models, which is helpful in evaluating 

the uncertainty surrounding model selection and which climate parameters most 

affect the target. Keller and others (2005) examined duration of snow cover and its 

interaction with plants at high altitudes and found duration most sensitive to mean 

temperature and identified plant habitat zones subject to the greatest advancement 

in snowmelt, which may indicate greater vulnerability. Sala and others (2000) 

looked at the relative influence of various drivers on declines in biodiversity by 

region (Fig. 4.8). Potential management actions can be incorporated into models to 

evaluate their effectiveness at reducing vulnerability. A model that accurately links 

the target to climate is critical to this type of analysis.

Figure 4.8. Effect of each driver on biodiversity change for each 

terrestrial biome and freshwater ecosystem type calculated 

as the product of the expected change of each driver 

times its impact for each terrestrial biome or freshwater 

ecosystem. Values are relative to the maximum possible 

value. Bars: 1, land use; 2, climate; 3, nitrogen deposition; 

4, biotic exchange; 5, atmospheric CO2. From Sala and 

others (2000). Reprinted with permission from American 

Association for the Advancement of Science (AAAS).
52 USDA Forest Service RMRS-GTR-309. 2013.

4.2 Informing Natural Resource Management Recommendations

Vulnerability measures provide a ranking of vulnerability and, in doing so, identify priority targets or actions (Table 4.1). In their review, Peterson and others (2011) 

outlined how to translate climate vulnerability data into adaptive management 

options (Fig. 4.9). Implementation of adaptive management strategies combines 

climate data and prioritization schemes to develop and assess management actions that are most likely to achieve management goals to promote resistance and 

resilience or facilitate transition. Ranking species according to their relative vulnerability provides a means to identify priority species and potential actions that, 

implemented in the near future, might prevent further population decline. This 

may also apply to critical ecosystems services, such as water quality, where assessment targets may include variables (processes, critical species) believed essential 

to continued functioning. Vulnerability measures may also consider how well 

current management strategies reflect the likely future state of the system under 

study. A vulnerability assessment that considers the degree of change in vegetation community distributions identifies vulnerable systems with a high likelihood 

of not persisting under future climate regimes. This type of assessment might also 

identify species that are likely to colonize the area in the future and thereby provide information regarding potential new management goals. Sensitivity to climate 

change as inferred by observations of response to past climate change, is a good 

measure of which species or habitats are likely to need the greatest degree of intervention. Probability of occurrence or likelihood of distribution shifts are useful 

measures to gauge the applicability of current management strategies under future 

conditions. Degree of vulnerability, as generated with a quantified vulnerability 

score, can inform both short- and long-term goals.

Table 4.1. Summary of common measures of vulnerability, applicable time scales, key limitations, and common applications.

Measure of 

vulnerability Time scales Limitations Common applications

Degree of recent Short-term only Sensitive targets only Phenology of flowering plants and

change from historic (extrapolate to migrating birds

longer)

Degree of modeled Any Relation of target to modeled Species distributions, exposure

future change from climate variables

baseline

Observed impacts Any (but older Historic or paleo record data Effects of extreme events and high levels

under comparable data more available, manipulations of few of CO2 at a large scale, experimental

conditions limited) variables response in plants

Exceeding thresholds Any Difficulty identifying ecological Vegetation communities

 thresholds

Ability to adapt to Varies by target Relationship between adaptations Single species

change and population parameters

Relative degree of Any Relation of target to modeled Single species

response to climate climate variables

variation
USDA Forest Service RMRS-GTR-309. 2013. 53

4.2.1 Planning for the Future

Vulnerability assessments can provide valuable information for what and how 

natural resources may be affected by climate change and, thus, facilitate proactive 

management (Fig 4.10). Identification of vulnerable resources helps to set management priorities and the underlying causes of vulnerability elucidate intervention 

points for management. For example, Bagne and Finch (2012) found high vulnerability in Morafkai’s desert tortoise in southern Arizona due to timing changes in 

herbaceous growth, susceptibility of succulents to fire and invasive plants, and 

temperature-determined sex ratios, which suggest intervention points of fire management, invasive plant control, and enhancement of vegetative cover, particularly 

in cooler microhabitats. Managers might use a vulnerability assessment focused on 

habitat characteristics to identify how climate will affect the suitability of future 

relocation sites or effectiveness of reintroduction activities. Conserving biologically important landscapes and species are a critical component of maintaining 

biodiversity, especially in the face of climate change. At the same time, managers 

and researchers recognize that successful strategies are increasingly focused on 

maintaining habitat and ecosystem function over individual elements. Vulnerability 

assessments focused on climate impacts to biodiversity can provide information 

Figure 4.9. Steps for developing and implementing adaptation options. This diagram, first presented in Peterson and others 

(2011), represents an iterative process and includes repetitive review and ranking steps.
54 USDA Forest Service RMRS-GTR-309. 2013.

on potential hotspots and refugia (e.g., Davison and others 2012), thus allowing 

managers to focus resources where there is the greatest benefit. Vulnerability assessments are also useful for identifying monitoring strategies, especially where 

current programs exclude potentially sensitive species.

4.2.2 Promoting Resistance and Resilience

Vulnerability assessments can be used to gauge the effectiveness of management 

strategies focused along a wide biological spectrum as well as determine the best 

practices for increasing resilience and resistance of our landscapes under climate 

change (Peterson and others 2011; Lou Comer and others 2012). Species-level 

assessments are valuable for identifying high priority species and potential new 

sources of concern. For instance, a manager might use a vulnerability assessment 

to determine whether ecologically important species might be negatively impacted 

by climate impacts or whether an invasive species will be favored by future conditions. Depending on the degree and nature of the change, managers can choose 

whether specific areas or species warrant actions to mitigate loss of biodiversity 

or prevent establishment of exotic species. For species already of concern, vulnerability assessments can improve our ability to foresee and reduce additive stressor 

impacts that arise from both the direct and indirect consequences of climate change. 

Restoration practices to enhance resilience include habitat restoration and reestablishment of natural and important disturbance processes such as floods and fire. A 

vulnerability assessment that compares the degree of expected change across the 

landscape could be used to prioritize restoration activities to areas with the greatest promise for sustaining populations. Alternatively, post-fire seeding activities 

may be informed by estimations of future conditions produced by a vulnerability 

assessment. Assessments are also helpful for facilitated transformation strategies 

Figure 4.10. Example of work-flow used to create a vulnerability assessment that is translated to on-the-ground 

management strategies. Endpoints are determined by the questions and objectives of managers and are 

largely influenced by management strategies to mitigate, improve, or facilitate response to climate change. 

Target selection depends upon the desired endpoint and influences the relevant measures used to gauge 

target vulnerability. Targets also influence the availability of relevant measures.
USDA Forest Service RMRS-GTR-309. 2013. 55

(USFS Scorecard Guidelines), which aim to work with anticipated changes in order to facilitate transitions to new stable states with minimal ecological disruption. 

Adaptation strategies aimed at improving genetic adaptive capacity, translocation 

and relocation activities can be informed by multiple types of vulnerability measures and assessments.

4.2.3 Landscape-Level Planning

Future climate envelopes inform landscape practices that involve the development and preservation of corridors, relocation of individuals, and preservation 

of high quality habitats (Glick and others 2011). Increasingly, conservation and 

management must cross political and jurisdictional boundaries. Common objectives and strategies for maintaining wildlife and habitat across landscapes can be 

informed by a vulnerability assessment conducted at regional levels or involving 

species relevant to multiple stakeholder groups. For example, increasing adaptive 

capacity of populations threatened by climate change commonly involves improving genetic stock through translocations. Assessments that outline future suitable 

conditions can guide practices that aim to improve the expression of drought tolerance or other favorable traits through translocations, which often must cross 

political boundaries.

4.2.4 Limitations

Predicting response to climate change and measuring climate change vulnerability is difficult because it is part of a complex and dynamic process. Vulnerability 

measures address effects through various means that highlight unique aspects of 

natural systems. The relevance of any one measure of change or impact relates to 

its capacity to accurately provide information regarding future trends and the potential for actions to ameliorate or mitigate effects. The dynamic and progressive 

nature of climate change also requires managers to carefully consider values and 

management goals. Divergence from current or historic conditions highlights the 

need for managers to identify appropriate and desirable benchmarks for natural 

resources. As noted in Chapters 1 and 2, benchmarks are often set on reference 

conditions but, in light of climate change, are now moving away from emulating 

historic conditions and toward a focus on preserving ecosystem function and services. Our understanding of the full range of potential changes and responses, as 

well as our ability to address them, will grow as more researchers and managers 

participate in the assessment process.

4.2.5 Assessment Tools and Guidance

Vulnerability assessments and the methods used to generate them can supply 

managers with the tools and resources needed to make decisions that incorporate considerations of climate change and climate change vulnerability (Fig. 4.10). 

There are a number of tools available to facilitate vulnerability assessment, several 

of which are specifically designed to inform management decisions (Table 4.2). 

Many of these sources (e.g., VEMAP) provide estimates of future impact, which 

can then be used within an assessment of vulnerability. Some sources provide critical baseline data, such as natural history information, that are useful for conducting 

analyses or applying an index scoring systems (e.g., NatureServe Explorer and 

BISON-M). Others (e.g., Treeage Pro) are methods to use the results of a vulnerability assessment to create management strategies or guide decision-making 
56 USDA Forest Service RMRS-GTR-309. 2013.

processes. These decision support tools are particularly useful for addressing issue 

relating to the uncertainty inherent in climate and distribution modeling efforts. 

Several networks and exchanges have developed in response to the increasing need 

for cross boundary collaboration. TACCIMO (Template for Assessing Climate 

Change Information and Management Options) was developed to provide an avenue for distributing scientific data to land managers. Similarly, ongoing efforts in 

the ILAP aim to synthesize and provide products to managers and conservationists base on output from many of the modeling methods discussed in this chapter. 

Additional tools can be found on the Forest Service Climate Change Resource 

Centers (SCRC, http://www.fs.fed.us/ccrc/). Another source for climate data, both 

historic and projected, is the Western Water Assessment page, which contains data 

for terrestrial and hydrological systems: http://wwa.colorado.edu/climate_change/

trendpro.html. In addition, a number of approaches have been developed that allow 

users to compare adaptive management strategies. These may range from specific 

analyses of risk (informed by products produced during a vulnerability assessment 

process) to more general conceptual frameworks, which consider either directly or 

indirectly the impacts of climate change for management targets and try to identify 

specific management objectives or options.

Table 4.2. Resources, including data repositories, for exploring management options and for use in climate change vulnerability 

assessments. Asterisks indicate data resource.

Name Description Target/scope Available from:

Data Sources/Tools

U.S. Geological Survey’s Online tool to aid in analysis and retrieval Land cover and http://www.nbii.gov/portal/

Gap Analysis Program of species distribution data. vertebrate species server.pt/community/gap_

(GAP)* online_analysis_tool/1851

Bureau of Reclamation’s Temperature, precipitation, evaporation, Climate and hydrology http://gdo-dcp.ucllnl.org/

Bias Corrected and and streamflow characteristics are available of western U.S. downscaled_cmip_projections/

Downscaled WCRP for download from website. watersheds, rivers, dcpInterface.html

CMIP3 Climate and and streams

Hydrology Projections*

National Atlas* Provides GIS format data on land cover, Varies http://www.nationalatlas.gov/

land use, hydrography, climate, and digital atlasftp.html

elevation models. 

Multi-Resolution Land Landcover databases. Bioregions http://www.mrlc.gov/mrlc2k_

Characteristics nlcd.asp

Consortium*

Vegetation/Ecosystem Uses historical and future projected climate Vegetation types/ http://www.cgd.ucar.edu

modeling and analysis data, soils and vegetation maps, and a biomes /vemap/

Project- VEMtAP* number of process models to project also see Feenstra 2009

communities across the globe. Produces 

spatially explicit information regarding 

terrestrial ecosystem sensitivity to elevated 

CO2 and climate change.

ClimateWizard* Estimate historical and future temperature Climate variables http://www.climatewizard.org/

and precipitation changes as absolute or 

percent change.

Biota Information System Biological information and summaries. New Mexico species http://www.bison-m.org/

of New Mexico 

(BISON-M)

IUCN Red List Uses current threats and population trends Threatened plants and http://www.iucnredlist.org/

and related information to determine risk animals globally

of future population declines.
USDA Forest Service RMRS-GTR-309. 2013. 57

Wyoming Natural Biological information and summaries. Wyoming http://www.uwyo.edu/wyndd/

Diversity Database

NatureServe Explorer* Biological information and summaries. Plants and animals of http://www.natureserve.org/

U.S. and Canada explorer/

U.S. drought monitor Historical, current, and future maps of U.S. http://droughtmonitor.unl.edu/

(current conditions and drought levels.

projections)*

Climate Change Tree and Current and future projections of species Eastern U.S.— http://www.fs.fed.us/ccrc/tools/

Bird Atlases* under climate change and considering 134 trees, 147 birds atlas.shtml

current management issues.

Climate-FVS [Forest Model that relies on modified FVS growth Tree species Crookston and others 2010

Vegetation Simulator] equations to predict performance under 

climate change. 

U.S. National Phenology Integrates data collection and houses Plants and animals of http://www.usanpn.org/home

Network* database of literature and observations U.S. and Canada

regarding species phenology.

Network and Exchanges

Southwest Climate Regional assessment of climate change Southwest U.S. http://www.

Change Network impacts on habitat and watershed. southwestclimatechange.org/

Includes workshops for managers and 

planners to introduce adaptation options.

Integrated Landscape Collaborative effort to explore dynamics of Currently in http://oregonstate.edu/inr/ilap

Assessment Project (ILAP)* land ownership, habitat disturbance, and development for SW

climate change. Provides data, tools, and and Oregon

information.

Template for Assessing Center provides forest landowners, Forests http://www.sgcp.ncsu.

Climate Change Impacts managers, and scientists with the latest edu:8090/

and Management Options research and expertise concerning

(TACCIMO)* environmental threats to forests.

Landfire Geospatial data regarding land Ecosystems, habitat, http://www.landfire.gov/

classifications across the U.S. as it relates and vegetation

to climate and disturbance. Typically characteristics

reports departure from reference conditions.

Statistical Decision Support Statistical methods to estimate potential Bernliner and others 2000;

response of targets to risk factors and uncertainty. Prato 2009

Bayesian Analysis Toolkit Software package that allows users to Uncertainty measure http://www.mppmu.mpg.de/

compare model predictions to data, test bat/

model validity, and extract values of free 

parameters of models.

Treeage Pro Decision support software that uses various Decision support www.treeage.com/products.

methods to distinguish between models index.html

and decisions options.

Delphi Decision Aid site Data gathering tool for forecasting purposes. Forecasts of climate armstrong.wharton.upenn.edu/

change delphi2/

BIOMOD Ecological niche factor analysis. BIOMOD Environmental http://r-forge.r-project.org/

is a computer platform for ensemble envelopes projects/biomod/

forecasting of species distributions, enabling 

the treatment of a range of methodological 

uncertainties in models and the examination 

of species-environment relationships. 

Table 4.2. Continued.

Name Description Target/scope Available from:
58 USDA Forest Service RMRS-GTR-309. 2013.

Literature Cited

Bagne, Karen E., Deborah M. Finch. 2012. Vulnerability of species to climate 

change in the Southwest: threatened, endangered, and at-risk species at the 

Barry M. Goldwater Range, Arizona. Gen. Tech. Rep. RMRS-GTR-284. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 139 p.

Billington, H.L., and J. Pelham. 1991. Genetic Variation in the Date of Budburst 

in Scottish Birch Populations: Implications for Climate Change. Functional 

Ecology 5: 403-409.

Bolger, D.T., M.A. Patten, and D.C. Bostock. 2005. Avian reproductive failure in 

response to an extreme climatic event. Oecologia 142: 398-406.

Breshears, D.D., N.S. Cobb, P.M. Rich, [and others]. 2005. Regional vegetation 

die-off in response to global-change-type drought. Proceedings of the National 

Academy of Sciences 102:15144-15148.

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 G. Sugihara. 2009. Thresholds of climate change 

in Ecosystems: Final Report, Synthesis and Assessment Product 4.2. : U.S. 

Geological Survey A report by the U.S. Climate Change Science Program and 

the Subcommittee on Global Change Research. 157+ p. Collatz, G.J., J.A. Berry, 

and J.S. Clark. 1998. Effects of climate and atmospheric CO2 partial pressure on 

the global distribution of C4 grasses: present, past, and future. Oecologia 114: 

441-454.

Davison, J.E, S. Coe, D. Finch, [and others]. 2012. Bringing indices of species 

vulnerability to climate change into geographic space: an assessment across the 

Coronado National Forest. Biodiversity Conservation 21: 189-204.

Enquist, C., and D. Gori. 2008a. Implications of Recent Climate Change and 

Conservation Priorities in New Mexico. The Nature Conservancy New Mexico 

Conservation Science Program, 79 p. Available at: http://www.nmconservation.

org. (Accessed December 2008.)

Iverson, L.R., A.M. Prasad, S.N. Matthews, and M. Peters. 2008. Estimating 

potential habitat for 134 eastern US tree species under six climate scenarios. 

Forest Ecology and Management 254(3): 390-406.

Iverson, L.R., M.W. Schwartz, and A.M. Prasad. 2004. Potential colonization of 

newly available tree-species habitat under climate change: an analysis for five 

eastern US species. Landscape Ecology 19: 787-799.

Jenni, L., and M. Kery. 2003. Timing of autumn bird migration under climate 

change: advances in long–distance migrants, delays in short-distance migrants. 

Proceedings of the Royal Society, London 270: 1467-1471.

Joyce, L.A., C.H. Flather, and M. Koopman. 2008. WHPRP Final Project Report: 

1.B: Analysis of Potential Impacts of Climate Change on Wildlife Habitats in 

the U.S. Available at: http://www.tribesandclimatechange.org/docs/tribes_214.

pdf.

Keller, F., S. Goyette, and M. Beniston. 2005. Sensitivity analysis of snow cover 

to climate change scenarios and their impact on plant habitats in alpine terrain. 

Climatic Change 72:299-319.

Lawler, J.J., S.L. Shafer, D. White, [and others]. 2009. Projected climate-induced 

faunal change in the Western Hemisphere. Ecology 90: 588-597.
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Parmesan, C. 2006. Ecological and Evolutionary Responses to Recent Climate 

Change. Annual Review of Ecology, Evolution, and Systematics 37: 637-669.

Petchey, Owen L., and Kevin J. Gaston. 2009. Effects on ecosystem resilience of 

biodiversity, extinctions, and the structure of regional species pools. Theoretical 

Ecology 2:177-187.

Peterson, D.L., C.I. Millar, L.A. Joyce, M.J. Furniss, J.E. Halofsky, R.P. Neilson, 

and T. Lyn Morelli. 2011. Responding to climate change in National Forests: a 

guidebook for developing adaptation options. Gen. Tech. Rep. PNW-GTR-855. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 109 p.

Schmitz, O.J., E. Post, C.E. Burns, and K.M. Johnston. 2003. Ecosystem Responses 

to Global Climate Change: Moving Beyond Color Mapping. BioScience 

53(12):1199-1205.

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A Guide for National Forest and Grasslands, Version 2. Available at: http://www.

fs.fed.us/climatechange/advisor/scorecard/scorecard-guidance-08-2011.pdf.
60 USDA Forest Service RMRS-GTR-309. 2013.

Chapter 5. Vulnerability of Southwestern Species and 

Ecosystems to Climate Change: A Review

5.1 Introduction

Climate change vulnerability assessments link scientific information of future 

change to specific features, species, or areas that must be managed to successfully mitigate the negative consequences of climate change for natural ecosystems. 

Ideally, vulnerability assessments would provide a comprehensive view of both 

small and large issues regarding species conservation across multiple regions of 

interest. In reality, the body of literature available for managers for any one region 

is immense and can be difficult to access, and few publications fulfill the specific 

characteristics of a climate change vulnerability assessments (see Chapter 1) that 

make them useful to managers. These shortcomings limit our capacity to understand and interpret the ecologically relevant issues relating to climate changes. In 

this chapter, we review and synthesize the current body of climate change vulnerability assessment literature for southwestern species, habitats, and ecosystems to 

provide a starting point upon which to build comprehensive and targeted assessments and plans in the future.

The Southwest United States (henceforth, “SW”) has undergone rapid expansion in the last half of the century with consequences for resident populations and 

the natural environment. Climate projections for the United States indicate that the 

SW will experience more extreme change with respect to temperature increases 

and drought than other regions (Fig. 5.1). These changes exacerbate existing problems with sustaining valued natural resources. For example, longer droughts and 

higher temperatures will reduce the already scarce water sources critical to plants, 

fish, and wildlife as well as the large human population that occupies the region. 

It is imperative to address these issues and develop a clear understanding of where 

and to what degree climate change will affect SW species and habitats in order that 

future management and development plans can be created to effectively balance 

human and environmental needs.

As discussed in Chapters 3 and 4, vulnerability assessments include a wide variety of approaches and measures. This is reflected in the information available for 

SW systems, which encompass a broad array of methods and output. Within this 

review, we identify trends where studies overlap and gaps in our knowledge, and 

we discuss implications for future assessment work, climate change studies and 

management plans. We focus primarily on synthesizing climate change vulnerability assessments that relate specifically to natural resource management than on 

the large and developing body of literature that addresses human-environmental 

systems (see Turner and others 2003 for discussion). We identify 12 reviews that 

summarize our understanding of climate projections and potential effects for the 

SW (Table 5.1). We have also compiled articles that contain information either 

used in SW assessments or of value for conducting future assessments of the SW 

(Appendix 2).
USDA Forest Service RMRS-GTR-309. 2013. 61

Figure 5.1. Figure from the 

U.S. Global Change 

Research Program’s 2009 

report showing relative 

changes in precipitation. 

Predicted change in spring 

precipitation for 2080-

2099 as compared to 

1961-1979 for lower and 

higher emission scenarios. 

Hatched areas indicate 

greatest confidence. Report 

available at: http://nca2009.

globalchange.gov/southwest

5.2 Methods

For this review, we compiled assessments that provided new information regarding species or habitat vulnerability to climate change in the SW. We focused 

on assessments with a strong biogeographic content, using Google Scholar and the 

Template for Assessing Climate Change Information and Management Options 

(TACCIMO) website (http://www.sgcp.ncsu.edu:8090/) and other online resources for climate change research and management (see Table 4.2). We concentrated 

on documents that fulfill our working definition of a climate change vulnerability 

assessment: they examine one or more aspects of vulnerability to identify species, 

habitats, or biomes that are most likely to suffer negative consequences in response 

to warming trends. We distinguished between products that synthesize current information (Table 5.1) and those that attempt to identify or assess vulnerability 

(Table 5.2). Vulnerability assessments are specific in nature (see Chapters 1, 4), 

but the question of future vulnerability in the SW and a comprehensive discussion 

of such at times requires a broader discussion of studies that address particular 

aspects of the vulnerability question. Therefore, we include publications that deal 

with important aspects of species or habitat vulnerability to climate change though 

they do not constitute a formal vulnerability assessment per se (e.g., Bradley 2009; 

Notaro and others 2012; Peery and others 2012; Lawler and others 2009). In addition, we have compiled a list of 47 studies presented in Appendix 2 that address 

several important aspects of climate change vulnerability but are not included in 

the following discussion.
62 USDA Forest Service RMRS-GTR-309. 2013.

Table 5.1. Reviews and syntheses of climate change projections and impacts for species and habitat specific to the SW United States.Source Description Target Topic Location Habitat typeChambers and Pellant Reviews projections and implications of climate change for Plants/ habitat Climate change Great Basin, Columbia Rangelands2008 water quantity and quality for wild and domestic species. Plateau, Colorado

Identify a number of trends in response to changes in Plateautemperature and CO2.

Dale and others 2001 Reviews climate effects on disturbance processes, including Disturbance Climate and U.S. Forests and

fire, insect, and pathogen outbreak; introduced species and and climate disturbance Grasslandsdrought; hurricanes; ice storms; windstorms; landslides; and changeinteractions of various disturbances. Discusses strategies for dealing with forest disturbance. Includes socioeconomic considerations.

Garin and Lenart 2007 Reviews the trends and latest research. Concluded that Water Climate change SW U.S. Water resources

temperature effects trump uncertainty associated with availabilityprecipitation because water is very likely to become less available due to temperature effects.

Hughes and Diaz 2008 Compiles data and information on future climate impacts for Climate Climate change Interior Western U.S. Arid lands

arid lands with particular focus on the effect of increased variability on the provision of ecosystem services.

Kolivras and Comrie Reviews potential consequences and observed changes to Animals Disease SW U.S. Various2004 infectious diseases in the SW U.S.Lenart and others 2007 Reviews the major expected climate changes for the SW. Water, forests, Climate SW U.S. Forests and

Elaborates on some of the indirect, downstream causes grassland projections grasslands(e.g., monsoon seasons, soil moisture) and discusses recent findings.

Lynn and others 2011 Reviews direct and indirect aspects of social vulnerability to Socio- Social U.S. Economic sectors

climate change. economic vulnerability

McPherson and Weltzin Summarizes literature regarding past vegetation change and Livestock, Disturbance and Southeastern Arizona and Rangelands2000 importance of various disturbances. Identifies experiments native climate change southwestern New

necessary to determine causal mechanisms underlying vertebrates Mexicocurrent and future change. Also evaluates the effects and and invertebratesimportance of disturbance and climate change on plant communities.

Perry and others 2012 Reviews direct and indirect impacts of climate change and Water Global change Western North America Water systems

discusses research priorities and strategies (preserving reservoirs as a strategy).

Pettit and Naiman 2007 Reviews fire effects for soil and vegetation and discusses Water Fire Western Oregon Arid savanna

adaptation of systems to fire and future implications under climate change. 

Poff and others 2011 Reviews literature to date regarding threats to riparian Water Threats Western North America Riparian habitats

systems. Climate change is one of many considerations.

Sammis 2001 Discusses current, past, and future climate of New Mexico. Climate Climate New Mexico VariousVasquez-Leon and A comparative assessment of climate vulnerability: Social and Socio-economic SW U.S. Variousothers 2003 agriculture and ranching on both sides of the economic

U.S.-Mexico border. sectors
USDA Forest Service RMRS-GTR-309. 2013. 63 Table 5.2. Climate change vulnerability assessments that contain information for the SW United States. We indicate the scale and spatial focus of the assessment as well as identify the emphasis and primary methods for determining or measuring vulnerability. Climate parameters are listed as reported. “na” indicates not applicable or not available. Time frame or Source Scale Location Emphasis Method Tools/Measures conditions Archer and Regional SW U.S. Plant species and Identifies species and Reviews issues and identifies impacts of expected climate na Predick 2008 habitats; special habitats that will undergo change using logical progression of known consequences focus on invasive greatest change for the commonly agreed upon increases in temperature and changes to hydrological regimes. Bagne and State/ Southern Animal species Quantifies species traits/ System for Assessing Vulnerability of Species (SAVS) to 30- to 50-year future Finch 2012 local Arizona vulnerability index Climate Change window Bagne and State/ Southern Animal species Quantifies species traits/ System for Assessing Vulnerability of Species (SAVS) to 30- to 50-year future Finch 2013 local Arizona vulnerability index Climate Change window Bradley 2009 National U.S. Bromus trectorum Identifies likelihood of Developed a bioclimatic envelope model for 2090-2100 presence: areas of greatest cheatgrass across the western U.S. Compared using impact Mahalanobis distance and Maxent to define climate suitability. Future climate from 10 AOGCMS under the A1B emission scenario. Buckley 2008 National North Animal species Compares energetic costs Bioenergetic model Doubling of CO2 America among populations Christensen Local Colorado Hydrological Identifies degree of Used macroscale hydrology and water resource models 2010-2039, 2040-2069, and and River systems expected change and climate data from 11 GCMs under 2 emission 2070-2099 Lettenmaier Basin (flow dynamics) scenarios. 2006 Coe and others State/local Sky Animal species Quantifies species traits/ System for Assessing Vulnerability of Species (SAVS) to 30- to 50-year future 2012 Islands, vulnerability index Climate Change window Arizona Comer and Regional/ Sonoran Focal natural Identifies habitats with Uses a climate change vulnerability index combined with 2060 others 2012 local and communities greatest vulnerability estimates of future climate (ensemble model of six GCMs) Mojave (plants) and disturbance change as well as impacts to keystone deserts, species to generate vulnerability scores. U.S. Currie 2001 National U.S. Animal and plant Identifies changes in Used multiple regression to relate richness to climate Doubling of CO2 species richness biodiversity variables derived from climate atlases, published maps of geographic ranges, and CCC GCM models. Enquist and State New Comprehensive Identify areas of greatest Climate Wizard, measure of moisture stress, drought 2030, 2060 Gori 2008 Mexico vulnerability sensitive species and habitats Enquist and State New Hydrological Identify areas of greatest Climate Wizard, measure of moisture stress, biodiversity 2030, 2060 others 2008 Mexico vulnerability Foden and Global Global Animal species Quantifies species traits IUCN Vulnerability Index Ongoing others 2008
64 USDA Forest Service RMRS-GTR-309. 2013. Friggens and State/ New Animal species Quantifies species traits/ System for Assessing Vulnerability of Species (SAVS) to 30- to 50-year future others 2013 local Mexico vulnerability index Climate Change window Gonzales and Global Global Ecotones Quantifies Vulnerability classification framework using IPCC 2100 others 2010 species traits and identifies uncertainty and a probabilistic framework. MC1 elements at greatest risk of vegetation model; nine GCM-emission scenario impact combinations (IPCC). Hansen and National U.S. Plant (tree) species Identifies greatest threats/ Synthesize of current knowledge and four biodiversity 2100/doubling of CO2 others 2001 and communities species that will undergo models: MAPSS, DISTRIB, Response surface model and and animal greatest change Currie models. Four equilibrium and three transient richness GCMs under six scenarios. Hauer and Regional Rocky Hydrological Identifies elements at Reviews of past and current conditions and hypothesizes na others 1997 Mountains systems (elements) greatest risk of impact about future climate conditions and its relationship to hydrological (and biological) processes. Discusses biogeochemistry, fluvial stuff, and biota of river and lake ecosystems. Hurd and Regional SW U.S. Hydrological Identifies systems Hydro-economic models, WATBAL, hydroelectric model 2000, 2030, and 2080 Coonrod 2008 systems (ecological and economic) the General Algebraic Model System (GAMS) under nine at greatest risk of impact scenarios (three baseline only considered socio factors). Estimates of output streamflow and runoff, reservoir evaporation rate, agricultural use, and urban water demand. HADCM3, CSIRO, NOAA, GFDL0 used to generate six scenarios. Joyce and National U.S. Plant communities Synthesizes/conducts Biogeochemical models: MAPSS, DOLY, BIOME2, BIOME3 Birdsey 2001 (forests) comparative analysis of and climate data available via 2nd and 4th Assessment studies measuring change Report (multiple scenario combinations). Joyce and National U.S. Animal Estimated/ranked species Review of documented climate related changes to 2100 others 2008 communities, by degree change estimate degree of habitat overlap, habitat area shift, and habitat change climate shift degree of expected climate change. Based upon three GCM, two emission scenarios, and two ecological assumptions regarding plant sensitivity to elevated CO2. Julius and Regional SW U.S. Hydrological Identifies current status Descriptive with classification based on current features. na others 2006 systems and potential for (Stromberg and (rivers) vulnerability others 2006) Kupfer and State/ Sky Island Ecotones Identify areas of greatest Linked plant maps, topographic data, and estimated mean temperatures: 1, 2, 3, others 2005 local ecosystem change/impact climate variables (mean daily temp high and low, 4, and 5 °C and precipitation for 60-m pixels using MT-CLIM [mountain precipitation:10, 25, 50, microclimate simulator]) to create predictive vegetation and 100% model. Used manually derived climate scenarios: range of

mean low temperature (+1-+5) and precipitation(-10%-+100) regimes.

Table 5.2. Continued.

Time frame or 

Source Scale Location Emphasis Method Tools/Measures conditions
USDA Forest Service RMRS-GTR-309. 2013. 65 Lawler and West Animals Identify areas of greatest Used BCE (random tree) to generate data on future 2071-2100 others 2009 Hemisphere change/impact distributions and calculates an index based on the number of “new” or “missing” species per grid cell on maps as well as a “turnover” value representing both species gain and losses. Lawler and West Amphibians Identify areas and species Three-prong approach that used BCE (Random Forest), 2071-2100 others 2010 Hemisphere of greatest change/impact mapped representations of species range limits, and maps depicting areas most likely to experience decreased moisture to identify areas of greatest turnover (greatest geographical vulnerability). Climate conditions based on 20 climate simulations under B1 and A2 scenarios. McKenney Continent North Plant species Identifies species that will ANUCLIM software with presence data. Model baseline 2011-2040, 2041-2070, and others America (trees) undergo greatest change data were taken from 30-year climate station averages and 2071-2100 2007 (1971-2000). Climate projections were generated from Canadian GCM, UK-based Hadley GCM and Australian- based Commonwealth Scientific and Industrial Research Organization GCM under two emission scenarios: A2 and B2. Also included dispersal scenarios. Meyer and National U.S. Hydrological Identifies elements at Review of published analysis of climate effects on goods na others 1999 systems greatest risk of impact and services provided by freshwater ecosystems. (freshwater) Specifically address climate changes, water withdrawal, species invasions, and land use. Munson and Local Sonoran Plant species and Identifies species/groups Historical trend analysis—100 years’ worth of vegetation na others 2012 Desert: functional types that will undergo greatest monitoring. Climate data obtained from weather stations National change and plant data obtained from surveys. Park lands in southern Arizona Neilson and National U.S. Plant communities Measures greatest change Biogeochemical models: MAPSS and BIOME3. Transient 2100 others 1998 (habitat) GCM projections. Only considers runoff/soil moisture and has expectations regarding persistence. Notaro and Regional SW U.S. Plant functional Identifies species, plant Combined bioclimatic envelope and dynamic vegetation 1970-2100 others 2012 type, species, types, and areas that will modeling approaches. MaxEnt (BCE modeling) and biodiversity (trees experience greatest Lund-Potsdam-Jena (LPJ) DGVM applied to 170 woody and shrubs) change species. Biodiversity is also estimated by summing BCE results for each cell. Uses both fixed and elevated CO2 values in simulations. Ojima and National U.S. Comprehensive Compiles data regarding First part of a larger effort to assess future impacts. na Lackett 2000 effects
66 USDA Forest Service RMRS-GTR-309. 2013. Ojima and Regional Western Comprehensive Identify areas of greatest Second part of a larger effort to assess future impacts. Historical, 2025-2034, Lackett 2002 U.S. change/impact Vegetation impacts gained through VEMAP and based 2090-2099 upon historical and GCM data. Peery and Regional SW U.S. Owls Identifies populations/ Linked observed vital rates (survival) and weather 1961-1990, 2041-2060, others 2012 areas that will experience variables using program MARK. Future climate under four and 2081-2100 greatest mortality GCMs (CNRM-CM3, SCIRO-Mk3.0, ECHam5, MIROC3.2) and three emission scenarios. Steinke and Regional Western Watersheds Identifies watersheds that Contains 11 case studies from National Forests in the West. 2030 and 2070 others 2012 U.S. will have the greatest Each case study has unique methods for estimating from Furniss vulnerability to climate vulnerability. Within the Coconino (Arizona), vulnerability and others change was a function of exposure (e.g., change to snowpack), 2012 water value, and watershed sensitivity (e.g., inherent biodiversity). Theobald and Regional Western Hydrological Quantifies vulnerabilities Created a “riparian threats score” integrating info from na others 2010 U.S. systems for riparian ecosystems three-prong analysis. Models of runoff and sediment yield. (watersheds) Used three GCMs under A1B and B1 scenarios. Williams and Regional SW U.S. Plant species Identifies habitats and Analyzes historic disturbance trends. Statistical models 2050-2099 others 2010 (Trees) species that will undergo of tree growth and four scenarios of future climate: greatest change and have A2, A1B, and linear trends established from previous highest vulnerability to 30 and 144 years. mortality due to fire/pests Winter 2000 National U.S. Hydrological Identifies types of Assesses characteristics of hydrological landscapes by na (landscape) hydrological systems most using the hydrologic-landscape concepts. vulnerable to climate change Zack and Regional Great Animal species Quantifies species traits/ NatureServe’s Climate Change Vulnerability Index Three time periods others 2010 Plains vulnerability index Table 5.2. Continued. Time frame or Source Scale Location Emphasis Method Tools/Measures conditions
USDA Forest Service RMRS-GTR-309. 2013. 67

5.3 Southwest Vulnerability Assessments

Our search identified 36 vulnerability assessments published over the last 

17 years addressing climate change in the SW (Table 5.2). These assessments 

span spatial scales ranging from the local/state level through national, continental, 

and global ranges (Tables 5.2, 5.3). Two global assessments (Foden and others 

2008; Gonzales and others 2010), though not specific to the SW, relate to species sensitivity and adaptive capacity to climate change that has relevance for SW 

animal species and plant functional types (Table 5.4). Two studies focused on the 

western hemisphere (Lawler 2009, 2010) and, though broadly focused, provide 

some insight as to future areas of concern for animal biodiversity. One continental assessment focused on North American animal species (McKenney and others 

2007). A large number of assessments (10) focused at the national scale (Tables 

5.2, 5.3, 5.4) and most specifically addressed species or habitats (6 of 10) found 

within the SW. Of the 12 regionally focused assessments, 6 centered on the SW, 

3 were more generally focused on the West, and 3 focused on specific habitats: 

the Great Plains, which includes some of New Mexico and Texas, and the Mojave 

and Sonoran Deserts, which includes Arizona, California, and Nevada. Of those 

assessments that dealt with water, 2 were conducted at the national level, 5 were 

regionally based (Furniss and others 2012 contained cases studies from 11 National 

Forests), and 1 each focused specifically within New Mexico and the Colorado 

River Basin (Table 5.3). Two analyses assessed plants and animals within New 

Mexico (Enquist and others 2008; Enquist and Gori 2008), one assessed plants 

(Kupfer and other 2005), and two assessments (Coe and others 2012; Friggens and 

others 2013) considered animal vulnerability and animal and plant vulnerability 

(Bagne and Finch 2012; Bagne and Finch 2013) at locales within states.

Assessment targets cover biological scales ranging from species to communities 

for both plants and animals (Table 5.4). Ecosystem-level analyses are quite common in our assessment pool and invariably dealt with habitat changes. One of the 

published studies (Peery and others 2012) addressed vulnerability at the level of 

populations. Eight assessments focused on vertebrate animal species, 11 dealt with 

plant species, and seven dealt with both animal and plant species.

Several common themes were found among SW assessments and climate-related studies:

• Vulnerability of grasslands to invasive species is likely to increase under climate 

change (Morgan and others 2008; Chambers and Pellan 2008; Neilson and others 1998).

• Changes to hydrological systems include:

ο Higher water temperatures with multiple effects for temperature-dependent 

species (Johnson and others 2005; Eaton and Scheller 1996).

ο Changes in precipitation events leading to less snowpack, changes to the 

timing of flood regimes, less flow, and reduced water tables (Theobald and 

others 2010).

ο Expansion of invasive species in both aquatic and riparian habitats (Theobald 

and others 2010; Rood and Conrad 2008).

• Many studies show that temperature alone drives or is sufficient to lead to observed or predicted changes (Williams and others 2010, tree growth; Currie 

2001, biodiversity; Garfin and Lenart, 2007; Eaton and Scheller 1996, loss of 

cool water fish species; Meyers and others 1999, many effects on aquatic systems; Hansen and others 2001, plant and animal species richness; Notaro and 
68 USDA Forest Service RMRS-GTR-309. 2013.

others 2012, plant species distributions; a notable exception is cheatgrass invasion, see Bradley 2009). 

• Areas near ecotones are more vulnerable to change (Kupfer and others 2005 

[in Appendix 2], Madrean habitats; Joyce and others 2008, Biomes; Allen and 

Breshears 1998).

• Future temperature projections often exceed historic observed maxima, limiting 

our ability to project future change to biotic species and communities based on 

bioclimatic modeing (Currie 2001; Williams 2001).

The following discussion compares assessments along a biological spectrum, 

first describing species-level analyses and moving through community to ecosystem-level analyses. Analyses of hydrological systems are unique and are discussed 

in their own section. Finally, we discuss assessments that comprehensively address 

animals, plants, and water.

Table 5.3. Number of assessments focused on animal, plant, or both types of organisms and the various scales 

presented in 36 assessments of climate change vulnerability for the SW.

Animal Both Plants Water Grand total

Global 1 1 2

West Hemisphere 2 2

Continental (North America) 1 1

National 1 4 3 2 10

North America 1 1

U.S. 4 3 2 9

Regional 2 2 3 5 12

Great Plains 1 1

Rocky Mountains 1 1

Sonoran and Mojave Desert 1 1

SW U.S. 1 1 2 2 6

Western U.S. 1 2 3

State (New Mexico only) 1 1 2

State/Local 3 5

MRG, New Mexico 1 1

Sky Islands, Arizona 1 1 2

DoD lands, Southern Arizona 2 2

Local 1 1 2

Colorado River Basin 1 1

Sonoran Desert: National Park lands 

in southern Arizona

1 1
USDA Forest Service RMRS-GTR-309. 2013. 69

Table 5.4. Spatial and biological scale of 36 climate change vulnerability assessments conducted for the SW United States. 

Landscape here refers to studies that address composition of multiple communities across a large area (e.g., North 

America).

Animal Plants Both Water Grand total Species Species Habitat Species, ecosystem Ecosystem Species habitat, Species, ecosystem Habitat Ecosystem Hydrological system Ecosystem Watershed

Global 1 1 2

Western Hemisphere 2 2

Continental 1 1

National 1 1 2 1 1 1 1 1 1 10

Regional 2 1 1 2 1 1 4 12

State 1 1 2

State/local 2 1 2 5

Local 1 1 2

Grand total 8 3 3 3 2 3 2 2 1 2 1 6 36

5.3.1 Vulnerability of Southwestern Species and Communities

Species-level assessments are useful for efficiently assessing species of conservation concern. When applied to many species within a taxonomic group or across 

numerous species in a target area, these methods can also inform management of 

regional levels of biodiversity. These approaches are most useful when they are 

used to supply spatially explicit effects of climate change on biodiversity. Of those 

studies that consider species, we discuss eight that addressed animals and five that 

considered plants.

Animals

Buckley (2008) used a dynamic biophysical model to assess relative vulnerability of Sceloporus lizards to climate change. Though broad in its analysis, this study 

showed changes in lizard distributions, including eastward shifts for many species 

under climate change. This analysis only considers a single aspect of vulnerability 

(impact), but the author tied these results into a discussion of future trends for lizard conservation. Ultimately, lizards appear quite resilient to the direct effects of 

warming trends, although it is not clear from this analysis how smaller-scale issues 

(e.g., interactions, dispersal ability, and plant species change) will influence future 

lizard distributions.

In a larger-scale analysis, Foden and others (2008) used quantification of traits to 

measure vulnerability of three animal groups: birds, amphibians, and corals. Foden 

and others (2008) generated comprehensive lists of species traits indicative of vulnerability that inspired later assessment tools such as NatureServe’s climate change 

index (Young and others 2010) and the SAVS system (Bagne and others 2011). The 

authors determined 35% of bird species across the globe possess traits that make 

them potentially sensitive to climate change. Specialized habitat or microhabitat 

climates and limited dispersal capacities were considered the most problematic 
70 USDA Forest Service RMRS-GTR-309. 2013.

issues. The taxonomic group with the greatest number of vulnerable species was 

the thrush (Turdidae) family, whereas cuckoos (Cuclidae), woodpeckers (Picidae), 

and doves and pigeons (Columbidae) were the least threatened. A greater proportion (52%) of amphibians than birds was considered vulnerable to future climate 

changes. Many species were from families not found within the SW. An exception 

is the Bufonidae family (toads and true toads), which had more than 50% of its 

species considered susceptible to climate change. Within the Caecilians only 18% 

of species were considered vulnerable though the authors note this might be due 

to a lack of information. Traits associated with amphibian vulnerability, including 

specialized habitat requirements, exclusive occurrence or reliance on threatened or 

unbuffered aquatic habitats, and dispersal issues related to barriers created by unsuitable habitats were most relevant to amphibians living the in the SW.

Lawler and others (2009) used a consensus based BCE model (see Chapter 3) 

to assess the effects of climate change as simulated by 10 GCMs under 3 emission 

scenarios (B1, A1B, and A2) on the range of 1818 birds, 723 mammals, and 413 

amphibians across the western United States. In addition to the basic BCE analysis, 

they compared results of analyses that assumed no dispersal versus unlimited dispersal and calculated a species turnover measure that represented degree of change 

in species composition. For the majority of climate scenarios (80%), Lawler and 

others’ (2009) analysis showed a loss of 11 and 17% for all species under B1 and 

A1 scenarios, respectively. Of importance to the SW, Lawler and others (2009) 

estimated that the greatest turnover for all taxa will occur in mountainous regions 

and that amphibians are most likely to experience range contractions and loss. In a 

second analysis focused on amphibians, Lawler and others (2010) combined distribution analysis with data on range limits and expected moisture changes across 

the western hemisphere to specifically identify sensitive species and areas prone 

to species loss under climate change. They identified vulnerability according to 

an index generated by the number of times certain conditions exist within an area. 

Specifically, one score was given for each of these conditions: (1) area has an estimated turnover (species loss and gain) of at least a 50%; (2) area contains at least 

50% range restricted species; and (3) at least 20% of grid cells within the area are 

projected to experience decreases in precipitation. Their analysis showed that the 

warm deserts of the United States were projected to have high vulnerability due to 

decreased precipitation and high species turnover (potential vulnerability of 2 on 

a scale of 0-3).

Zack and others (2010) examined several species native to the Great Plains region using the Natureserve’s Climate Change Vulnerability Index (CCVI, Young 

and others 2010). They found none of the 30 species ranked in their assessment fell 

within the highest vulnerability class. Zack and others (2010) did find the Lesserprairie chicken, regal fritallary, and black-footed ferret moderately vulnerable to 

climate change effects. Overall, the authors concluded that climate change is not 

a driving negative force for many species and most species were presumed stable. 

Zack and others (2010) also examined range location relative to the study site (the 

Great Plains Landscape Conservation Cooperative) but did not draw additional 

conclusions regarding species vulnerability.

Four other assessments (Bagne and Finch 2012, 2013; Coe and others 2012; 

Friggens and others 2013) focused on vertebrate animal species with another vulnerability scoring system, SAVS (Bagne and others 2012). Coe and others (2012) 

focused on species in southern Arizona and specifically across the Sky Island region. Species were selected based upon criteria set forth by USDA Forest Service 

Region 3 biologists and included species of conservation and management concern. Riparian associated birds (elegant trogon, western yellow-billed cuckoo) and 
USDA Forest Service RMRS-GTR-309. 2013. 71

amphibians (Tarahumara frog, Chiricauhua leopard frog) were among the highest 

scoring species. Arid adapted species such as the desert tortoise, mesquite mouse, 

desert bighorn sheep, and Slevin’s bunchgrass lizard were the least vulnerable. 

Coe and others (2012) also provided a comprehensive review of climate projections and evidence for future effects of climate warming trends within the region. 

Bagne and Finch (2012, 2013) examined species inhabiting two Department of 

Defense (DoD) installations in Southwestern Arizona (Fig. 5.2). This study was 

focused on Threatened, Endangered and At-risk species including 21 animals and 

2 plants from Ft. Huachuca and 15 animals and 1 plant from Barry M. Goldwater 

Range (Bagne and Finch 2012, 2013). Their findings suggest that many already 

threatened species are at risk of additional issues due to climate change. The northern Mexican garter snake, southwest willow flycatcher and Arizona tree frog were 

the most vulnerable, whereas the Desert massagauga, aplomado falcon, and blacktailed prairie dog were the least vulnerable on Fort Huachuca. Both plant species, 

the Lemmon fleabane and Huachuca water umbel, received scores indicating increased vulnerability under climate change. For the Barry M. Goldwater Range, the 

Sonoran pronghorn was the highest scoring species followed by the desert tortoise 

and cactus ferruginous pygmy owl. The desert bighorn was moderately vulnerable 

while the California leaf-nosed bat, gilded flicker, and saddled leaf-nosed snake 

were least vulnerable. Friggens and others (2013) examined 117 species inhabiting 

the Middle Rio Grande of New Mexico. The species list included both common 

and rare species that inhabited the riparian corridor of the study area. Their results 

show many common species have higher vulnerability scores than rare species. In 

part, this pattern is explained by the natural division between riparian dependent 

species and those that use the riparian corridor more opportunistically. Within amphibians, frogs (northern leopard frog, western chorus frog) were most vulnerable 

and spadefoot toads the least vulnerable. Within reptiles, the Great Plains skink 

and gartersnakes (followed by aquatic turtle species) were the most vulnerable, 

whereas the rattlesnakes (western diamond back and prairie), kingsnake, and desert grassland whiptail were the least vulnerable. Riparian obligate birds such as the 

southwest willow flycatcher and the common yellowthroat were the most vulnerable to future changes, whereas the brown-headed cowbird and greater roadrunner 

were least vulnerable. Within the mammals, the New Mexican meadow jumping 

mouse and hoary bat received the highest scores whereas the jackrabbit and desert 

shrew appeared to be the least vulnerable.

Peery and others (2012) conducted a risk analysis that predicted survival 

and reproduction for three spotted owl (Strix occidentalis) populations in the 

SW (including California). Their analyses show that growing season precipitation (Arizona and New Mexico populations), nesting season temperature (New 

Mexico), annual precipitation, and minimum nesting season temperatures (Arizona 

and California) explained most of the observed variation in reproductive status. 

Survival was positively associated with previous year’s precipitation (previous 

two years in California) and negatively associated with growing season temperature (New Mexico, Arizona). Population projections show rapid declines for owls 

residing in New Mexico and Arizona under warming trends, whereas Southern 

Californian populations remain more stable. Peery and others (2012) also assessed 

potential effects of increased weather variation by doubling variations in precipitation and temperature changes but found little additional effect. For New Mexico 

and Arizona, populations are already predicted to be nearly extinct by the end of 

the century so additional decreases in survival rates due to increased variability 

had little effect on outcome. Southern Californian populations were little affected 

by projected weather changes whether those projections showed high variability 
72 USDA Forest Service RMRS-GTR-309. 2013.

or not. It appears that the distinction between southern California populations and 

New Mexico and Arizona populations reflect the current weather related population trends. Cold, wet springs limit reproductive success in northern California 

and Oregon populations, and these conditions are likely to decrease with climate 

warming potentially to the benefit of southern California populations as well. Owls 

in Arizona and New Mexico are more strongly affected by reductions in prey as a 

result of drying and warming trends. In addition, temperature increases are likely 

to be more important drivers than precipitation changes of population trends for 

owls in Arizona and New Mexico because predicted increases in temperature far 

exceed norms in annual variation: temperatures increase 3-fold whereas precipitation decreases 1-fold. 

Plants

Five studies considered impacts to plant species under warming conditions: the 

distribution of trees across North America (McKenney and others 2007) and within the SW (Williams and others 2010), the distribution of Sonoran desert plants 

(Munson and others 2012), and of shrubs and trees (Notaro and others 2012) and 

invasive grasses (Bradley 2009) in the SW.

McKenney and others (2007) used a climate envelope approach (see Chapter 3) 

to model the species-environment relationship for 130 North American tree species. Though little is said about the SW, it is interesting to note that projections for 

the southern United States fall outside of known historical ranges, highlighting a 

potential issue affecting management planning in the region.

Bradley (2009) provided a measure of risk for invasion from Bromus spp. in 

the western United States, which, while not technically a vulnerability measure 

(see Chapters 1 and 4), will undoubtedly be important determinant of vulnerability for many native species and ecosystems. The author found that decreased 

precipitation, particularly in the summer, led to an expansion of up to 45% of 

Figure 5.2. Vulnerability of species 

from the Barry M. Goldwater Range 

(from Bagne and Finch 2013). 

This study used a vulnerability 

index tool that assessed exposure, 

sensitivity, and adaptive capacity 

through scenario building and a 

questionnaire that results in scoring 

system. Scores were generated 

for four categories as well as for 

the collective (overall) expected 

vulnerability. 
USDA Forest Service RMRS-GTR-309. 2013. 73

suitable habitat for Bromus. Increased precipitation was associated with reductions 

in suitable habitat by as much as 70%. This study is interesting in that identified 

precipitation as an important if not primary element determining future suitability 

for invasive Bromus spp., in contrast to many other modeling efforts. There are 

also interesting implications for future Bromus spp. spread because precipitation 

patterns are likely to vary across the Great Plains with increases to the north and 

decreases to the south. As noted by Bradley (2009), many others have modeled 

invasive species (Beerling 1993 [Europe]; Sutherst 1995 [IUCN]; Zavaleta and 

Royval 2002 [Appendix 1]; Kriticaos and others 2003 [Australia]; Mika and others 2008). Among these, Zalaleta and Royval (2002) and Mika and others (2008) 

provide important discussions regarding the risk of existing habitats to future invasion or expansion of exotic species (Appendix 2). Zavaleta and Royval (2002), in 

particular, provide information and predictions for plant and animals species that 

are relevant to the United States though outside our study region.

Williams and others (2010) focused specifically on Pinus (P. edulis and P. ponderosa) species and Pseudotsuga menziesii (Douglas-fir) within the West. Williams 

and others (2010) used statistical models of tree growth based upon tree ring data 

to estimate growth under future conditions. They also explored contrasts between 

wildfire and tree mortality due to beetles over a two-decade period to evaluate 

trends and magnitude of disturbance effect. This analysis projects that the greatest and most widespread decreases in growth will occur in the SW and Colorado 

Rockies. Their findings also point to the influence of temperature on these observations; SW ponderosa pine and Douglas-fir are likely to be at greatest risk for 

drought induced mortality at lower level elevations, whereas pinyon pine appears 

sensitive throughout its range.

Munsen and others (2012), used historic data for plant species and communities 

types to produce information regarding relative vulnerability among the study species. Using 100 years of vegetation monitoring, the authors described population 

trends in response to recent changes in climate for Sonoran Desert habitats. They 

then extrapolated these findings to expectations for future trends in plant species 

composition. In mesic mesquite savanna, perennial grasses declined with declining precipitation, cacti increased, and Prosopis velutina declined in response to 

increased mean annual temperature. In xeric upland areas, leguminous Cercidium 

microphyllum declined in hillslopes and Fouquieriea spledens decreased on south 

and west facing slopes in response to increasing mean annual temperature. In xeric shrublands, Larrea tridetata and hemiparasite Krameria Grayi decreased with 

decreasing cool season precipitation and increasing aridity.

Notaro and others (2012) conducted a comprehensive analysis of future 

plant distributions for the SW United States that includes New Mexico Arizona, 

Colorado, and Utah. Their analysis is presented here because it represents valuable 

information regarding future changes with relevance to the assessment of plant vulnerability to climate change though this study is not in and of itself a vulnerability 

assessment. Similar studies exist (Bachelet and others 2001; Daly and others 2000; 

Thompson and others 1998; Shafer and others 2001; Rehfeldt and others 2006) that 

represent early versions of this type of analysis, which are not reviewed here but 

are listed in Appendix 2. Notaro and others (2012) simulated future distributions 

of 170 tree and shrub species. Their analysis compared 14 GCMS under the A2 

emission scenario. Using Maxent (Table 3.2), Notaro and others (2012) identified 

three important predictors of future range of North American species: temperature (with precipitation changes modifying patterns driven by warming trends), 

growing degree days, and annual mean temperature. Their analysis showed that 83 

species will experience a shift northward in ideal conditions, whereas 32 species 
74 USDA Forest Service RMRS-GTR-309. 2013.

are expected to shift southward. Overall, 76 species will experience range expansions and 70 species will experience range contractions. For species with the most 

robust results (agreement between 12 and 14 of the 14 GCMs used), the authors 

found 29 species with expected increases in range, whereas 39 were expected to 

experience range contractions, indicating that declines may be more frequent. Two 

species, Engelmann spruce (Picea engelmannii) and Mesquite (Prosopis juliflora), 

were well modeled by current climate conditions and represent plants limited by 

either high or low temperatures, respectively. Engelmann spruce is a cold tolerant, 

high elevation, evergreen conifer and the most abundant tree species in the SW 

(Notaro and others 2012), and mesquite, a drought tolerant tree/shrub, is found 

in the hotter regions. The distribution of Englemann spruce is expected to decline 

in the SW, whereas mesquite is likely to expand and shift northward, with the 

greatest changes seen for those scenarios that predict both warming and drying. 

Among evergreen species, Tumey oak (Quercus toumeyi) and Pringle Manzanita 

(Arctostaphylos pringlei), showed the largest predicted increase in range, whereas 

limber pine (Pinus flexilis) and big sage brush (Artemisia tridentate) had the largest projected range contractions. For deciduous species, the greatest expansions 

were seen for Tracy’s hawthorn (Crataegus tracyi) and Knowlton’s hophornbeam 

(Ostrya knowltonii), and the greatest contractions were seen for Willow hawthorn 

(Crataegus saligna) and narrow leaf cottonwood (Populus angustifolia). Climate 

changes are predicted to lead to an expansion of all yucca species and a majority 

of the oak species but contractions in most pine species, including ponderosa pine 

(Pinus ponderosa).

Community-level assessments

Analyses based on plant species often form the basis of community level assessments of climate change impacts on habitat (see MAPSS in Chapter 2). From our 

review, we found three assessments that consider community response as derived 

from analysis of species, each using quite different methods to measure future 

vulnerability. One assessment considered plant species and communities and the 

implication for animal species (Hansen and others 2001), one study (Currie 2001) 

examined patterns of biodiversity for both plants and animals, and one study focused on the impact of climate changes on distributions of plant groups (Kupfer 

and others 2005).

Hansen and others (2001) provided the most comprehensive assessment. Their 

report is broken in two parts: one models vegetation and biodiversity change under 

three different GCMs, and a second synthesis draws together current knowledge of 

interactions among climate, land use, and biodiversity with the distribution analysis to discuss future biodiversity and provide strategies and future research needs. 

Hansen and others (2001) used 4 biodiversity models: MAPSS (biogeographic 

approach) for biomes, DISTRIB (statistical regression tree) for tree species/forest community type, response surface model (statistical local regression) for tree/

shrub species, and Currie (statistical) models for species richness of trees, mammals, birds reptiles, and amphibians. Though their efforts are based upon climate 

parameters generated from early and now obsolete GCMs, their results identify 

a number of interesting trends. Statistical regression methods for individual species show that suitable habitat for ponderosa pine is likely to expand westward, 

whereas sagebrush (Artemesia), mountain hemlock, and Engelmann spruce will 

experience decreases in suitable habitat. MAPSS results showed alpine habitat all 

but disappears, whereas shrublands and arid woodlands expand into grasslands 

of the Interior West and Great Plains. The same models predict grasslands expansion into the deserts of the SW. Analysis of community richness revealed a strong 
USDA Forest Service RMRS-GTR-309. 2013. 75

correlation of richness with temperature but not precipitation. For trees, species 

richness is positively related to temperature up to a threshold where the relationship becomes negative. Importantly, richness in SW deserts is expected to decline 

as conditions get hotter and drier. Endotherms are expected to increase in northern 

(cooler) areas and experience moderate decreases in warmer areas, whereas ectotherms show monotonic increases along a latitudinal gradient from northern to 

southern areas. The authors noted great variability in results across GCMs.

Kupfer and others (2005) created predictive models of plant community distributions under 36 different scenarios of temperature and precipitation change 

with the intent to identify the most sensitive areas within the Sky Island region of 

southern Arizona. The authors focused on four dominant ecotones: desert scrub, 

desert grassland, Madrean evergreen woodland, and montane conifer. Increased 

temperatures alone were predicted to increase desert scrub habitat and lead to 

upslope movement of most other habitat types. Downslope movements were observed under scenarios of higher precipitation, indicating a high degree of moisture 

limitation in this system. Desert scrub and montane conifer habitats experienced 

greater than 50% change in area. However, these patterns could be partially attributed to their limited distribution in the lower and upper reaches of the Sky Islands, 

respectively, and to the current low abundance values, which led to relatively dramatic changes. In their assessment of vulnerability, Madrean evergreen classes and 

areas near ecotones were most sensitive and showed the greatest change in area 

with change in climate variables.

Currie (2001) used bioenergetics approaches to estimate future changes in biodiversity and predicted significant declines in tree, bird and mammal species in the 

SW. Currie’s predictions were based upon relationships between maximum July 

temperature and biodiversity, which appear to be closely linked. Species richness 

increased with increasing temperature though eventually reached a plateau and 

fell (decreases) as temperatures continued to rise. Importantly, Currie noted, as 

did Williams (2001), that projections for the SW often exceed the hottest observed 

July temperature predictions and, therefore, these models may underestimate the 

true effect of temperature increases on species richness in the SW.

Results of Notaro and others (2012) bioclimate analysis (see previous section) 

were used to estimate shifts in biodiversity across the region. They predicted major 

declines in high elevation evergreen forests, especially in Utah and Colorado, as 

well as Sonoran desert habitat but expected some level of enhancement to biodiversity of prairie ecosystems of eastern New Mexico. Changes to high elevation 

species appeared to be driven mostly by increases in mean July temperature, and 

the Sonoran desert species appear to be most sensitive to projected January temperatures. Interestingly, drier hotter conditions were predicted to favor diversity in 

New Mexico grasslands by providing an environment conducive to the establishment of Sonoran or Chihuahuan desert habitat, which are relatively species rich.

Animal species of the southwestern United States

Though a number of important areas of concern were noted for species inhabiting the SW, results from species-level vulnerability assessments were generally 

not comparable because they lack broad applicability. There were no instances 

where a single species was assessed by more than one of the studies reviewed here 

in a way that allows direct and meaningful comparisons. However, we did find a 

tendency for riparian associated species to be among the highest ranking or most 

vulnerable species within each assessment (Bagne and Finch 2012, 2013; Coe and 

others 2012; Friggens and others 2013). In addition, lizard species were not found 

to be particularly vulnerable to climate change effects where they were studied 
76 USDA Forest Service RMRS-GTR-309. 2013.

(Buckley 2008; Friggens and others 2013). Peery and others (2012) was the only 

study to model survival and reproduction for three SW sites, and their analysis 

showed important regional differences relating to current limiting conditions (cold 

versus dry weather).

Species-level studies covered a variety of spatial scales (Tables 5.3, 5.4). Foden 

(2008) examined species across their range and made general conclusions about 

taxonomic group vulnerability that did not consider local-level influences. Peery 

and others (2012) examined three owl populations in three states and found distinct differences in predictions across the region but also recognized that their 

predictions for local extinctions may not reflect trends at larger scales. Buckley 

considered lizard distributions at the national level, and Bagne and Finch (2012, 

2013), Coe and others (2012), and Friggens and others (2013) created ranks based 

on local-level responses to predict future population trends for species inhabiting 

specifically defined areas. Though information gathered from these latter assessments could be applied to other populations, issues noted for a specific region 

cannot be extrapolated to new regions without careful consideration. However, 

smaller-scale assessments that address specific species within their current habitats provide information that is highly relevant to managers. In addition, fine-scale 

analyses of species of specific concern or inhabiting areas of management interest 

are likely to be necessary for most management plans.

The focal target of each vulnerability assessment varied with important implications for the interpretation of assessment results. The ranking reported in Foden 

(2008) compliments the results found for the MRG bird study (Friggens and others 2013) but differs somewhat from Bagne and Finch (2012, 2013) and Coe and 

others (2012). The latter assessments focus on species already of concern, a status 

related to life history traits (e.g., specialization and sensitivity to habitat loss) that 

not only influence population vulnerability to declines but relate, in many cases, to 

additional vulnerability to climate-related effects.

Most species-level assessments relied on a quantification technique to rank species rather than statistical modeling. In such systems, uncertainty is considered by 

the user and appended to the output of the ranking typically as a separate associated 

value. This measure of uncertainty addresses data gaps and the general confidence 

in methods that model climate and habitat change. Foden (2008) indicated high 

levels of data insufficiency for amphibians. Coe and others (2012), Bagne and 

Finch (2012; 2013), and Friggens and others (2013) identified species and areas 

that require more research as related to species information, but do not estimate 

uncertainty related to climate modeling or other estimates of exposure. For Peery 

and others (2012), who did use a statistical model, uncertainty relates to variation in climate projections and the complex relationship between environmental 

change and population size. For instance, behavioral avoidance mechanisms like 

seeking cool roosts or shifting ranges northward are expected to prevent overall 

extinction of owls.

Plant species and community data

Within the studies that considered plant species and communities, estimates and 

predictions of future responses relied mostly on modeled projections relating either 

growth or habitat suitability to climate conditions. As with animal species, variations in scale and targets of the various assessments limit generalized conclusions 

for the SW. However, trends for species and communities tended to compliment on 

another. In general, tree species did well until reaching a temperature threshold beyond which trees and forested habitats are expected to decline. These patterns are 

more evident for high altitude areas, where temperature thresholds are generally 
USDA Forest Service RMRS-GTR-309. 2013. 77

lower, and for low elevation areas in the hottest part of New Mexico and Arizona. 

In particular, Engelmann spruce was predicted to experience declines in suitable 

habitat in multiple assessments. Interestingly, at a national level, SW forests are 

not expected to be the most vulnerable to climate change relative to other North 

American habitats. Still, at a smaller spatial scales, it is evident that tree species 

will undergo significant changes in habitat suitability as a result of climate change. 

Changes to forest systems may have wide reaching implications as the composition of these systems affect biogeochemistry and hydrological processes.

An important implication of these plant studies, particularly at those that look 

at the collective response of species to increasing temperatures (Hansen and others 

2001; Currie 2001) is the notion that there is a threshold effect where either species 

do well (e.g., richness increases) up to a certain temperature level or species within 

a region do well provided conditions are not already near a particular temperature 

limit. There are areas that may experience increases in biodiversity as warmer 

temperatures extend growing seasons and lead to range expansions of species and 

biomes (e.g. northward shifts of Sonoran and Chihuahuan deserts). Alternatively, 

areas that already experience hot and dry conditions (e.g., the SW) may experience 

temperatures beyond the tolerances of many native species. Ultimately, changes in 

biodiversity will vary across the landscape with variations in preexisting species 

and communities and the exact nature of temperature and precipitation changes.

5.3.2 Vulnerability of Southwestern Habitats

Neilson and others (1998) provided the earliest estimates for species composition change. In their national assessment, they compared MAPSS and BIOME3 

results under a variety of projections and assessed habitat change based on transient GCMs. These early climate projections are less xeric than those produced by 

later models. Perhaps the most interesting result of this analysis is their projection 

for an overall increase in Leaf Area Index (LAI, commonly used as a measure 

of productivity) in direct response to increased CO2. Though their predictions do 

not consider other factors commonly known to limit LAI, and are projected for 

a somewhat mesic future, these results are reflected in later analyses. Joyce and 

Birdsey (2000) took a different approach and presented a comparative analysis of 

trends and impacts of climate change on America’s forest communities (Fig. 5.3). 

This extensive report contains several chapters that outline major biomes shifts 

under various climate projections, addresses ecosystem productivity, and discusses 

the implications of climate change for carbon budgets as well as the forest sector. 

Bachelet and Neilson (2000) compared multiple simulations generated through 

MAPSS, BIOME2 and BIOME3, and DOLY from three distinct studies. Each 

study was conducted either at a regional or continental scale under varying climate change scenarios. Comparisons among DOLY, BIOME2, and MAPSS using 

projections from the Fourth Assessment Report (FAR) show a large degree of divergence between models. Ultimately, the results of their comparison showed that 

the strongest agreement centered on expectation for large decreases in the area of 

arid lands, which ultimately become grasslands. All models predict a decrease in 

runoff for all habitat types under all scenarios. However, their comparison also 

highlights the influence of CO2 on model performance where notable differences 

existed for the same model run with and without CO2 effects. Only two trends 

were consistent among the model results: where CO2 inputs were included, all 

models agreed that shrubland areas decreased; when CO2 effects were excluded, 

all models agreed that tundra and alpine habitats decline. MAPSS and BIOME2 

simulated loss of boreal forests and increases in savanna and grassland habitat. 
78 USDA Forest Service RMRS-GTR-309. 2013.

MAPSS also predicted a decrease in temperate forest and increase in savannas and 

grasslands in direct contrast to predictions by BIOME. With respect to arid lands, 

both DOLY and MAPSS simulated increases though DOLY produced the greatest 

increase in southwestern deserts.

Joyce and others (2008) reviewed 50 State Wildlife Action Plans to assess climate change as potential threat to wildlife terrestrial habitats. Using descriptions 

provided by the wildlife action plans they ranked areas along a gradient of high to 

low threat via a “climate stress index.” The index incorporates information on the 

degree of expected climate change as well as climate induced shifts in habitat area 

and quality and expected plant species response based on ecological assumptions 

regarding the effects of elevated CO2. Habitat specific analyses were conducted 

for Arizona, Minnesota, and Tennessee. From 189 studies that document climate 

related changes, Joyce and others (2008) identified multiple range expansions for 

mammals in Arizona and New Mexico, one bird phenology change in Arizona, 

and a single instance of mammal morphological change in New Mexico (Fig. 4.1). 

The authors also provided a detailed analysis that considers current land use practices, prevalence of at risk species, and expected change in total habitat area. They 

found that areas of greatest stress are associated with biome transitions zones 

and areas with high topographic relief. They also noted that areas expected to 

be most stressed by climate change did not overlap areas currently considered at 

high risk (due to fragmentation, urbanization, etc.), which might complicate future 

management priorities. The southern great plains were considered least sensitive 

according to their stress scale (northern Arizona and New Mexico scored high). 

Habitat-level analysis showed that species gains and losses were relatively rare 

events in Arizona though xeric habitats are identified as potential high stress areas. 

For Arizona, temperate mixed xeromorphic woodlands were considered at risk of 

Figure 5.3. Forest 

responses to two 

climate change 

scenarios (OSU, 

GFDL-Q at 10-km (fine 

grid) and 50-km (coarse 

grid) scales. From 

Chapter 4 of Joyce and 

Birdsey (2000).
USDA Forest Service RMRS-GTR-309. 2013. 79

complete loss, and the area covered by temperate coniferous forest, tropical thorn 

woodland, and warm temperate/subtropical mixed savanna is expected to increase. 

Temperate arid shrublands were among the most stressed habitat types but were 

also generally rarer.

Enquist (2008) and Enquist and Gori (2008) summarized data for New Mexico 

regarding climate effects on habitats and watersheds. Both studies are entirely retrospective but use the data to infer future vulnerability and are focused across the 

entire state of New Mexico. In their first report, Enquist and Gori (2008) used the 

number of drought sensitive species inhabiting an area and the magnitude of recent 

climate changes to compare the relative and likely vulnerability of New Mexican 

habitats. They identified 11 areas potentially vulnerable to climate change. Three 

areas, the Sierra San Luis/Peloncillo Mountains, the Jemez Mountains, and the 

southern Sangre de Cristo Mountains, were considered particularly vulnerable. 

They also identified 10 sites that were less vulnerable. These sites, despite harboring large numbers of drought-sensitive species, experienced lower climate 

exposure and include Bottomless Lakes, Bitter Lake, and Blue River/Eagle Creek. 

Sites with few drought sensitive species or little observed change in climate, such 

as the western plains of San Augustine, the Middle Pecos River, Saldo Creek, and 

Patura Grasslands, were not considered particularly vulnerable to future climate 

change.

Gonzales and others (2010) used observed and expected biome distributions to 

estimate vulnerability. Vulnerability represented a composite of the probability of 

change in climate and the degree overlap between historic and projected vegetation as generated using MC1 (Gonzales and others 2010). Their findings reinforce 

the notion that the SW has and will continue to undergo a relatively large change 

in temperature. At the global scale, temperate shrublands, temperate grassland, 

and deserts had high fractions of their coverage area within a vulnerable or highly 

vulnerable classification as determined by measures of observed change. Future 

projections and degree of overlap between current and future distributions were 

not substantially different for these habitats, however, and these vegetation communities were not among those considered most vulnerable.

Notaro and others (2012) simulated future changes in plant functional types 

in the SW using dynamic vegetation modeling. Their analysis, which considered 

high (A1) and medium (B1) emission scenarios under fixed and increasing (CO2 

enriched) CO2 for 17 GCMs provides several informative trends. First, vegetation 

is generally expected to decline. Second, some areas and vegetation types are more 

likely to experience loss. For example, deciduous forests in Utah and Colorado are 

expected to undergo the greatest declines. Spatial patterns of vegetation loss corresponded with the greatest expected declines in associated plant types. Grasses 

were predicted to increase into areas formerly occupied by deciduous trees at 

lower elevations but trees (evergreens) may expand at higher elevations. Third, 

regional changes in vegetation cover are influenced differentially by temperature 

and precipitation changes. Precipitation strongly influenced estimates of vegetation die off, particularly for grasslands. Conversely, tree cover was strongly and 

inversely correlated to temperature. Fourth, CO2 enrichment mitigates some of 

the expected declines in vegetation, especially for grassland habitats (as found for 

Neilson and other 1998; Joyce and others 2000). Loss of grass cover in the eastern 

Colorado, New Mexico, and northern Sonaran/Chihuahuan deserts were the most 

robust trends under fixed CO2 scenarios. These changes were much smaller under 

enriched CO2 conditions.

A more specific assessment was conducted for the Mojave and Sonoran Deserts 

in Arizona, Nevada, California, and Mexico (Comer and others 2012). Using an 
80 USDA Forest Service RMRS-GTR-309. 2013.

index-based system derived from the NatureServe’s CCVI (see Appendix 1) for 

species, Comer and others (2012) integrated estimates of climate exposure, change 

to disturbance processes, and species distribution models to develop a system for 

assessing a wide variety of ecosystem or community types. The authors also employed a series of workshops to incorporate expert opinions. For the purpose of 

their case study, the authors apply this system to 10 focal natural communities 

and provide estimates of vulnerability due to combined influence of indirect and 

direct climate change effects as well as adaptive capacity. The assessment is comprehensive and brings together estimates of the degree change in temperature and 

precipitation from average, climate envelope shift, forecasts of fire and hydrological events, and multiple indicators of current landscape condition as generated 

from a number of modeling techniques as well as a ranking system to integrate 

multiple effects into one indicator value. Climate change vulnerability is calculated by generating scores for direct effects, which represents exposure elements 

(e.g., climate change stress, BCE analysis of habitat change, and forecasts of disturbance processes) and a resilience score, which combines several elements of 

the indirect effect of climate change (e.g., invasive species, departures from disturbance regimes) as well as inherent characteristics that might lend the ecoregion 

greater adaptive capacity (e.g., biodiversity, narrow distribution).

Overall, three of eight communities within the Sonoran ecoregion, North 

American warm desert riparian woodland and stream, North American warm 

desert mesquite bosque, and Sonora-Mojave creosotebush-white bursage desert 

scrub had high climate change vulnerability (Comer and others 2012). The remaining communities, desert springs and seeps, Sonaran Palo verde-mixed cacti 

desert scrub, Sonora-Mojave mixed salt desert scrub, North American warm desert active and stabilized dunes, and Apacherian-Chihuahuan semi-desert grassland 

were considered moderately vulnerable to climate change. Each of the moderately 

vulnerable communities was considered highly sensitive to the direct effects of 

climate change but had a high resilience score, whereas, high vulnerability was 

associated with highly sensitivity to direct climate change effects but only moderately resilience scores. For the Mojave ecoregion, three of seven community types, 

Mojave mid-elevation mixed desert scrub, North American warm desert mesquite 

bosque, and North American warm desert riparian woodland and stream had high 

overall vulnerability scores (Comer and others 2012). Each of these community 

types had high sensitivity scores for direct climate effects and moderate resiliency 

scores. The remaining five community types, Sonora-Mojave mixed salt desert 

scrub, Sonora-Mohave creosotebush-white bursage desert scrub, Great Basin pinyon-juniper woodland, North American warm desert active and stabilized dunes, 

and desert springs and seeps were moderately vulnerable to climate change. The 

Sonora-Mojave mixed salt desert scrub and Sonora-Mohave creosotebush-white 

bursage desert scrub were moderately sensitive to direct climate effects and moderately and highly resilient, respectively, due to low risks of indirect impacts and 

generally good adaptive capacity. Great Basin pinyon-juniper woodlands and North 

American warm desert active and stabilized dunes were considered highly sensitive but also had a high resilience score. Community types received similar ranks 

across both ecoregions with the exception of Sonora-Mojave creostebush-white 

Bursage desert scrub, which was given moderate and high vulnerability for the 

Mojave and Sonoran ecoregions, respectively. Direct impacts of climate change 

were scored as vulnerable for this habitat within the Sonoran desert ecoregions, 

which appeared to be driven by less drastic shifts in temperature and precipitation 

and predicted distribution of this habitat within the Mojave region (see Comer and 

others 2012).
USDA Forest Service RMRS-GTR-309. 2013. 81

Summary and discussion: Habitat-level assessments

From the studies conducted for the SW, we found several indications for widespread shifts in many plant functional groups. As with species and community 

level analyses, most studies show a significant reduction in habitats associated 

with high elevations but increases in the area suitable for desert grassland and 

shrubland habitat. Shifts in suitable habitat were evident for most plant functional 

groups but results were strongly influenced by CO2, and temperature and precipitation changes with varying results across the SW region. The greatest confidence 

lies with predictions for expansions northward and up in elevation of most habitats 

and functional groups.

Studies of suitable habitat were difficult to compare because output targets 

varying time frames (see Table 5.3) and varying levels of biological organization 

(plant species versus plant functional groups versus habitat types) and are based 

upon a large and variable subset of model parameters (see chapter 3, Tables 3.1, 

3.2). For instance, the studies conducted for the SW and discussed or incorporated 

into vulnerability assessments are based on a subset of 12 different GCM outputs, 3 possible emission scenarios, and 8 unique vegetation modeling approaches 

(Table 5.2). In addition, CO2 has a strong but variable influence on models and its 

treatment across the current analyses is not consistent. Joyce and Birdsey (2000) 

provided the most comprehensive review of model output for a single habitat 

type—forests. It is clear from this work that the individual needs and expectations 

of a management problem will be important for identifying the relevance of a particular effort for informing management actions.

Habitat-level vulnerability assessments are useful for predicting impact of climate change on diversity patterns where specific taxonomic group are not well 

studied or in areas with high species richness (which creates a burden to conducting numerous individual assessments). Large-scale analyses tend to rely on 

modeling methods, which provide valuable tools to create and compare scenarios. 

However, the tradeoff in these types of approaches is that they are less suited for 

generating analyses of well-known taxa and results can be somewhat generalized. 

In addition, analyses that are done at the scale of a biome may not be able to distinguish between fine-scaled effects and cannot account for changes in community 

structure. In one example, the effect of reduced tree density on biomass as might 

occur from diebacks would not be modeled in habitat level studies because these 

changes do not change biome classification. It has been argued that such broadbrush approaches can lead to underestimations in rate of change (Gonzales and 

others 2010). Uncertainty is also harder to measure and mitigate for these analyses 

because it is incorporated through statistical means that can be rather obscure to 

interpret or even notice. Joyce and others (2008) observed variation in stress scores 

were more closely related to the GCM selected than either emission or ecological 

scenario assumptions, an interesting observation that reflects the impact of various 

methodologies.

5.3.3 Vulnerability of Southwestern Water

Water is important to arid regions such as the SW. Riparian systems typically 

support a disproportionate amount of the regional biodiversity. These systems 

are also likely to be strongly affected by changes in climate with a concordant 

disproportionate effect on surrounding landscapes and features (Fig. 5.4). The 

primary factor understood to determine vulnerability of water systems to climate 

change is its reliance on atmospheric versus ground water sources. The former are 
82 USDA Forest Service RMRS-GTR-309. 2013.

generally considered much more prone to issues with respect to changing precipitation regimes. We review nine assessments that consider the future vulnerability 

of Western or SW riparian and water systems to climate change.

Hauer and others (1997) provided the earliest focus on the Rocky Mountains 

and discussed biogeochemistry, fluvial characteristics, and the biota of river and 

lake ecosystems. Specifically, they identified research needs and strategies for 

dealing with the combined issue and influence of anthropogenic disturbance and 

climate effects. Their report provides useful information regarding past and more 

current conditions and hypotheses about future climate conditions with a particular 

focus on the relationship to hydrological and biological processes.

Meyer and others (1999) reviewed climate assessments conducted for freshwater systems across the United States in an attempt to gauge effects on goods 

and services. Though not a typical assessment, this review provides valuable insight as to the implications of climate change on future sustainability and it draws 

together a body of literature in order to compare the relative vulnerabilities of various regions and systems to climate change. The authors quantify the vulnerability 

of each region according to the extent of the expected effect and the context of 

the systems with respect to anthropogenic influences. Notably, these authors suggested that altered patterns of land use, water withdrawal, and species invasions 

may dwarf or at the very least exacerbate climate change impacts. Their review 

highlights several specific predictions:

• Shifts in distributions of aquatic insects (Sweeney and others 1992).

• Altered plant assemblages and changes in nutrient cycles (Meyer and Pulliam 

1992).

• Changes in sediment load and channel morphology (Ward and others 1992).

• Loss of some fish species (particularly from east-west oriented streams [Carpenter 

and others 1992]).

• Changes in hydrologic variability (e.g., the frequency or magnitude and seasonality of storm or flood events) may have a greater impact on aquatic systems 

than long-term changes in means.

• Vulnerability has different sources depending upon the system. For wetlands, 

changes in water balance increase vulnerability to fire and changes to greenhouse gas exchanges are the largest consequence. Streams will be more heavily 

Figure 5.4. Projected changes in 

median runoff for 2041-2060 

as compared to 1901-1970 

based on modest emission 

scenario. Grey lines represent 

water-resource region. 

Hatched areas indicate 

greatest confidence in model 

projections. From the U.S. 

Global Change Research 

Program, 3rd National Climate 

Assessment.
USDA Forest Service RMRS-GTR-309. 2013. 83

influenced by impacts on the riparian zone, species-specific thermal tolerances, 

and changes in flow regime.

Meyer and others (1999) identified the Arctic, Great Lakes, and Great Plains 

(particularly the prairie potholes) as vulnerable to climate change effects. For 

the Rocky Mountains, warmer temperatures are likely to lead to fragmentation 

of cold water fish habitat and change aquatic insect distributions. This region 

is overtly affected by human activities, however, so many of the climate effects 

may be dwarfed by ongoing issues (Meyer and others 1999). The Great Plains 

contain a variety of aquatic ecosystems under a strong east-west precipitation 

gradient. Warming is expected to dramatically influence the form of precipitation 

and timing of floods. As a result, the Great Plains and Prairie regions of the United 

States and California are considered particularly vulnerable to climate change. 

Increased salinity as a result of increases in evaporation rates, especially in the 

western Great Plains, is a leading factor predicted to lead to loss of endemic fish 

species, many of which are already near their thermal tolerance limit. The authors 

also noted that the arid SW should be considered vulnerable to climate effects 

but that the nature of predicted changes, which have a great deal of uncertainty, 

prevent accurate assessments. The arid SW is already characterized by its variable 

and unpredictable weather patterns, which may be one reason why the riparian 

systems in this region are so sensitive to changes in precipitation and, potentially, 

temperature. In addition, the isolated nature of many systems increases the vulnerability of endemic species to extinction as a result of climate change. Increases 

in salinity due to increase evaporation and reduced precipitation may exacerbate 

the rate of species invasions and lead to widespread changes in riparian structure 

and food webs (Meyer and others 1999). Interestingly, the authors considered the 

degree of change in flow components from current conditions to be more important in determining the extent of impact on streams than increased temperature 

along. Finally, changes to the periodicity of flow (from episodic to perennial or 

vice versa) are much more likely to result in irreversible changes and reduce ecosystem function in the short term.

In another review, Perry and others (2011) (Table 5.1) identified specific outcomes under a set of assumptions about future stream flow. Specifically, changes 

in streamflow are expected to reduce the abundance of native early successional 

species but favor greater herbaceous species and late successional and droughttolerant woody species. Climate changes will also reduce nutrient cycling and litter 

decomposition and, in general, decrease habitat quality for many species.

Winter (2000) used hydrological landscapes characteristics to determine climate 

change vulnerability and concluded that systems that rely primarily on rainwater 

are the most vulnerable. Specifically, vulnerability is related to the inherent capacity of the system to compensate (through reliance on other water sources) for 

variation in precipitation variables. This study does not, however, identify specific 

scenarios of exposure or identify the specific water bodies that are likely to be 

vulnerable. Rather, this acts as a measure of how and why a hydrological system 

is vulnerable to climate change, which then can be applied to classify systems. 

Winter (2000) classified six types of hydrological landscapes. Among these, wetlands in mountainous landscapes, wetlands associated with glacial landscapes and 

broad interior basins (playas) are predicted to be the most vulnerable. Vulnerability 

of wetlands in plateaus and high plains and riverine landscapes vary according the 

degree to which upper and lower regions depend upon precipitation and according 

to the size of the hydrological systems. Those that rely on precipitation (typical of 

upland areas) and are small are most vulnerable.
84 USDA Forest Service RMRS-GTR-309. 2013.

Christensen and Lettenmaier (2006) conducted a quantitative analysis to estimate the implications of future climate change on runoff for the Colorado River 

Basin. Though not a climate change vulnerability assessment as technically defined, this publication represents a comprehensive modeling effort for their study 

region and is the first to identify specific outcomes as a result of climate change. 

Christensen and Lettenmaier (2006) considered output from 11 GCMs under 2 

emission scenarios for 3 time periods. Most models predict increased winter precipitation and decreased summer precipitation and all showed substantial declines 

in runoff. The authors found evapotranspiration had greatest influence on runoff 

estimates and runoff declines were reflected in reservoir performance, which led to 

lost reservoir storage and declines in hydropower. The role of evapotranspiration 

in driving trends in runoff is likely to be important for determining system vulnerability in light of the conclusions of Meyer and others (1999) who argued that the 

changes in flow regime are most important for determining future condition.

A recent case study for the San Pedro National Conservation area in Southern 

Arizona, though based on a risk assessment approach, provides detailed information regarding issues related to climate effects for a SW system (Julius and others 

2006). The authors evaluated the influence of five climate scenarios on species, 

vegetation, and habitat suitability and linked vegetation data into ground water 

and surface water models to characterize evaporation processes. Results were generated by linking climate, hydrology, and ecosystems models to simulate future 

change and then incorporating land use stressors. They used historic weather data 

to create transient climate scenarios for the period 2003-2102 and created multiple possible future scenarios that were based on projections provided by SRAG, 

2000. Climate change clearly affected riparian communities leading to greater 

fragmentation of habitats and a transition to more xeric plant communities. Some 

variability existed for predictions of future recruitment of native species where 

winter precipitation timing and amount could lead to lower or higher than currently 

observed recruitment rates. The authors also determined a strong negative effect 

for avian biodiversity, particularly for currently abundant species.

Hurd and Coonrod (2008) focused within the SW and conducted analysis using 

models of streamflow and runoff. They also incorporated a land use component 

and estimated future agricultural and urban water demands. Their analysis shows 

that peak flow and total stream flow declines across both wetter and drier scenarios. Further, their results suggest increased monsoons will not offset effects 

of reduced snowpack in headwaters. Finally, over time, they model a pronounced 

shift to an early peak flow and significant shift in late winter runoff. This leads to 

greater reliance on reservoirs and aquifers. The southern reaches of the Rio Grande 

are likely to be more impacted by these effects.

In the second of three reports regarding New Mexico ecosystems, Enquist and 

others (2008) ranked watersheds in New Mexico according to two measures: magnitude of exposure and biological diversity. Exposure is estimated using climate 

data generated from the Climate Wizard site, which allows a user to map and analyze long-term trends in climate data. They also combined that information with 

recent trends in snowpack and water runoff patterns to generate an estimate in 

moisture stress (identified as a rate or trend). Biodiversity and, in particular, diversity of sensitive species was used as an indicator of importance of each watershed. 

In addition to important conservation species, Enquist and others (1998) identified 

species as sensitive if they were reported as a species of greatest conservation 

need by the New Mexico Department of Game and Fish’s State Wildlife Action 

Plan. In general, lower elevation watersheds have experienced greater drying than 

high elevation watersheds though about 93% of watersheds overall showed some 
USDA Forest Service RMRS-GTR-309. 2013. 85

decrease in moisture availability over the 1970-2006 study periods. In addition, 

there was a tendency to find more drying at drier watersheds (versus more mesic sites). Some watersheds appeared to experience less drying, primarily in the 

southeast quadrant of the state, for summer and fall seasons. Enquist and others 

(1998) found 45 to 109 sensitive species across individual watersheds. Though 

there were no significant trends overall, they did find a strong and significant relationship between increasing moisture stress and species richness when considering 

only the most species-rich watersheds. They identified the Jemez Cloverdale and 

Playas Lake watersheds as the most vulnerable due to the magnitude of observed 

moisture stress. The Pecos Headwaters, Upper Rio Grande, Upper Gila, and San 

Francisco watersheds have experienced little moisture stress but are species rich 

and were identified as potential targets for successful conservation. In their conclusion, Enquist and others noted that changes in climate and hydrology affect species 

in numerous ways and, within the SW, may be especially important where species 

face critical thresholds relating to metabolic and reproductive success (noted first 

in Burkett and others 2005 and Ryan and others 2008). The authors also identified two types of watersheds that should be focused upon: those that were found 

most vulnerable and those that are less vulnerable but ecologically important due 

to high levels of biodiversity. This study is particularly useful for summarizing 

trends specifically within the SW because it supports many of the suppositions of 

the previous reviews.

Theobald and others (2010) reviewed and analyzed threats to riparian ecosystems in the western United States. Again framed as a risk assessment that formally 

quantifies (by definition of RAP) risk, sensitivity, resilience, threat and vulnerability of the systems, this study warrants review here. The authors created “riparian 

threats score” integrating information from a three-prong analysis. They developed three scenarios that classify three system components—longitudinal, upland, 

and riparian zone—consisting themselves of multiple variables. Using geospatial 

data, models of runoff and sediment yield were generated and past and future scenarios of climate and land-use change were used to characterize landscape-scale 

processes influencing riverine and riparian areas. They also integrated two key 

stressors: urbanization and changes in climate (primarily precipitation). Similar to 

other analyses and assessments, Theobald and others (2010) expected decreases in 

flow for Rio Grande region due to increased discharge. However, they also predicted increased flow for Colorado and Great Basin Regions. Still, southern Arizona 

and New Mexico received very high riparian threats scores. Flow fragmentation 

was among the worst for watersheds in Arizona and New Mexico though these 

same watersheds were not among those with the highest degree of modified riparian area. Lower Arizona watersheds are expected to be most heavily impacted by 

future changes. Southern New Mexico watersheds were considered among those 

in the worst condition (furthest from natural setting) with respect to future increases in sedimentation. Additional considerations were also noted for riparian 

systems in the study region. For instance, higher order streams have most severe 

and widespread modifications (versus lower order). This study designed a broad 

approach method for scoring systems to identify high scoring riparian areas in 

need of further investigation at smaller spatial scales.

Most recently, the USDA Forest Service Climate Change Resource Center in 

collaboration with National Forest conducted a pilot watershed assessment of 11 

National Forests (Furniss and others 2012). The primary goal of this assessment 

was to link and integrate the relatively well known hydrological impacts of climate 

change with existing programs and policies currently applied to western National 

Forests. Each participating Forest independently assessed watershed vulnerability 
86 USDA Forest Service RMRS-GTR-309. 2013.

under a proposed National Watershed Vulnerability Assessment (WVA) framework 

that considered aquatic species, water uses, and infrastructure. Of particular interest to the current discussion is the assessment conducted in the Coconino National 

Forest (Steinke 2012). Using a step-wise approach based on USDA Watershed 

Analysis (USDA 1994), the authors compared vulnerability among five watersheds. In addition to the three variables of the WVA, Steinke (2012) included values 

relating to riparian and spring and stream habitats. Each watershed was given a resource value based on the number of sensitive fish and amphibian species, degree 

of anthropogenic disturbance (roads), and miles of riparian habitat. Exposure estimates of future temperature, precipitation, runoff and snowpack for years 2030 and 

2080 were generated using predictions from climate and the Variable Infiltration 

Capacity models provided by the Climate Impacts Group (CIG). Sensitivity was 

determined by considering current condition as well as natural sensitivities of 

watershed to changes in climate and flow parameter. Each element, water value, 

sensitivity, and exposure was then assessed by the pilot working team and scored 

as either low, moderate, or high. 

Composite scores and maps were 

then generated describing areas 

by these scoring values (Fig. 5.5). 

The greatest perceived issue arising from changes in climate dealt 

with decreased snowpack, to which 

watersheds above 6400 ft are most 

likely to be susceptible. In addition, 

high elevation sites tended to have 

high resource values and sensitivity scores and were generally found 

most vulnerable in this assessment. 

Considerable information is presented on specific results from this 

assessment in appendices of Furniss 

and others (2012).

Figure 5.5. Vulnerability of watershed (blue outline) as estimated by combining 

scored estimates of water resource values, sensitivity, and exposure.
USDA Forest Service RMRS-GTR-309. 2013. 87

Summary and discussion: Hydrological systems

It is clear from most assessments, and particularly those focused on SW systems, that there are wide ranging effects for the precipitation driven systems of the 

SW. Temperature alone drives these changes and precipitation effects are largely 

a matter of delaying or accelerating the consequences of increased temperatures. 

Riparian analyses and assessments invariably considered human land use change 

to determine future vulnerability. It is clear from a review of the literature that riparian and aquatic scientists consider human influences as a critical component of 

systems. Overall, higher elevation watersheds and those in southern New Mexico 

and Arizona were considered most vulnerable to climate changes. A cross-site 

analysis of three U.S. systems showed that the impact of climate change on an 

individual watershed was largely determined by the extent of its influence on current stressors, which directly relate to human use issues (Julius and others 2006). 

For the SW, water draw is the primary issue, and many of the assessments (e.g., 

Hurd and Coonrod 2008) show some reason for concern with respect to future 

water availability under warming scenarios. In addition, many SW systems rely on 

hydrological characteristics driven by seasonal precipitation patterns, in particular, 

winter snow pack. Given the relationship between river flow and riparian habitat, 

there seems a high probability that western riparian habitats will decline under 

future warming trends, particularly in the SW.

Scale influences the degree to which SW ecosystems are considered at risk of 

climate change effects. In their national analysis, Theobald and others (2010) considered SW riparian systems to be in a relatively undegraded state. However, this 

may be a matter of identifying the lesser of a suite of negative future expectations, 

and it is clear from their report that the future of these systems under the combined 

stress of human water use and climate induced changes to the hydrological regime 

will create substantial issues for species conservation and human consumption.

5.3.4 Comprehensive Assessments

Ojima and Lackett (2000) compiled climate change impact data for the entire 

United States into an assessment as part of the National Assessment Synthesis 

Team for the Global Change Research Program Report. This assessment reviewed 

knowledge and extrapolated information to identify areas of greatest concern. 

Many of the issues drawn from this report continue to be reflected in the studies 

and assessments identified in this synthesis of SW climate change vulnerability 

assessments. Though general in its applicability, this report succinctly summarizes 

ongoing issues with respect to climate change in the United States.

Ojima and Lackett (2002) assessed future impacts of climate change on both 

natural and social systems. Though not formatted as a traditional assessment, this 

report combines several elements that relate to aspects of vulnerability. Ojima and 

Lackett (2002) employed a stakeholder driven assessment where stakeholders 

from multiple economic sectors directed analysis of future vulnerability. A major 

product of this effort was to provide coping strategies for future expected changes. 

Both climate projections and response of vegetation were modeled (historical and 

GCM data for 2025-2034 and 2090-2099,VEMAP). Output from these efforts was 

used to inform workshops that were conducted to identify additional issues (socioeconomic), potential vulnerabilities, and coping strategies. This report is the 

second of a larger effort to assess future impacts and focused on agriculture, ranching/livestock systems, conservation, and natural areas and water (Appendix 2). 

From these efforts, Ojima and Lackett (2002) identified areas of greatest change. 
88 USDA Forest Service RMRS-GTR-309. 2013.

Their analysis shows that the greatest increases in winter temperature are expected 

along the western parts of the Great Plains, especially along the Front Range. They 

also identified elements that will experience the greatest impact. For instance, 

winter moisture will favor cool season invasive species, woody perennials, and 

sagebrush; will reduce shallow aquifer recharge; will change streamflow timing; 

and will have consequences for forage availability and quality and disease incidence. Warmer winters will affect pest outbreaks, soil organic matter, community 

composition, grass, and exotic invasions (leafy spurge and Japanese brome may 

move south). Summer temperature increases are likely to impact hail, the spread 

of invasive tree species, and fire. From the social perspective, farm/ranch families 

will experience modified vulnerability as a result of climate impacts to ecological 

and market systems. Water use competition will change and will affect human 

residence as well as natural resource management.

Archer and Predick (2008) reviewed issues affecting SW habitats to identify 

impacts of expected climate change for plant and wildlife species. They based their 

analysis on a logical progression of expected outcomes for increases in temperature and changes to habitats and hydrological regimes within the SW. For instance, 

water availability will become more limited and riparian areas will shrink resulting 

in a decline of native fish species. They also expected a decrease in net primary productivity to lead to decreased carbon storage and increases in erosion and 

nonnative species. Ultimately, they predicted upward elevational shifts in plant 

communities and conditions, which increase mortality for dominant woody vegetation and open the door for greater establishment of nonnative annual grasses. 

Saguaros are expected to decline but increasing CO2 may promote Joshua tree 

seedling survival leading to a shift in species dominance rather than loss of species. However, increased fires pose a major threat to this region’s plant species. 

The impact of interactions of climate with non-climate stressors was emphasized 

in this study and, in particular, the authors noted that fire management will be a 

priority, particularly in shrublands, woodlands, and areas invaded by nonnative 

plants and exposed to increasing urbanization. Livestock enterprises may also be 

challenged on many fronts: reduced forage, forage quality, and more physiological 

stress reduces summer weight gains (high temps) and potentially increases disease.

5.4 Future Assessment Needs

The SW United States is biologically diverse, mirroring the diversity of habitats 

and microclimates present within this region. There is a clear need for more assessments that address many of the unique and at-risk habitats within this region. 

Animal species were almost exclusively assessed with index based methods, which 

though useful, often lack spatially relevant data that could be used to prioritize areas for management actions. Species level analyses for the SW need more efforts 

that incorporate distribution modeling and site specific studies. In addition, most 

animal species assessed were rare or special status, which limits application to 

broader assessments of natural communities or ecosystems. Only one assessment 

addressed fish (Furniss and others 2012) and none addressed invertebrates (though 

see Appendix 2 for some studies). Plants were typically assessed with niche model 

analyses and in particular BCE analysis. These approaches have different target 

scales, which leaves gaps in our knowledge for many species and habitats. Future 

assessments that address multiple scales and combine several methods for measuring vulnerability would create a more balanced understanding of future conditions 

and greater translatability of data to solutions for resource managers. We also 
USDA Forest Service RMRS-GTR-309. 2013. 89

noticed some inequality in the target area of the assessments conducted to date. No 

species-level assessments addressed areas in Northern New Mexico and northern 

Arizona. Data for this region could only be derived from larger-scale analyses. In 

addition, most modeling efforts have been conducted for tree species. More assessments are needed that focus on native grasslands and, in particular, incorporate 

recent findings with respect to invasive grassland species. We also identify new 

potential focus areas for future assessment work. Many studies of grassland and 

rangeland systems note the potential for change in forage quality through changes 

in both plant species composition and quality. However, we do not currently have 

any assessments that explore these relationships under climate change. Impacts 

from larger-scale analyses appear to identify greater vulnerability in transitional 

habitats. There is a need for more research and assessment of the importance of the 

types of habitats or transient habitat dynamics for ecosystem function and species 

conservation.

Many estimates for future climate fall outside of observed extreme events (e.g., 

Currie 2006). For species in the hot dry climates of the semiarid and arid SW that 

are already near their physiological limits, small changes in climate variables may 

have a disproportionate effect on population status and overall species composition (Enquist and Gori 2008; Enquist and others 2008). Measures of vulnerability 

that examine response experimentally or using process-based models may provide 

better information for an extreme future than bioclimate models or response based 

on past observations. Future studies and assessments need to incorporate physiological limitations to better model impacts. There is also a general need to explore 

thresholds of ecological change for many of these species and systems.

We also did not find studies that integrate the indirect and direct effects of climate change in an effective manner. For terrestrial systems, little work has focused 

on the influence of pathogens and pests. However, Dale and others (2001) and 

McPherson and Weltzin (2000) (Appendix 1) considered the effects of pest and 

disease in their reviews of climate change issues. Many modeling procedures used 

for habitat and larger-scale analyses in the SW are not adequate for estimating 

future species response because they do not consider dispersal, species interactions, and extreme events. McKenney and others (2007) incorporated dispersal 

considerations albeit in a somewhat limited manner, but none considered competitive interactions. This is a particularly important topic given recent findings that 

indicate a growing issue for invasive grass species (Bradley 2009). Better regional 

models of climate change, multiscale models linking climate variability and ecological processes, and integrated assessment of the potential impacts will allow 

managers and conservationist to provide viable response options for alternative 

climate futures.

Similar gaps were noted for assessments of water resources. Greater emphasis 

on assessments of terrestrial-aquatic linkages (Meyers and others 1999) is needed 

since many of the predicted changes to aquatic systems are mediated by surrounding terrestrial landscape (nutrient load, silt, etc.). Meyers and others (1999) noted 

that future information needs include more extensive datasets and better models 

to link hydrologic regime with various ecosystem processes and interactions. In 

particular, Meyers and others (1999) suggested vegetation composition and hydrology within the riparian zones as critical targets. This sentiment is echoed in the 

Environmental Protection Agency’s report that considers climate change effects 

for three riparian systems in the United States (Julius and others 2006) as well as 

previous assessments of National Water Resources (Gleick and Adams 2000).
90 USDA Forest Service RMRS-GTR-309. 2013.

5.5 Using Assessments to Develop Adaptive Management Strategies 

for the Southwest

Vulnerability assessments are needed to better integrate climate change into 

current and future management plans. Through the assessment process, managers 

and researchers are able to pinpoint where and how species and habitats are likely 

to be influenced by global changes. Many current management practices, including 

restoration, reintroductions, and habitat preservation are based on the assumption 

that climatic variables will remain relatively stable. Climate change vulnerability 

assessments allow us to test this assumption and develop contingency plans where 

dramatic change is likely.

With increased knowledge, managers will also need more flexible planning 

strategies to allow them to focus on economically feasible actions (Chambers and 

Pellant 2008; Morgan and others 2008). This type of planning is likely to be better 

achieved through focus on output that identifies areas of greatest risk and recovery 

potential rather than on historic practices and knowledge (e.g., as is generated 

by state and transition models). This is especially true for the water sector where 

climate change is likely to result in widespread and irreversible changes to water 

availability and quality (Hurd and Coonrod 2003). Glick and others (2011) noted 

that future water management strategies will need to focus on the promotion of 

water conservation through protection and restoration of riparian areas and shifts 

in human land use practices. In addition, managers will increasingly need to consider the vulnerability not only of natural systems but of human systems. Turner 

and others (2003) detailed a framework for integrating environmental and human 

systems into traditional assessment processes.

Within the Forest Service, adaptive actions focus in the traditional areas of 

promoting ecosystem or habitat resilience and resistance under climate change as 

well as facilitating transition (FS Guide to Climate Change Scorecard; Peterson 

and others 2011). Climate change vulnerability assessments provide valuable 

information for managers who must determine the appropriate strategy for their 

natural resources. From the information generated by assessments of potential climate impacts and sensitivities, managers develop adaptation strategies (Fig. 1.1). 

Ultimately, managers must decide whether they are aiming to implement shortterm stop gap measures to prevent damage and improve ecosystem resilience, or 

to plan for inevitable long-term changes. Where resources are available and highly 

desirable resources are at risk, short-term measures to improve resilience and resistance are warranted and desirable. In these situations, management activities will 

focus on preserving and restoring current communities and promoting resilience 

to disturbance. These activities may include promoting genetic adaptation, minimizing the impact of other stressors such as drought or reducing the likelihood of 

catastrophic disturbance (Williams and others 2010). Vulnerability assessments 

can be used to achieve these goals by identifying targets that are the least vulnerable to future changes (refugia in the sense of Gonzales and others 2010) or most 

able to adapt to new conditions. Vulnerability assessments provide a mechanism 

for identifying priorities both within and across a landscape by identifying areas 

of sensitivity and importance (see Gonzales and others 2010 for discussion). For 

example, vegetation models can identify areas at risk of invasions by exotic species (e.g., Aldridge 2008) or native invasive species (e.g., Zavaleta and Royval 

2002); species-level assessments can point to additional climate related stresses 

for species already of conservation concern; and assessments can highlight opportunities to mitigate climate related impacts (e.g., Coe and others 2012; Bagne 
USDA Forest Service RMRS-GTR-309. 2013. 91

and others 2012). In the long-term, goals include strategies that focus more on 

sustaining ecosystem services such as clean air or water rather than conserving 

specific species or assemblages. This approach may become increasingly important as research points to the likely transition of many SW forest and habitats to 

new habitats under the combined pressures of climate and anthropogenic change. 

Management activities focused on assisting transition to a new state and potentially preserving some areas or communities that are expected to be reestablished 

elsewhere are examples of long-term approaches (Hansen and others 2001; Suttle 

and Thomsen 2007). Specific examples include replanting with drought-tolerant 

species, accelerating natural migration or dispersal of warm tolerant species, and 

transplanting schemes that use plants rather than seeds (Suttle and Thomsen 2007). 

Vulnerability assessments may also help form restoration strategies for areas most 

at risk of exceeding historic measures of variability and or be applied in analysis to determine the relative importance of lands for corridor preservation. The 

individual needs and interests of managers will determine the use of the information presented here. Climate change assessments are not meant to stand alone as 

a measure of future conditions but are meant to add to the body of knowledge on 

future threats and the sensitivity and strengths of individual components within 

the ecosystem. However, by nature, assessments are a valuable tool for identifying 

potential management intervention points. We hope that managers will use this 

report to locate potentially useful sources of information that are relevant to their 

own management plans.

5.6 Additional Information

The body of climate change literature and related assessment work is growing 

at an ever increasing rate from a diversity of disciplines. Within these assessments, 

there is considerable knowledge regarding potential climate change effects and 

we continue to improve the link between this knowledge of climate related impacts and decision-making processes (NCA 2011; Peterson and others 2011). The 

assessment process has its greatest value when it effectively evaluates potential 

responses to climate change and leads to improved planning efforts. Integration of 

climate change data is the first step toward developing effective adaptation options 

(Peterson and others 2011). However, variation in assessment methodologies and 

objectives creates challenges for managers and researchers who need information 

that both synthesizes data and provides comprehensive data on the vulnerability of 

biological systems and components.

The Forest Service Resources Planning Act assessment contains a number of 

chapters relating to climate change and synthesizes a great amount of literature 

regarding this topic (Joyce and Birdsey 2000). The USDA Forest Service Climate 

Change Science Center (http://www.fs.fed.us/ccrc/) is a repository of data, research, and tools for managing natural resources under a changing climate. In 

addition, many assessments are routinely conducted by the Forest Service at Unit, 

State, and Regional levels (e.g., Furniss and others 2012; various presented at 

http://www.fs.fed.us/ccrc/). Multiagency efforts are also generated in the form of 

Bureau of Land Management rapid ecological assessments. A series of resource 

based articles are available from Volume 21 of Climate Research (http://www.

int-res.com/abstracts/cr/v21/n3/), which focuses on climate effects in the SW. This 

journal contains several articles that review current and forecasted weather and 

hydrology for the region and includes vulnerability analyses for social issues such 

as urban water use and ranching (Ojima and others 2002).
92 USDA Forest Service RMRS-GTR-309. 2013.

Accessibility is an important requirement for the successful integration of vulnerability assessments into management plans. Products such as Scanning the 

Conservation Horizons, which reviews vulnerability assessment methods and 

provides several working examples, are integral to improving the utility of information derived from these efforts (Glick and others 2011). A recent effort by the 

United States National Climate Assessment report (2011) developed guidelines 

and standards to structure the 2013 National Climate Vulnerability Assessment 

(NCA 2011) and the resulting framework has applications beyond that of the NCA. 

Several publications, including this synthesis, are designed to bridge the sciencemanagement gap (e.g., Peterson and others 2011). Peterson and others (2011) 

provided a toolkit for applying adaptation options to a variety of situations.

Finally, interagency collaborations and working groups play an increasingly important role in the dissemination of climate science information. Many of 

these programs and organizations have demonstrated successful strategies for 

organizing science and management across diverse user and stakeholder groups. 

The Malpai Borderlands Group and United Nations Educational, Scientific, and 

Cultural Organization biosphere reserve program, are examples of collaborations 

among Federal, state, tribal, and private groups to conserve SW ecosystems. These 

organizations are often able to reach goals together that might be more difficult 

than when approached on their own. The Sustainable Rivers Project collaboration 

between the Nature Conservancy and the U.S. Army Corps of Engineers aims to 

marry river conservation and dam management (Theobald and others 2005). The 

Southwestern Climate Change Initiative brings together non-profit, tribal, state, 

and Federal organizations to generate research that identifies ongoing and future 

issues relating to climate change in the SW. The Department of the Interior has 

established several regional Climate Science Centers (CSCs) to improve coordinated research efforts to better address the needs of land management agencies. 

A primary goal of this organization is to improved forecasts of conditions as well 

as the assessment of climate change vulnerability, risks and uncertainties. Federal 

programs such as the recently implemented CSCs and the Landscape Conservation 

Cooperatives (LCC) aim to support development of research programs and tools 

that deal with ongoing management issues. The Southcentral and Southwestern 

CSCs are organizing science needs and research for the SW, and the Desert and 

Southern Rockies LCCs are coordinating the development of tools and science 

delivery to managers within this region.

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Appendix 1. Examples of specific programs and analysis methods (see Box 3.1) for 

models used to estimate species distribution (environmental envelope, simulation models) 

or species adaptive capacity (adaptive capacity models) in climate change vulnerability 

assessments. This list is not comprehensive but gives representative examples of different 

methods employed and commonly used programs. Examples of methods used in projects are 

presented in Table 4.2.

Type Examples Analysis Purpose

Target/

scope Source

Adaptive Capacity Models

Landscape 

Resistance Models

Landscape 

analyses

Estimates cost per pixel of land cover type 

as it relates to species presence. Translates 

into dispersal ability of species under various 

scenarios. Species with the greatest limitations 

have the least adaptive capacity. 

Species and 

ecosystems

Cushman and 

others 2006

RAMAS Viability 

analysis

Links geographic data (including land change 

data) to metapopulation models to generate 

population statistics.

Plant or 

animal 

populations

http://www.

ramas.com/

ramas.htm#gis

MigClim: 

Simulating 

migration under 

climate change

Machine 

learning 

Method

Cellular Automaton model that simulates plant 

dispersal under climate change and landscape 

fragmentation scenarios. Used in conjunction 

with envelope type analysis (requires maps of 

current and future suitable habitat distributions). 

Plants, user 

defined area

http://cran.rproject.org/

web/packages/

MigClim/index.

html

Dynamic Bioenergetic/ Ecophysiological Models 

Various Varies (see 

text)

Typically an analysis that links a bioenergetic 

or biophysical model with models of species 

traits relative to species extinction under 

climate change. Can be used to estimate 

species distribution or persistence under climate 

change. Depending upon the output, this 

approach may produce an estimate of species 

distribution or adaptive capacity.

Animal 

and plant 

species

Used by Buckley 

2008 for plants 

and animals and 

by Kearney and 

others 2009 for 

mosquitoes

Environmental Envelope Models

Genetic Algorithm 

for Rule-Set 

Prediction (GARP) 

Machine 

learning 

method

Niche model; uses spatial data on temperature, 

rainfall, and elevation with point data on species 

range to estimate potential range

Native and 

non-native 

species

nhm.ku.edu/

destopgarp

Maximum entropy 

(Maxent) 

Machine 

learning 

method

Habitat model; uses set of environmental 

variables and georeferenced occurrence 

(presence absence data) locations to produce 

models of species’ ranges

Animal 

or plant 

species 

http://www.

cs.princeton.

edu/~schapire/

maxent/

Statistical Species 

Distribution Models 

(SSDM)

Regression Predicts potential distribution of species and 

colonization based on 7 climate and 31 site and 

soil variables. 

Tree species, 

no temporal 

component

www.nrs.fs.fed.

us/atlas

SPECIES: Spatial 

Evaluation of 

Climate Impact on 

the Envelope of 

Species.

Machine 

learning 

method

Predict suitable habitat of species using neural 

network analyses

Species Pearson and 

others 2002
USDA Forest Service RMRS-GTR-309. 2013. 103

Type Examples Analysis Purpose

Target/

scope Source

Hydrological Models

Regional HydroEcologic Simulation 

System (RHESSys) 

Regression GIS-based hydro-ecological model simulates 

water, carbon, and nutrient flow

Watershed fiesta.bren.ucsb.

edu/~rhessys/

setup/downloads/

downloads.html

Sea Level Affecting 

Marshes Model 

(SLAMM)

Regression Models processes dominating wetland 

conversion and shoreline modification

Coastal areas http://www.

slammview.org.

Variable Infiltration 

Capacity

Physically based distributed hydrological model; 

suited for water/energy balance studies, so used 

extensively in climate change studies

River/stream/

reservoir 

systems

 Christensen and 

Lettenmaier, 2007

Dynamic 

Ecophysiological 

Models

Ecophysiological 

model

Models fish growth rates, algae production, and 

similar under various environmental conditions

Fish Keith and others, 

2008. 

Simulation/Process-Based Models

Biogeographic Models

BIOME, DOLY, 

MAPSS 

Regression Biogeographic equilibrium models that calculate 

plant available water and temperature thresholds 

according to climatic zone, life form, and plant 

type. 

Vegetation 

types; area 

defined by 

user

www.fs.fed.us/

pnw/corvaliis/

mdr/mapss

Biogeochemical Models

Instantaneous 

Canopy Flux Model 

(PnET) 

Regression Biogeochemical model that merges three 

computational models that simulate carbon, 

water, and nitrogen dynamics

Forest 

ecosystems; 

typical scale 

= 5 km

http://www.

pnet.sr.unh.edu/

download.html

Soil Organic Matter 

Model (CENTURY)

Regression Biogeochemical model that simulates nutrient/

hydrological flows and includes fire/harvest 

frequency on monthly scale

Watershed; 

typical scale 

= 5 km

www.nrel.

colostate.edu/

project/century5/

BIOCLIM (BIOMAP) Regression Prediction system (biogeochemical model) that 

uses mean monthly climate estimates to predict 

energy and water balances at specified location

Vegetation 

type; area 

defined by 

user

software.

infromer.com/

getfree-bioclimdownloadsoftware

DAYCENT Simulation 

model

Biogeochemical model that simulates nutrient/

hydrological flows and includes fire/harvest 

frequency on a daily scale. Daily version of 

CENTURY.

Vegetation 

types; 

watershed; 

typical scale 

= 5km.

www.nrel.

colostate.edu/

project/century5/

Dynamic Global Vegetation Model (DGVM)

LANDIS, LANDIS II Simulation 

Model 

Simulates dynamic interactions between 

vegetation, climate and disturbance. Can also 

incorporate dispersal as the probability of a 

seed moving to a new cell. Like other process 

based models, LANDIS can be used to develop 

projections of future distribution but does not in 

and of itself do so. LANDIS II relies on FIA data

Tree species 

age classes. 

Multiple 

scales though 

typically 

=10km. 

http://www.fs.fed.

us/pnw/mdr/

mapss
104 USDA Forest Service RMRS-GTR-309. 2013.

Type Examples Analysis Purpose

Target/

scope Source

MC1 Regression (DGVM) Combines biogeographic with 

biogeochemical models (CENTURY and MAPSS) 

and fire models to identify composition of plant 

groups under varying conditions. 

Plant 

physiognomic 

group; 

Multiple 

scales 

typically 

10km 

http//www.fsl.

orst.edu/dgvm

GAP models 

FOREL, JABOWA, 

ZELIG, FORSKA

Simulation 

Model

Also known as succession models. Simulates 

gap dynamics and stand dynamics of forests 

by using individual based models that simulate 

interactions and dynamics. Typically applied to 

mixed species forests. 

Individual 

and species 

level by patch

http://www.pikpotsdam.de/topik/

t6scs/safe/home/

forclim.html

Integrated Models

Tree Mig Simulation 

Model

Forest dynamics are simulated with a multispecies, height structured forest model which 

considers growth, competition, death as well 

as seed productions, seed bank dynamics, 

germination, and sapling development under 

various environmental conditions.

Tree species Lischke and 

others 2006

The Terrestrial 

Observation and 

Prediction System 

(TOPS)

Simulation 

framework

Simulation framework. Links historical climate 

data, remotely sensed data, climate projections, 

and species response (via distribution analysis) 

models.

Plant species Nemani and 

others 2009

FireBGCv2 Simulation 

model

Simulation modeling platform. Links mechanistic 

vegetation succession model (a spatially explicit 

fire model incorporating ignition, spread, and 

effects on ecosystem components) and a detailed 

fuel treatment module. FireBGCv2 dynamically 

simulates synergistic and interacting effects of 

weather and climatology, vegetation growth 

and succession, disturbance (e.g., wildfire, bark 

beetles), and land management (e.g., prescribed 

fire, thinning) on landscape structure and 

ecosystem processes.

Plant type Keane and others 

2011; Loehman 

and others 2011
USDA Forest Service RMRS-GTR-309. 2013. 105

Appendix 2. Studies that present analysis, theoretical context, and/or background data 

for potential species’ responses to future climate conditions, with an emphasis on literature 

relevant to the SW United States. “na” represent Not Applicable.

Author/Year Title Location Target

Archer and others 2004 Tree and shrub encroachment is a result of combination U.S. Trees and Shrubs

of climate, C02, fire regimes, livestock grazing

Audubon 2009 Birds and climate change: ecological disruption in U.S. Birds

motion

Austin and others 2000 An assessment of climate vulnerability in the Middle San Pedro River Social

San Pedro River

Bachelet and others 2001 Climate change effects on vegetation distribution and U.S. Nutrients, NPP

carbon budget in the United States

Bale and others 2002 Herbivory in global climate change research: direct Global Insect herbivores

effects of rising temperature on insect herbivores

Barron-Gaffin and others Temperature and precipitation controls over leaf- and Global Plant functional

2012 ecosystem-level CO2 flux along a woody plant type

encroachment gradient 

Bowers 2007 Has climatic warming altered spring flowering date of Sonoran Desert Shrubs

Sonoran desert shrubs?

Bradley 2010 Assessing ecosystem threats from global and regional Nevada Sagebrush habitat/

change: hierarchical modeling of risk to sagebrush Bromus tectorum

ecosystems from climate change, land use and invasive 

species in Nevada, USA

Crimmins and others 2009 Flowering range changes across an elevation gradient Arizona Plants

in response to warmer summer temperatures

Daly and others 2000 Dynamic simulation of tree-grass interactions for South Dakota Plants

global change studies

Early and Sax 2011 Analysis of climate paths reveals potential limitations Western U.S. 15 amphibians

on species range shifts

Ehlringer and others 1991 Differential utilization of summer rains by desert plant Utah Plants

Eisen and others 2007 A spatial model of shared risk for plague and hantavirus Southwestern Disease

pulmonary syndrome in the southwestern United States U.S.

Flather and others 2008 Geographic patterns of at-risk species U.S. T&E species

Goodrich and Ellis 2008 Climatic controls and hydrologic impacts of a recent Southwestern Water

extreme seasonal precipitation reversal in Arizona U.S.

Graham and Nobel 1996 Long-term effects of a coupled atmospheric CO2 Southwestern Agave

concentration on the CAM species Agave deserti U.S.

Guarin and Taylor 2005 Drought triggered tree mortality in mixed conifer California Trees

forests in Yosemite National Park, California, USA

Hunt and others 1991 Simulation model for the effects of climate change on U.S. Shortgrass prairie

temperate grassland ecosystems

Hurtt and others 2002 Projecting the future of the U.S. carbon sink U.S. Carbon

Jiguet and others 2006 Thermal range predicts bird population resilience to France Birds

extreme high temperatures

Johnson and others 2005 Vulnerability of northern prairie wetlands to climate Prairie pothole Waterfowl habitat

change region

Kearney and others 2009 Integrating biophysical models and evolutionary theory Australia Mosquitoes

Klopfenstein and others Approaches to predicting potential impacts of climate Northwest Armillaria root

2009 change on forest disease: An example with armillaria U.S. disease

root disease

Kranjcec and others 1998 The responses of three riparian cottonwood species to Western U.S. Cottonwood

water table decline

McCabe and Wolock 1999 General-circulation-model simulations of future Southern Rockies Water

snowpack in the western United States
106 USDA Forest Service RMRS-GTR-309. 2013.

Author/Year Title Location Target

McDonald and Brown 1992 Using montane mammals to model extinctions due to Western U.S. Vegetation/

global change mammals

Morgan and others 2011 Management Implications of global change for great Western U.S. Rangelands

plains rangelands

Patrick and others 2009 Physiological responses of two contrasting desert plant Texas Plants

species to precipitation variability are differentially 

regulated by soil moisture and nitrogen dynamics

Pierce and others 2008 Attribution of Declining Western U.S. Snowpack to Western U.S. Water

Human Effects

Poiani and others 1996 Climate change and northern prairie wetlands: Western U.S. Habitats

Simulations of long-term dynamics

Root 1988 Energy constraints on avian distribution and abundances U.S. Birds

Rowland and others 2011 Approaches to evaluating climate change impacts on na Plants, animal,

species: A guide to initiating the adaptation planning water

process

Schimel 2000 Contribution of increasing CO2 and climate to carbon Habitats

storage by ecosystems of the United States 

Seager and others 2007 Model projections of an imminent transition to a more Southwestern Climate

arid climate in southwestern North America U.S. projections

Sekercioglu and others 2008 Climate change, elevation range shifts, and bird Global Birds

extinctions

Sheppard and others 2002 The climate of the US Southwest Southwestern Climate 

U.S. projections

Skirvin and others 2000 Climate change and land tenure: potential impacts on Arizona Plant species in

vegetation and developments in the San Pedro River Sand Pedro River

watershed, southwestern Arizona 

Smith and others 2000 Elevated CO2 increases productivity and invasive Western U.S. Cheatgrass

species success in an arid ecosystem

Stewart and others 2004 Changes in snowmelt runoff timing in western North Western U.S. Water

America under a “business as usual” climate change 

scenario

Weiss and Overpeck 2005 Is the Sonoran desert losing its cool? Southwestern Climate

U.S. projections

White and others 2009 Past and projected rural land conversion in the US U.S. by region Socio-economic

at state, regional, and national levels Land use

Zavaleta and Royval 2002 Climate change and the susceptibility of U.S. 

ecosystems to biological invasions: Two cases of 

expected range expansion 

Schloss and others 2012 Dispersal will limit ability of mammals to track climate Western Mammals

change in the Western Hemisphere Hemisphere

Shafer and others 2001 Potential changes in the distribution of western North Western U.S. Tree and shrub

America tree and shrub taxa under future climate species

scenarios

Williams and others 2012 Temperature as a potent driver of regional forest drought na Tree productivity

stress and tree mortality and survival

Thompson and others 1998 A strategy for assessing potential future change in Western U.S. Vegetation and

climate, hydrology, and vegetation in the western water

United States 

Rehfeldt and others 2006 Empirical analyses of plant-climate relationships for Western U.S. Plant biotic

the western United States communities
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