United States Department of Agriculture

Forest Rocky Mountain General Technical Report

Service Research Station RMRS-GTR-383 September 2018

Socioeconomic Vulnerability to Ecological 

Changes to National Forests and 

Grasslands in the Southwest

Michael S. Hand, Henry Eichman, F. Jack Triepke, Delilah Jaworski
Cover photo: Desert and scrub vegetation encroaching into what was historically semidesert grassland in southern New Mexico (credit: F. Jack Triepke, USDA Forest Service).

All Rocky Mountain Research Station publications are published by U.S. Forest Service employees and are in the 

public domain and available at no cost. Even though U.S. Forest Service publications are not copyrighted, they 

are formatted according to U.S. Department of Agriculture standards and research findings and formatting cannot 

be altered in reprints. Altering content or formatting, including the cover and title page, is strictly prohibited.

Hand, Michael S.; Eichman, Henry; Triepke, F. Jack; Jaworski, Delilah. 2018. Socioeconomic vulnerability to ecological 

changes to national forests and grasslands in the Southwest. Gen. Tech. Rep. RMRS-GTR-383. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 100 p. 

Abstract

The flow of ecosystem services derived from forests and grasslands in the Southwestern United States may change in 

the future. People and communities may be vulnerable if they are exposed, are sensitive, and have limited ability to adapt 

to ecological changes. Geospatial descriptions of ecosystem services, projected climate-related ecological changes, 

and socioeconomic conditions are used to assess socioeconomic vulnerability to changes in the provision of ecosystem 

services by national forests and grasslands in the Southwest. Vulnerability is uneven in the Southwest due to varying 

projected effects of climate on forest ecosystem services, and different levels of exposure, sensitivity, and adaptive capacity 

of people in the region.

Keywords: ecosystem services, socioeconomic vulnerability, economic contribution, national forests and grasslands
Authors

Michael S. Hand is a Research Economist with the USDA Forest Service, Rocky Mountain Research Station in 

Washington, DC. He earned a B.S. degree in human resources and industrial relations at the University of Minnesota, and 

an M.S. degree and a Ph.D. in economics at the University of New Mexico.

Henry Eichman is an Economist with the USDA Forest Service, Ecosystem Management Coordination in Duluth, 

Minnesota. He earned a B.A. degree in biology at Colorado College and an M.S. degree in agricultural and resource 

economics at Oregon State University.

F. Jack Triepke is the Regional Ecologist and Air Program Manager, Forest Service, Southwestern Region in Albuquerque, 

New Mexico. He earned B.A. degrees in environmental biology and botany at the University of Montana, an M.S. degree in 

biology from Boise State University, and a Ph.D. in biology at the University of New Mexico.

Delilah Jaworski is the Regional Social Scientist and Climate Change Coordinator with the USDA Forest Service, Rocky 

Mountain Region in Lakewood, Colorado. She earned a B.A. degree in Middle Eastern studies at The George Washington 

University and an M.S. degree in environment and development at the London School of Economics and Political Science.

Acknowledgments

The authors wish to thank Richard Periman and Matt Turner for project support and guidance, Kristen Waltz and Jennifer 

Dobb for research support, and Kathleen Hawkos for help with geospatial analysis and map preparation.
ii

Executive Summary

Ecological changes have the potential to alter the provision of ecosystem services from national forests and grasslands in 

the Southwestern Region (comprising Arizona, New Mexico, and parts of Texas and Oklahoma) of the Forest Service, U.S. 

Department of Agriculture. People and communities who derive benefits from ecosystem services will be affected to varying 

degrees depending on their exposure, sensitivity, and capacity to adapt to ecological changes. 

This report describes an assessment of socioeconomic vulnerability to changes to ecosystems within national forests and 

grasslands in the Southwest. The assessment examines:

• Where in the region climate-related ecological changes are expected to alter the provision of ecosystem services and 

potentially interact with existing ecological stressors, 

• Who in the region is more or less dependent on ecosystem services provided by national forests and grasslands in the 

region, and 

• Where socioeconomic conditions are more or less conducive to adaptation to ecological changes. 

This assessment has been integrated with a previous assessment of Southwestern ecosystems that considered 

vulnerability based on the disparity between the characteristic climate envelope for each ecosystem type, and future climate 

predictions at the year 2090 (Triepke et al. 2014). Socioeconomic vulnerability of people and communities in the Southwest 

is determined by the degree of exposure, sensitivity, and capacity to adapt to changes in the provision of ecosystem 

services derived from national forests and grasslands.

Exposure

Exposure to ecological changes is assessed by pairing projections of climate-induced vegetative change with geospatial 

descriptions of existing disturbances and stressors (primarily wildfire hazard and insects and disease) and the supply of 

selected ecosystem services (outdoor recreation, forage for livestock, forest products, and surface water supply). Although 

variation exists between ranger districts within a forest, the forests with the lowest average exposure are Carson, Prescott, 

Santa Fe, and Gila National Forests. Highest average exposure is found in Kaibab and Lincoln National Forests.

Sensitivity

Sensitivity to ecological changes is assessed by examining which areas in the region currently experience economic 

contributions and benefits related to ecosystem services provided by national forests and grasslands. An input-output (I-O) 

model—which estimates production relationships among economic sectors within a region or economy—is used to describe 

the current market economic contributions of three types of national forest and grassland activities that may be exposed 

to ecological changes: outdoor recreation, livestock grazing, and forest products. Results indicate that about 10,000 fulland part-time jobs and $270 million in labor income in the region are derived from sensitive national forest and grassland 

activities annually. Although a small share of the Southwest’s regional economy, sensitive activities account for between 

1.0 and 1.5 percent of employment and income in some areas. Benefits derived from other ecosystem services provided by 

national forests and grasslands—such as water supply for residential and commercial activities, firewood for home heating, 

and cultural and spiritual uses—are discussed in qualitative terms. 
Adaptive Capacity

The assessment of adaptive capacity examines socioeconomic characteristics that may be associated with the ability of 

people to adapt to ecological changes. Conducted at the county level, the analysis summarizes several socioeconomic 

characteristics into index measures of relative adaptive capacity. The index values suggest that on average adaptive 

capacity is highest for national forests that are close to larger metropolitan areas, and lowest for forests that are more 

isolated from other economic activities. 

Management Implications

National forests and grasslands present a unique setting to examine socioeconomic vulnerability because the provision 

of ecosystem services from these lands is affected, in part, by management decisions made by a public entity (the Forest 

Service). This may provide public land managers with the opportunity to adapt to projected ecological changes by adjusting 

management plans and activities to avoid negative effects and take advantage of opportunities associated with climaterelated changes to public lands. The assessment results can be used by managers to inform forest planning efforts under 

the 2012 Planning Rule and to satisfy requirements of the Climate Change Scorecard. Results can be used in a variety 

of ways, including evaluations of tradeoffs, structured decisionmaking, and participatory decisionmaking. (Forest-level 

summaries of vulnerability are provided in the Appendix.)

Although the assessment does not endeavor to determine the benefits and costs of specific regional management activities 

and forest plans to respond to climate change, the results may be useful for prioritizing future activities and understanding 

which communities in the region may be more or less affected by ecological changes to forests and grasslands.
vi

Contents

Chapter 1: Introduction.............................................................................................................1

Defining Vulnerability ......................................................................................................................................2

Study Scope .....................................................................................................................................................3

Sources of Change: Ecological Changes to National Forests and Grasslands Due to Climate Change ....................3

Geographic Scope: Socioeconomic Impacts in the Southwest...............................................................................5

Relevant Ecosystem Goods and Services Supplied by National Forests and Grasslands .........................................5

Study Outline...................................................................................................................................................5

Chapter 2: Conceptual Model and Methods for Assessing Socioeconomic 

Vulnerability........................................................................................................................8

A Household Production Model of Vulnerability ..............................................................................................8

Exposure.............................................................................................................................................................11

Sensitivity ...........................................................................................................................................................11

Adaptive Capacity...............................................................................................................................................11

Method for Assessing Socioeconomic Vulnerability........................................................................................12

Step 1—Likelihood of Vegetative Changes as an Indicator of Climate-Related Ecological Change ..................12

Step 2—Economic Dependence on Ecosystem Services.................................................................................13

Step 3—Socioeconomic Indicators Related to Adaptive Capacity...................................................................14

Unit of Analysis..............................................................................................................................................15

Chapter 3: Climate-Related Changes to Ecosystem Services in the Southwest ..............16

Projected Forest and Rangeland Ecosystem Changes .....................................................................................16

Ecosystem Vulnerability—Analysis of the Likelihood of Vegetative Change.....................................................17

Disturbance Regimes.....................................................................................................................................22

Wildfire ..............................................................................................................................................................23

Insects and Disease.............................................................................................................................................26

Floods, Landslides, and Noxious and Invasive Plants ..........................................................................................28

Intersecting Risk of Climate Change Effects and Disturbances.............................................................................30

Uses and Roles of Ecosystems in the Southwest .............................................................................................30

Forage for Livestock ............................................................................................................................................31

Surface Water Supply..........................................................................................................................................34

Timber Products..................................................................................................................................................38

Recreation ..........................................................................................................................................................39

Composite Exposure of Ecosystem Services to Climate Change ...........................................................................44

Summary .......................................................................................................................................................44

Chapter 4: Economic Dependence on Ecosystem Services Supplied by 

Southwestern Region National Forests and Grasslands.............................................48

Assessing Market Dependence on Forest Resource Sectors by Using Input-Output Analysis...........................48

Economic Subsectors Related to Recreation, Rangeland Grazing, and Forest Products........................................50

Delineation of Forest Area County Groupings for Analysis of Economic Contributions........................................58

Forest Area Economic Contributions to Employment and Income of National Forest Climate-Sensitive 

Recreation, Rangeland Grazing, and Forest Products ..........................................................................................60

Dependence on Forest Resources not Measured in Market Transactions.........................................................60

The Importance of Water to Communities and Industry Sectors ..........................................................................60

Firewood Gathered for Personal or Residential Use.............................................................................................63

Cultural and Spiritual Values Associated with Southwestern Region National Forests and Grasslands..................64

Conclusions...................................................................................................................................................64
Chapter 5: An Indicator Analysis of Adaptive Capacity at the County Level.....................67

An Indicator Approach to Assessing Adaptive Capacity ..................................................................................67

Summarizing Socioeconomic Characteristics by Using Indices ......................................................................69

Data...............................................................................................................................................................70

Results...........................................................................................................................................................71

Summary .......................................................................................................................................................73

Chapter 6: Incorporating Socioeconomic Vulnerability Assessment Information 

Into Forest Planning and Adaptation Activities ............................................................76

Using the Socioeconomic Vulnerability Assessment Results for Forest Planning and Adaptation.....................76

Management Approaches to Planning for Climate-Related Changes to Forests and Grasslands.......................78

Inclusive Decisionmaking ...................................................................................................................................78

Adaptive Management Frameworks.....................................................................................................................79

Partnerships and Innovative Funding ...................................................................................................................80

Evaluating Tradeoffs ............................................................................................................................................81

Caveats and Limitations to the Assessment ..........................................................................................................83

References ...............................................................................................................................84

Appendix—Summaries of Forest-Level Socioeconomic Vulnerability Assessments ......94

Apache-Sitgreaves..........................................................................................................................................94

Carson ...........................................................................................................................................................95

Cibola............................................................................................................................................................95

Coconino.......................................................................................................................................................96

Coronado.......................................................................................................................................................96

Gila ...............................................................................................................................................................97

Kaibab ...........................................................................................................................................................97

Lincoln ..........................................................................................................................................................98

Prescott..........................................................................................................................................................98

Santa Fe .........................................................................................................................................................99

Tonto ...........................................................................................................................................................100

vii
USDA Forest Service RMRS-GTR-383. 2018 1

Chapter 1: Introduction

Healthy, well-functioning ecosystems produce a variety of life-fulfilling goods 

and services, known collectively as “ecosystem services.” Ecosystem services are defined as the components of nature, directly enjoyed, consumed, or used to yield human 

well-being (Boyd and Banzhaf 2007; Daily 1997) Although some authors maintain a 

distinction between ecosystem goods (i.e., tangible products) and services (i.e., the results of ecosystem processes that enhance things of value) (Brown et al. 2007), we will 

generally refer to them collectively as “ecosystem services,” and distinguish between 

the two categories as necessary. These services are essential to fulfilling basic human 

needs, including food, clean water and air, natural fibers and wood products, the regulation of pests and diseases, and recreation opportunities. Personal well-being derived 

from these services includes safety, the basic materials for a viable livelihood, health, 

social and cultural relations, and freedom and choice (McMichael et al. 2005: 45). The 

flow of ecosystem services facilitates social and economic vitality and contributes to the 

general well-being of people and households. 

Communities across the Southwest (hereafter, “the region”) rely on ecosystem 

services to support the social and economic livelihoods of their people. Yet forest and 

grassland ecosystems are likely to be altered due to a changing climate (IPCC 2007: 

14). As climate change alters ecosystem functions, the type and amount of ecosystem 

services provided by forests and grasslands will also change (Alig et al. 2011).

Research in the Southwest suggests that the climate in the region will continue to 

grow more variable (Gutzler 2013; Overpeck et al. 2013). Increased climate variability 

is anticipated to cause an increase in the occurrence and intensity of weather-related 

hazards (e.g., floods, droughts, heat waves, intense storms) and environmental disturbances (e.g., wildfires, pest infestations, invasive species). Changing hazard patterns 

and disturbance regimes will alter the natural landscape in the Southwest and change the 

quality and quantity of ecosystem services. The magnitude and types of changes to the 

provision of ecosystem services depend on the sensitivity of various ecological, social, 

and economic systems (Gallopin 2006; Luers 2005).

Managers of forests and grasslands face a daunting task in developing land 

management plans under a changing climate and changing socioeconomic conditions. 

Although a changing climate and its effects on ecosystem services will have broad 

impacts, not all people and communities will be equally affected (IPCC 2007: 12). A 

number of factors may determine the extent to which people are vulnerable to changes 

in a forest or grassland, including: proximity to the forest or grassland, reliance on ecosystem services, level of use of outdoor recreation opportunities, and exposure to natural 

hazards that are related to the ecosystem. Further, people and communities have different capacities to adapt to changes in supply of ecosystem services, mitigate potential 

negative effects, and take advantage of potential opportunities related to climate change. 

This study seeks to better understand the vulnerability of people and communities to climate-related forest and grassland changes in the Southwest. The analyses in 

this report are designed to provide information to managers to assist in the forest and 

grassland planning process, particularly in light of the 2012 Planning Rule (USDA 

Forest Service 2012b). Although this study is not designed to provide detailed analyses 
2 USDA Forest Service RMRS-GTR-383. 2018

of the socioeconomic impacts of specific planned activities, the results can be used to 

understand how climate change may alter the benefits people derive from forests and 

grasslands in the future, develop land management direction, help assess tradeoffs, and 

set management priorities.

This study addresses the following general research questions:

1. What are the roles of ecosystem services in local and regional economies and in 

community well-being in the Southwest?

2. What are the potential effects on economic outcomes and community well-being of 

changes in ecosystem services, and how do they vary across the region?

3. How can management actions be implemented to mitigate potential negative impacts 

of climate change on vulnerable human populations, and take advantage of potential 

positive impacts?

The study serves as a complement to a previous assessment of the vulnerability 

of major upland ecosystems of Arizona and New Mexico (Triepke et al. 2014). Based 

on the anticipated influence of climate change to vegetative site potential, ecosystem 

vulnerability was assessed and rated according to the departure of future climate from 

the climate envelope of a given ecosystem type at a given location. The socioeconomic 

assessment uses the ecological assessment results to indicate how projected changes to 

ecosystems may affect ecosystem services that are important for people and communities in the Southwest.

Defining Vulnerability

This assessment of vulnerability is concerned with identifying communities and 

geographic areas where ecological changes to forests or grasslands have the potential to 

adversely affect human well-being. People who are at greater risk of changes in wellbeing are considered more vulnerable to ecological changes. 

A working definition of vulnerability used by the Intergovernmental Panel on 

Climate Change (IPCC) can be adapted for the purposes of this study: The degree 

to which people or communities are susceptible to, and unable to cope with, adverse 

effects of climate-related ecosystem changes. The vulnerability of people and communities is a function of the character, magnitude, and rate of ecosystem changes to which 

they are exposed, as well as their sensitivity and capacity to adapt to ecosystem changes 

(adapted from IPCC [2007]). 

This definition of vulnerability is useful for assessing the degree to which people 

are susceptible to climate-related ecological changes in the Southwest because it encompasses the three components of vulnerability: exposure, sensitivity, and adaptive 

capacity. Exposure is the degree to which stressors or disturbances alter the provision of 

ecosystem services; sensitivity is the magnitude of change in well-being given a change 

in the provision of ecosystem services, and adaptive capacity is the ability of social and 

ecological systems to adjust to changes to mitigate potential damages, take advantage 

of opportunities, or cope with consequences (McCarthy 2001). In Chapter 2, these concepts are used to develop a conceptual model to describe socioeconomic vulnerability 

and inform the development of assessment measures. 
USDA Forest Service RMRS-GTR-383. 2018 3

Study Scope

People and communities will experience the effects of ecological change through 

numerous pathways. Only a subset of these will be relevant to public land management 

in the Southwest. The scope of this study is designed to offer insight on how people are 

likely to experience the effects of ecological changes to national forests and grasslands 

in the Southwest. To provide the most relevant information to managers at the regional 

and forest unit level, the study scope is limited along three dimensions: The source of 

change to ecosystem services, the geographic extent where those effects are experienced, and the set of relevant ecosystem services. 

Sources of Change: Ecological Changes to National Forests and Grasslands Due to 

Climate Change

Forests and grasslands in the region may be subject to many forces of change 

in the future (USDA Forest Service 2012a), and climate change will affect natural 

resources across ownerships and jurisdictions. The scope of this study is meant to provide insight on the management implications of ecological changes to national forests 

and grasslands. In this study, only the effects of climate change on ecosystem services 

derived from lands managed by the Forest Service in the agency’s Southwestern Region 

(Arizona, New Mexico, and parts of Texas and Oklahoma) (fig. 1.1) are considered. 

Figure 1.1—National forests and grasslands in the Southwestern Region (Forest Service Region 3).
4 USDA Forest Service RMRS-GTR-383. 2018

Climate change may affect ecosystem services through multiple pathways. In some 

cases, changes in ecosystem services are directly related to variations and changes in 

temperature and precipitation. For example, changes in precipitation patterns can affect 

recreation opportunities for skiing (Irland et al. 2001) and fishing (Morris and Walls 

2009). Other goods and services are related to climate impacts on soil conditions and the 

distribution of plant and animal communities (Huntley 1991; Kardol et al. 2011; Root 

et al. 2003). Disturbance regimes are also important, including interactions among disturbances that are related to climate change (Dale et al. 2001) and relationships between 

disturbances and vegetation (see Morris and Walls [2009] for a review). These pathways 

may in many cases be interrelated, and the provision of a given ecosystem service may 

be subject to changes through multiple pathways.

Of the many pathways by which climate change may affect the provision of ecosystem goods and services by national forests and grasslands, four primary pathways 

can be identified (fig. 1.2):

• Climate change directly affects the provision of ecosystem goods and services;

• Climate change increases the likelihood of vegetative change on the landscape, 

which alters the provision of goods and services that rely on the existing vegetative 

community;

Figure 1.2—Relationships between climate change 

and the supply of ecosystem goods and services.
USDA Forest Service RMRS-GTR-383. 2018 5

• Climate change alters disturbance regimes (e.g., fire, insects) that are important 

ecological processes for ecosystem goods and services;

• Climate change increases the likelihood of vegetative change on the landscape, 

which alters disturbance regimes that are important for ecosystem goods and 

services.

Alternatively, climate change can directly alter disturbance regimes, which can 

result in changes in vegetation and the provision of goods and services. (Note in figure 

1.2 that the connection between vegetative change and disturbances can flow in either 

direction.)

Geographic Scope: Socioeconomic Impacts in the Southwest

The geographic scope of all benefits derived from Southwestern forests and 

grasslands can range from onsite enjoyment of a particular recreation opportunity to 

contributions to global climate change mitigation through carbon sequestration. Some 

benefits from forest and grassland ecosystem goods and services accrue across State and 

international boundaries (e.g., provision of surface water to Mexico in the Rio Grande; 

regulation of smoke from wildfires on Southwestern forests migrating across multiple 

States). Other benefits and impacts of ecosystem services are likely to accrue only 

within the region.

National or global impacts are not included in this study. Although ecosystem 

services derived from national forests and grasslands in the Southwest potentially 

contribute to the well-being of people beyond this geographic area, it is likely that the 

largest and immediate impacts will be felt by people living close to the lands where 

changes are taking place.

Relevant Ecosystem Goods and Services Supplied by National Forests and Grasslands

Ecosystem services that are supplied by national forests and grasslands in the 

Southwest and that may be affected by climate-related ecological changes (table 1) 

range from tangible goods (e.g., water for consumptive use, biomass for wood products, 

forage for livestock), to services used in situ (e.g., recreation site access), to non-use 

values (e.g., valuing the existence of natural features). These ecosystem goods and 

services also vary in their relationship to markets. In some cases goods and services are 

tied closely to market transactions, whereas others may confer benefits with little to no 

market activity. The list of relevant ecosystem services is limited to those affected by 

one of the climate pathways (fig. 1.2) described earlier; for example, the provision of 

minerals is an ecosystem good provided by national forests in the Southwest, but it is 

not likely to be affected by climate change.

Study Outline

An assessment of socioeconomic vulnerability proceeds by conducting a regional 

analysis of each of the three components of vulnerability—exposure, sensitivity, and 

adaptive capacity. These three analyses are then combined to identify areas within the 

region that exhibit high, moderate, and low vulnerability to ecosystem changes. A broad 
6 USDA Forest Service RMRS-GTR-383. 2018 Table 1—Ecosystem goods and services relevant for national forests and grasslands in the Southwest. Ecosystem good or service Description Pathways that may affect provision Scope of beneficiaries Water supply Municipal, agricultural, commercial, and in-stream uses Direct – Reduced precipitation and increased temperatures Disturbance regimes – Increased incidence and severity of wildfires and erosion events Within region; onsite; downstream users (e.g., Texas and Mexico) Recreation opportunities Site access and availability for onsite recreation opportunities Direct – Reduced snowpack for skiing Vegetative vulnerability – Suitable habitat for game mammals, fish Disturbance regimes – Degradation or enhancement of sites due to wildfire Benefits derived onsite, but could accrue to local and non-local beneficiaries Forage for livestock Forage availability and quality on National Forest lands appropriate for grazing Vegetative vulnerability – Changes in range or extent of suitable forage Disturbance regimes – Incidence and severity of invasive plants Onsite use by within-region livestock operations Forest products – commercial use Commercial timber and biomass, wood for personal use, food and forage for personal use Vegetative vulnerability – Changes in range and extent of certain plant species Disturbance regimes – Increased bark beetle mortality; increased incidence and severity of wildfire Wood product market participants, ranging from local to global Forest products – personal use Fuelwood, food and forage Vegetative vulnerability – Changes in range and extent of certain plant species Disturbance regimes – Increased bark beetle mortality; increased incidence and severity of wildfire Mostly residents within the region, particularly those close to forests Air quality Forests as a source (dust, smoke) and a sink for emissions Disturbance regimes – Increased smoke from wildfires Local and regional residents within regional airshed Climate regulation Forests as a carbon sink and carbon sequestration option Vegetative vulnerability – Reduced biomass/carbon sequestration Disturbance regimes – Increased emissions from wildfire; loss of biomass due to bark beetle mortality Global Biodiversity and non-use species Plant and animal genetic resources; non-use values for plant and animal species (e.g., cultural, spiritual, and existence values); biomass input for forest products and recreation opportunities Direct – Loss of aquatic habitat due to reduced surface water flows; change in climate conditions for certain species Vegetative vulnerability – Change in range or extent of habitat Disturbance regimes – Wildfire; invasive plants; bark beetle mortality Values accrue to residents within and outside the region Spiritual, cultural, and historic significance Sites, species, and landscape characteristics that hold spiritual, cultural, or historic value Vegetative vulnerability – Change in range or extent of plants or habitat for animals for cultural and spiritual uses Disturbance regimes – Damage to cultural sites from wildfire Spiritual site users (within region); residents within and outside the region with cultural and historic ties to forests in the region Offsite amenities Viewsheds and landscape characteristics that hold aesthetic value that can be enjoyed offsite Vegetative vulnerability – Change in vegetative composition of land adjacent to private properties Disturbance regimes – Effects of wildfire on desirable viewsheds Residents adjacent or close to forests
USDA Forest Service RMRS-GTR-383. 2018 7

outline of the research design is described next, with specific details for each analysis 

described in the following chapters of the report.

Chapter 2 outlines the conceptual model developed to link changes in the provision of ecosystem goods and services to household well-being and vulnerability. Based 

on household production models, this model shows how potential changes in the quality 

or quantity of goods and services that are provided by ecosystems in the Southwest may 

affect the well-being of households across the region, and how these changes relate to 

vulnerability.

Chapter 3 assesses how climate-related ecological changes may affect the provision of ecosystem services that support well-being in the region. An analysis of the 

likelihood of climate-related vegetative change within the region’s national forests and 

grasslands is used to identify areas where climate change is likely to alter the composition of natural landscapes (Triepke et al. 2014). Geospatial data describing forest 

disturbances (such as fire and tree insect infestations) and uses of ecosystem services 

(such as municipal watersheds and livestock grazing allotments) are paired with the 

vegetative assessment to identify areas that may also be exposed to climate-sensitive 

ecological stressors and that are critical for providing benefits from ecosystem services.

Chapter 4 provides an analysis of regional economic dependence on ecosystem 

services derived from national forests and grasslands in the region. Subregional economic contribution models are created for each forest unit (the administrative unit for 

each of the 11 national forests and grasslands in the region) based on county-level data 

from an input-output model; each model is a grouping of counties where direct expenditures from a given national forest are likely to occur. The models are used to calculate 

the indirect and induced contribution—that is, multiplier effects—of direct expenditures 

to generating income and employment in each subregional area. 

Chapter 5 uses socioeconomic data to assess which areas in the region may have 

a greater or lesser density of households that can adapt to changes in the provision of 

ecosystem services. Building on a household production model of well-being, a set of 

socioeconomic indicators is summarized to indicate relative adaptive capacity across 

counties in the region. The county-level summaries of adaptive capacity are compared 

with the economic contribution analysis and the ecosystem services assessment.

The final chapter of the report discusses how management actions at the national 

forest unit level may relate to socioeconomic vulnerability. A set of management approaches is reviewed to highlight opportunities for mitigating negative impacts and 

enhancing positive impacts on well-being of climate-related changes. The broad results 

of the study are also discussed in relation to the 2012 Planning Rule and how managers 

may be able to incorporate an understanding of socioeconomic vulnerability into land 

management planning activities. Results for each forest unit are summarized in the 

Appendix.
8 USDA Forest Service RMRS-GTR-383. 2018

Chapter 2: Conceptual Model and Methods for 

Assessing Socioeconomic Vulnerability

Definitions of vulnerability and its components say little about how and why individuals or communities may be vulnerable. In order for these definitions to be functional 

for assessment purposes, a conceptual model is developed to link changes to ecosystem 

services and household well-being to the concept of vulnerability. An assessment 

method is then developed to relate observations of ecological, social, and economic 

characteristics of households in the Southwest to the factors that affect vulnerability.

A Household Production Model of Vulnerability

The goal of this assessment is to identify people and communities who may be 

susceptible to or unable to cope with adverse effects of climate-related ecological 

changes in the Southwest. The term “adverse effects” is interpreted as negative impacts 

of climate-related forest or grassland changes on household well-being or utility. 

However, the assessment method is designed to accommodate both negative and positive impacts on well-being. Examples of positive impacts on well-being are increased 

productivity of forage for livestock, or increased availability of certain recreation sites. 

A household that is considered vulnerable is more likely to experience a relatively larger 

reduction in well-being if a climate-related change occurs to a forest or grassland, and is 

less able to mitigate reductions in well-being through adaptive action. 

It is also useful to differentiate between marginal changes in well-being, and 

changes that can be identified as a discrete “adverse effect.” For example, smoke from 

a wildfire may cause some people to alter daily activities and run air conditioner units 

more frequently, which may reduce well-being. Other people with greater exposure or 

sensitivity to smoke may suffer increased disease or death as a result of cardiovascular 

and respiratory health problems. Although many people will be made worse off due to 

smoke, the latter group is considered more vulnerable than the former because morbidity and mortality represent a distinct departure of well-being from the status quo.

Discrete thresholds of well-being are an important concept for vulnerability assessments (see, for example, Luers 2005; Adger 2006; Nelson et al. 2007). For assessment 

purposes, vulnerable people and communities are those who are more likely to cross 

discrete thresholds of well-being that indicate a significant departure of well-being from 

the status quo. Thresholds associated with discrete outcomes (e.g., visits to the hospital, 

or evacuation of homes due to the threat of wildfire) that are observable or potentially 

observable are more readily quantifiable in an assessment than are marginal changes. 

Climate-related changes to ecosystem services are assumed to affect well-being 

through a household production function. A household production approach to ecosystem services has been used in a variety of contexts. Household production models 

can describe the role of environmental inputs in recreation decisions (Bockstael and 

McConnell 1981; Freeman et al. 2014), and are the foundation for estimating values 

for nonmarketed ecosystem services (Barbier 2000). Versions of the model have also 

been used to examine how forest products are used as natural insurance in developing 

countries (Pattanayak and Sills 2001; Völker and Waibel 2010), and how households in 
USDA Forest Service RMRS-GTR-383. 2018 9

developing countries rely on ecosystem goods and services to adapt to climate change 

(e.g., Fisher 2004; Fisher et al. 2010; Pramova et al. 2012). 

Ecosystem services are used as inputs to yield goods and services that households value (fig. 2.1). For example, fish in a forest stream are an input for producing a 

recreational fishing day, which may have value expressed in some households’ utility 

functions. Other inputs in the production process may include fishing gear and tackle, 

time, transportation, and access to the recreation site. 

A household production model recognizes that ecosystem services used as inputs 

may exhibit a range of values under climate change scenarios. As the provision of ecosystem services changes in the future, the ability of households to produce well-being 

and the likelihood of well-being falling below a discrete threshold will change (fig. 2.2). 

Increases in well-being (e.g., due to improved productivity of livestock forage) are 

interpreted as a reduction in the likelihood of crossing a well-being threshold and thus 

as a reduction in vulnerability. Conversely, decreases in well-being (e.g., decreases 

in productivity of livestock forage that necessitate purchases of supplemental forage) 

are interpreted as increasing the likelihood of crossing a well-being threshold and thus 

increasing vulnerability.

The components of socioeconomic vulnerability—exposure, sensitivity, and adaptive capacity—can be described in terms of how each relates to changes in household 

well-being and the likelihood of well-being falling below a threshold level given a 

change in ecosystem services. Each component is discussed in turn. Greater vulnerability is characterized by exposure to the risk of degraded ecosystem services, dependence 

on ecosystem services that support well-being, limited substitutability between ecosystem service inputs and other production inputs, and inelastic demand for ecosystem 

goods and services (fig. 2.3). 

Figure 2.1—Household production model for ecosystem goods and services.
10 USDA Forest Service RMRS-GTR-383. 2018

Figure 2.2—Changes in household 

production in the event of climaterelated ecological changes.

Figure 2.3—Components of socioeconomic vulnerability.
USDA Forest Service RMRS-GTR-383. 2018 11

Exposure

In a household production model, exposure captures the likelihood and magnitude 

of a change in the quantity or quality of ecosystem services. Exposure to ecological 

change effects on the provision of ecosystem services is a combination of two elements: 

The likelihood of ecosystems experiencing climate-related changes, and the magnitude 

of ecological effects associated with climatic changes (Gallopin 2006; Luers 2005). 

The magnitude and types of change in the provision of ecosystem services depend on 

relationships between ecological, social, and economic systems (Gallopin 2003; Luers 

2005). 

Ecological changes that affect exposure are considered exogenous to individual 

household or community decisions. For example, the extent of expansion in bark beetlerelated tree mortality under climate change in a given area is assumed to be a result of 

climatic, ecological, and economic forces, and not influenced by individual household 

decisions. However, different households may make decisions (e.g., where to live) that 

affect their exposure to climate-related risks.

Sensitivity

In the context of a household production model, sensitivity can be described as 

the degree to which households depend on ecosystem services to support well-being. 

Greater forest dependence implies that a given reduction in forest ecosystem service 

provision will have a greater effect on well-being, and result in a greater likelihood of 

well-being falling below a threshold.

Measuring ecosystem dependence may be difficult in some cases because 

household links to ecosystem services are often unobserved, particularly for non-use 

relationships (e.g., spiritual, cultural, and heritage values). For resource-use links (e.g., 

commercial resource development, forest product gathering, recreation), observations 

of forest product uses can allow for inferences about relationships that determine wellbeing. Examples of studies of these inferred connections are Fisher (2004), Fisher et al. 

(2010), Pattanayak and Sills (2001), and Völker and Waibel (2010).

Adaptive Capacity

Adaptive capacity encompasses the ability to engage in activities that either alter 

the risk of experiencing ecological changes or change dependence on ecosystem services (Murphy et al. 2015; Smit and Wandel 2006). In a household production model, 

adaptive capacity describes the degree to which households can alter the allocation of 

inputs to mitigate a decrease in well-being in response to an exogenous decrease in the 

quantity or quality of ecosystem services.

Households may adapt in several ways. For risks defined by geographic location 

(e.g., risk of wildfire), households may migrate to lower-exposure locations. Households 

may also make investments to reduce exposure to climate risks (e.g., installing central 

air conditioning, or investing in more efficient irrigation equipment), or make changes 

to consumption patterns to substitute non-ecosystem goods and services for goods and 

services derived from ecosystems in the Southwest.

The ability to make such changes is likely not to be uniform across households 

in the Southwest. Adaptive capacity can vary based on the economic resources, 
12 USDA Forest Service RMRS-GTR-383. 2018

technology, information and skills, infrastructure, institutions, and equity within 

Southwestern communities (McCarthy 2001). Adaptive capacity may be related to 

income and wealth and various forms of human and social capital, including knowledge, 

skills, abilities, and access to support networks. Health status, education, and traditional 

knowledge may also be important factors contributing to a household’s ability to adapt 

to changing ecological conditions (Smit and Wandel 2006).

Method for Assessing Socioeconomic Vulnerability

Despite widespread interest in ecosystem services, their link to human well-being 

is less well understood (Yang et al. 2015). The goal of an assessment of vulnerability 

is to identify households, communities, or other entities (e.g., counties) that are close 

to a vulnerability threshold and are relatively likely to cross a threshold. For example, 

a vulnerability assessment may attempt to identify which households in a coastal area 

are likely to experience a severe disruption from a hurricane due to exposure to coastal 

flooding, lack of insurance, and limited available community support resources. Adger 

(2006) describes a generalized class of vulnerability measures that compares well-being 

of observational units relative to a known threshold. However, well-being is usually 

unobservable, and thresholds may be unobservable as well.

Multiple methods exist to assess socioeconomic vulnerability depending on the 

underlying conceptual framework used to describe and define vulnerability, ranging 

from data-intensive quantitative approaches, to detailed case studies, to scenario building and participatory approaches that involve consultations with multiple stakeholders 

(Fischer et al. 2013; Murphy et al. 2015). Further, the types of information gathered by 

using a selected assessment method may fall into two general categories: (1) Profile 

information, which describes the characteristics of a community; and (2) process information, which describes how people in communities are related and interact (Fischer et 

al. 2013). This conceptual framework most closely resembles an “outcome-oriented” 

framework of vulnerability described in Murphy et al. (2015), which focuses attention 

on discrete exposure units (i.e., households) and the potential impacts of threats (i.e., 

climate-related changes to forest ecosystems). 

The assessment method applied in this study is a modified version of a method 

used by Thornton et al. (2008) and Emerich and Cutter (2011). The modified version 

applied here is composed of three steps: (1) assessment of the likelihood of vegetative 

change as an indicator of changing ecological conditions, (2) assessment of economic 

dependence on ecosystem services, and (3) assessment of socioeconomic factors related 

to adaptive capacity. This method corresponds to a combination of methods described 

in Murphy et al. (2015), primarily the “dose-response” and “indices and indicators” 

methods.

Step 1—Likelihood of Vegetative Changes as an Indicator of ClimateRelated Ecological Change

An important aspect of assessing vulnerability is determining where climate-related changes to ecosystems are more and less likely to affect the provision of ecosystem 

services. The approach taken here is to examine the location of potential changes in the 
USDA Forest Service RMRS-GTR-383. 2018 13

Southwest by conducting a fine-scale analysis of likely vegetative change due to projected climatic changes in the region. 

Triepke et al. (2014) have used a fine-scale approach to examine how projected 

shifts in the distribution of temperature and precipitation may affect the pattern of 

vegetation currently on the landscape. This approach relies on comparing the historical 

range of climate variables associated with different vegetation types with projected 

ranges of climate variables across the region. Areas at high risk of vegetative change are 

those where the projected range of climate variables is unlikely to support the existing 

vegetative theme in that area. In this study we examined where projected climate-related 

vegetative change intersected with geospatial information on the occurrence and severity of ecological disturbances, including wildfire and tree mortality due to insects and 

disease. This intersection can indicate where ecological changes are likely and may 

exacerbate existing ecological stressors. 

Exposure was assessed by identifying areas important for the provision of selected 

ecosystem services—recreation opportunities, forage for livestock, forest products, 

and surface water—using geospatial, administrative, and survey data. High exposure 

to climate-related changes to ecosystem services is defined as the intersection of high 

likelihood of vegetative change, the presence of disturbance hazards, and importance for 

the provision of ecosystem services. 

Two caveats are necessary to put the exposure analysis in context. First, the analysis does not directly model potential changes in the provision of ecosystem services. 

Rather, likely changes in vegetation and natural resource availability are used as an 

indicator of where changes in ecosystem service provision are likely to occur. Second, 

the underlying assessment of ecosystems in the Southwest identified only the likelihood 

of vegetative change, not what type of vegetation is likely to replace the current state. 

Nor did the vegetative analysis identify the type and magnitude of ecosystem services 

that will flow from the area in the future. For example, the analysis does not incorporate 

potential changes in vegetative productivity independent of changes in vegetation type. 

However, the provision of ecosystem services is dependent on the underlying ecosystem 

functions that result in different types of vegetative cover, as well as the type of vegetation itself. Thus, projected changes in vegetation can at least indicate areas where the 

provision of ecosystem services is likely to change.

Step 2—Economic Dependence on Ecosystem Services

Households in the Southwest may depend on ecosystem services to support wellbeing in many ways. They may receive income from markets for products, use natural 

areas for recreation, or gather forest products for personal use, among others. This 

study considers dependence in two ways: economic contributions that can be measured 

through market transactions of ecosystem services, and reliance on ecosystem services 

that are not closely linked to market transactions. 

The economic contribution analysis approach identifies how much economic 

activity (measured in employment and income) in the region can be attributed to 

expenditures that are related to ecosystem services flowing from national forests and 

grasslands. Relevant ecosystem services include provision of timber and wood products, 

forage for livestock, and recreation opportunities. Provision of these three resources 
14 USDA Forest Service RMRS-GTR-383. 2018

accounts for the majority of market transactions involving ecosystem services that are 

provided by national forests and grasslands in the Southwest. 

Economic contributions were calculated from economic relationships specified in 

a set of regional economic models using IMPLAN (IMPLAN 2013). The contribution 

analysis indicates the current economic link between ecosystem services provided by 

national forests and grasslands and subregional economies where those services have 

the greatest market impact. For each national forest and grassland in the region, a set 

of counties was identified where firms and households make expenditures derived from 

forest and grassland activities. These counties were defined as a subregional forestrelated economy for each national forest and grassland. By using the relationships 

between economic sectors in IMPLAN, the contribution of forest and grasslands 

expenditures (indirect, and induced employment and income) was calculated for each 

subregional economy. Contributions were calculated for three categories of ecosystem 

services: recreation, rangeland, and timber and wood products.

People in the Southwest also depend on ecosystem services to support well-being 

in ways that are not fully captured by employment and income contributions of forestand grasslands-related transactions. For example, the economic contribution analysis 

can indicate how changes in recreation opportunities may affect people in the region 

whose employment and income depend on recreation expenditures. Yet it does not account for the effect on well-being for people engaged in recreation. 

Ecosystem services have a range of connection to market transactions, including 

some with limited or no connection to market transactions, such as the supply of surface 

water by forests, offsite amenities enjoyed by nearby residents, firewood and other forest products gathered for personal use, and cultural and spiritual values. Regionwide 

data describing the demand and supply of these services vary; sensitivity to changes 

in these services is discussed qualitatively and supplemented by descriptive data when 

possible.

Step 3—Socioeconomic Indicators Related to Adaptive Capacity

Adaptive capacity describes the ability of households or communities to avoid 

negative impacts from ecological changes. Although many of the adaptations that people can engage in are observable, the capacity or ability to engage in adaptations may 

be difficult to observe before they occur. This study examined household characteristics 

that are commonly associated with adaptive capacity, and used these characteristics 

to identify geographic areas that may have a greater capacity to withstand a negative 

change to ecosystem services and avoid reductions in well-being. 

A set of socioeconomic characteristics was identified that can be related to the 

conceptual model of adaptive capacity. Data were drawn primarily from those reported 

at the county level in the U.S. Census. The characteristics were summarized by using 

indexing methods to generate indicators that describe average differences between counties in socioeconomic characteristics associated with adaptive capacity. Counties with 

greater adaptive capacity were defined as those with higher values of socioeconomic 

indicators relative to other counties in the Southwest.

A potential shortcoming of index summarization methods is that they can overemphasize socioeconomic demographic characteristics that are easy to observe, and 
USDA Forest Service RMRS-GTR-383. 2018 15

deemphasize other more subtle factors that contribute to vulnerability, such as institutional capacity, community, and social capital (Kelly and Adger 2000). This may limit 

conclusions about adaptive capacity that can be drawn from the indices as they are 

developed here because they only reflect differences in socioeconomic status. Indices 

can be helpful for generating a descriptive profile of different communities; other approaches, such as case studies and participatory community engagement (Fischer et al. 

2013) or in-depth interviews and focus groups (Paveglio et al. 2015), may be needed to 

gather information on social processes and identify different types of communities that 

may respond differently to ecological changes. 

Unit of Analysis

In order to be relevant for land management planning efforts within the 

Southwestern Region, the primary unit of analysis for the vulnerability assessment is defined by the 11 national forest and grassland administrative units within the region. The 

goal of the assessment is to provide socioeconomic vulnerability information for each 

management unit that can assist in developing unitwide management plans and prioritizing responses to climate change within each unit.

Data used in the assessments are available at a variety of spatial scales and reporting units: fine-scale geospatial representations of vegetation, watersheds, rasterized 

outputs from ecological models, subunits of national forests and grasslands (such as 

ranger districts and grazing allotments), and groupings of counties. Although the primary unit of analysis is relatively coarse, in some cases it is feasible to report assessment 

results at finer scales to provide additional detail about geographic variations within a 

management unit or to assist with planning for subunits. 
16 USDA Forest Service RMRS-GTR-383. 2018

Chapter 3: Climate-Related Changes to Ecosystem 

Services in the Southwest

Communities in the Southwest depend on national forests and grasslands for the 

provision of a variety of ecosystem services. The provision of these ecosystem services 

depends on climatic and landscape conditions that support a variety of biological functions and processes. A changing climate may alter existing biological functions and 

processes, and thus the existing supply of ecosystem services derived from forests and 

grasslands. Climate change may also intensify natural and anthropogenic disturbances 

that threaten the provision of ecosystem services provided by the region’s forests and 

grasslands.

This chapter assesses risk to the provision of ecosystem services from 

Southwestern forests and grasslands due to projected climatic changes. Forest areas that 

provide several types of ecosystem services are identified by using geospatial data. The 

likelihood of changes to the provision of these ecosystem services is based on climate 

projections and the likelihood of vegetative change.

Four broad categories of ecosystem services are identified here: forage for 

livestock (grazing), municipal water supply, timber products, and outdoor recreation 

opportunities. Geospatial data describing the uses or potential provision of these ecosystem services are used to identify areas managed by the Forest Service that are important 

for supplying ecosystem services. Other ecosystem services, such as cultural and spiritual opportunities, and risks to their provision due to climate change are also discussed. 

However, comprehensive regionwide datasets describing the geographic extent of these 

services were not available. 

This chapter first describes an ecological assessment of the likelihood of vegetative change due to climate change across the region and its relationship to prominent 

disturbance regimes. Existing uses or provision of ecosystem services is then examined 

relative to vegetative change and current disturbance regimes. These discussions 

build our contextual understanding of climate change, disturbance regimes, and the 

dependence of communities on ecosystem services provided by national forests and 

grasslands in the Southwest. 

Projected Forest and Rangeland Ecosystem Changes 

The provision of ecosystem services by national forests and grasslands depends 

on complex interactions among climatic conditions (i.e., patterns of temperature and 

precipitation), site-specific conditions, ecological processes and disturbances, plant 

communities, wildlife populations, and human activities. Detailed modeling of how 

the provision of ecosystem services may change in the future is challenged by multiple 

interacting systems and a lack of available data at appropriate scales. But broad-scale 

assessments of changes in conditions that affect ecosystem services can indicate the 

direction and magnitude of potential changes, if not the precise nature of ecosystem 

services, in the future.

Broad-scale assessments suggest that climate change may have significant impacts 

on ecosystems in the Southwest (e.g., Cole 2010; Comer et al. 2012; Enquist and Gori 
USDA Forest Service RMRS-GTR-383. 2018 17

2008; Bagne and Finch 2012; Friggens et al. 2013; Rehfeldt et al. 2012). Exposure of 

ecosystem services to climate-related changes suggests that climate change has the 

potential to greatly alter well-being of people and communities in the region. Average 

annual temperatures are expected to increase by between 2.5 and 5.5 oF by 2070, with 

potential for greater increases by the end of the century (Garfin et al. 2014). Higher 

temperatures, particularly in summer, are likely to lead to a more arid future in the 

Southwest (Seager et al. 2007) and increase the severity and extent of drought conditions (Gutzler and Robbins 2010). Mountain snowpack is expected to decrease due to 

greater incidence of precipitation falling as rain (McCabe and Wolock 1999), resulting in earlier spring runoff and lower streamflow (Garfin et al. 2014; Gutzler 2013). 

However, uncertainty remains about future climatic conditions and climate-related 

ecological change.

General relationships between climatic changes and ecosystem service provision provide some insight on potential socioeconomic impacts of ecological change. 

A change in temperature is itself sufficient to drive ecological changes (Williams et 

al. 2013) (see Friggens et al. 2013 for a review). Drought and heat stress can lead to 

increased tree mortality (Williams et al. 2010) and create feedback mechanisms with 

other ecosystem processes (e.g., insects and wildfire) (Allen et al. 2010). Decreases in 

snowpack are likely to have negative impacts on ecosystem services that directly rely on 

snow (e.g., winter recreation) and streamflows (e.g., surface water for irrigation). 

A shortcoming of climate and ecosystem assessments at the regionwide scale 

is that it can be difficult to draw inferences about effects to ecosystem services at the 

subregional level. In particular, uncertainty about future conditions, about ecological 

responses to climate change, and about human adaptation to changing ecological conditions within a region combine to make it difficult to predict vulnerability with precision. 

But local assessments of climate impacts may be critical to management and planning 

(Williams et al. 2010). 

Ecosystem Vulnerability—Analysis of the Likelihood of Vegetative 

Change

The method used by Triepke et al. (2014) to assess potential climate-related 

changes to ecosystems on national forests and grasslands relied on downscaled climatological projections paired with spatially detailed ecological data on the distribution 

of ecosystem types across the region. By segmenting national forest and grassland area 

into individual plant communities and assessing the climatic conditions that support 

each community, downscaled climate projections were used to predict vegetative change 

at a fine spatial resolution. 

Each segment was assigned to 1 of 31 ecological response units (ERUs) identified for major upland ecosystems in the Southwest (Wahlberg et al. 2016) (table 2). 

Individual segments were also assigned likelihood of change ratings based on the 

departure of future climatic conditions from climatic conditions that support current 

vegetation (see box 1 for a description of data, climate models, emissions scenarios, and 

methods used to develop the ratings). Segments where future climatic conditions are 

projected to diverge significantly from the climate envelope of their respective ecosystem type were rated at higher likelihood of vegetative change. If future conditions were 
18 USDA Forest Service RMRS-GTR-383. 2018

Table 2—Ecological response units (ERUs) for major upland ecosystems of the Southwest.

Ecological response unit ERU subclass ERU code System type

Spruce-Fir Forest

SFF forest

Spruce-Fir – Lower SFM forest

Spruce-Fir – Upper SFP forest

Bristlecone Pine BP forest

Mixed Conifer with Aspen MCW forest

Mixed Conifer – Frequent Fire MCD forest

Ponderosa Pine Forest

PPF forest

Ponderosa Pine/Bunchgrass PPG forest

Ponderosa Pine/Gambel Oak PPO forest

Ponderosa Pine – Evergreen Oak PPE forest

Pinyon-Juniper [PJ] Sagebrush PJS woodland

PJ Evergreen Shrub PJC woodland

PJ Woodland

PJO woodland

PJ Woodland – Cold PJOc woodland

PJ Woodland – Mild PJOm woodland

PJ Grass

PJG woodland

PJ Grass – Cold Temp PJGc woodland

PJ Grass – High-Sun Precip, Mild PJGmHS woodland

PJ Grass – Low-Sun Precip, Mild PJGmLS woodland

Juniper Grass

woodland

Juniper Grass – Cold JUGc woodland

Juniper Grass – High-Sun Precip, Mild JUGmHS woodland

Juniper Grass – Low-Sun Precip, Mild JUGmLS woodland

Madrean Encinal Woodland MEW woodland

Madrean Pine-Oak Woodland MPO woodland

Montane/Subalpine Grassland MSG grassland

Colorado Plateau/Great Basin Grassland CPGB grassland

Semi-Desert Grassland

High-Sun Precip SDGhs grassland

Low-Sun Precip SDGls grassland

Piedmont Grassland PFG grassland

Foothill Grassland FHG grassland

Semi-Desert Lowland Grassland (also 

called

Chihuahuan Semi-Desert Grassland)

SDLG grassland

Sandy Plains Grassland (also called 

Chihuahuan Sandy Plains Grassland) SPG grassland

Alpine and Tundra ALP shrubland/mixed

Mountain Mahogany Mixed Shrubland MMS shrubland

Gambel Oak Shrubland GAMB shrubland

Sagebrush Shrubland SAGE shrubland
USDA Forest Service RMRS-GTR-383. 2018 19

projected to be similar to conditions supporting current potential vegetation, the likelihood of vegetative change was rated as low. Results for each plant community segment 

were then aggregated to subregional scales.

The downscaled assessments of likelihood of vegetative change are useful for 

identifying areas that may be susceptible to climate impacts that can affect the provision of ecosystem services. The ecosystem assessment did not explicitly model how 

climate change (or climate-induced vegetative change) affects ecosystem functions and 

the provision of goods and services. Rather, it can aid in describing the probability of 

ecological change and, for our purposes, potential effects to the supply of ecosystem 

services. 

An advantage of this approach is that it provides a spatially detailed assessment 

that covers nearly the entire region. Further, because the ecological assessment is tied 

to vegetation, it provides an important link to climate-induced change for ecosystem 

services that primarily rely on particular vegetation types (e.g., timber products and 

forage for livestock). Disadvantages of this approach include limited ability to make inferences about ecological functions in the future and the magnitude of potential change 

in provision of specific ecosystem services. For example, the hydrological functions are 

correlated with the distribution of vegetation types on the landscape (Stephenson 1998); 

the likelihood of vegetative change assessment can identify areas where hydrological 

functions are more likely to be affected by climate-related changes to vegetation, but it 

cannot predict how those functions will change or the magnitude of change.

The effects of climate change on vegetation may vary geographically across the 

region (fig. 3.1). Overall, about 45 percent of area within national forests and grasslands 

in the region is at high or very high likelihood of vegetative change due to climate 

change, representing over 8 million acres. Some areas, such as in the Carson National 

Forest in northern New Mexico and in the Gila and Apache-Sitgreaves National Forests 

along the border between Arizona and New Mexico, exhibit relatively low likelihood of 

climate-induced vegetative change. Yet national forests in central Arizona (the Tonto) 

and northern Arizona (the Kaibab) show a higher proportion of area with high or very 

high likelihood of vegetative change.

Interior Chaparral IC shrubland

Sand Sheet Shrubland SSHR shrubland

Intermountain Salt Scrub ISS shrubland

Sonora-Mojave Mixed Salt Desert Scrub SDS shrubland

Chihuahuan Salt Desert Scrub CSDS shrubland

Chihuahuan Desert Scrub CDS shrubland

Mojave-Sonoran Desert Scrub MSDS shrubland

Sandsage SAND shrubland

Shinnery Oak (Black Kettle National Grasslands) SHIN Great Plains

Mixed-Grass Prairie MGP Great Plains

Shortgrass Prairie SGP Great Plains

Ecological response unit ERU subclass ERU code System type

Table 2—Continued.
20 USDA Forest Service RMRS-GTR-383. 2018

BOX 1—An Ecosystem Approach to Assessing the Likelihood of Climate-Induced Vegetative 

Change

The assessment of likelihood of vegetative change relies on a correlative modeling approach to develop climate 

envelopes for individual ecosystem types. The correlative modeling approach is based on finding a range of 

climatic conditions (i.e., the climate envelope) that is correlated with the existence of ecosystem types on the 

landscape. Projections of future climatic conditions can then be compared to the climate envelope for each 

ecosystem type to determine where on the landscape the future climate may induce changes in the composition, 

structure, and processes of ecosystems. The development of an analysis of vegetative change rating is described 

in the following steps, with detailed description of methods available in Triepke et al. (2014):

1. The landscape across the region was stratified into ecological response units (ERUs) (see table 2), 

representing recognizable ecosystem types that repeat across the landscape. 

2. Based on topographical data, the landscape was segmented into base-level polygons that have similar 

energy patterns, as an inference of site vegetation potential and natural disturbance regimes. Each segment 

was assigned to an ERU.

3. Discriminant analysis was used to associate climate variables with the existence of each ERU on the 

landscape; the results of the analysis indicate the climate variables from which to develop climate envelopes 

for each ERU, along with the explanatory value used to weight each variable. The climate envelope 

describes the range of climatic conditions associated with the existence of each ERU under current climatic 

conditions prior to about 1990. The climate variables used to describe the climate envelopes are mean 

annual temperature, mean annual precipitation, degree days greater than 5 oC (9 oF) within the frost-free 

period, degree days less than 0 oC based on mean minimum monthly temperature, mean temperature in the 

warmest month, and mean temperature in the coldest month.

4. Downscaled climate model outputs from the Rocky Mountain Research Station’s laboratory in Moscow, 

Idaho, were used to project climate variables into the future (year 2090) at the 90-m2 (970-ft2) spatial 

resolution. The downscaled outputs are then associated with each ERU polygon on the landscape. 

5. For each landscape polygon, future climate from the CGCM3 global circulation model (GCM) under the A1B 

emissions scenario was compared to the climate envelope for the relevant ERU, with each climate variable 

weighted by its explanatory value. A likelihood of vegetative change rating was given to each polygon 

based on departure of future climate from the climate envelope of the given ecosystem type. Low likelihood 

represents 2 standard deviations (s.d.) or less of climate envelope mean; moderate likelihood represents 

between 2 and 3 s.d.; high likelihood, between 3 and 4 s.d.; and very high likelihood, greater than 4 s.d. from 

the envelope mean.

6. Uncertainty in the likelihood of vegetative change rating is reported for each polygon based on agreement 

of ratings from three GCMs (CGCM3, HADCM3, and GFDLCM21), each of which yields slightly different 

projections under a given emissions scenario. Low uncertainty was assigned if all three GCMs yield the 

same likelihood rating for a given polygon under the A2 emissions scenario, moderate uncertainty was 

assigned if two of the three GCMs yield the same likelihood rating, and high uncertainty was assigned if none 

of the three GCMs agree on the likelihood rating. Likelihood of change ratings for each ERU are used for 

tabulation and reporting only when uncertainty is low or moderate.

Note: The A1B emissions scenario is used to report likelihood of vegetative change because it represents a 

commonly used mid-range emissions scenario (Gutzler and Robbins 2010). However, scenario outputs are not 

available for all GCMs. To develop the uncertainty ratings, the A2 scenario was used because the downscaled 

data for that scenario were available for multiple GCMs for comparison. See Triepke et al. (2014) for more details 

on model and scenario selections.

Summarizing likelihood of vegetative change by administrative units can indicate 

where managers may expect to encounter climate impacts (fig. 3.2). Ranger districts exhibit considerable variation in potential climate-induced vegetative change, from as little 

as 5 percent to almost 90 percent of the area at high or very high likelihood of vegetative change. The degree of uncertainty associated with climate change projections also 

varies by administrative unit. Several ranger districts have more than 20 percent of area 

where vegetative change is highly uncertain, in other words, where the climate models 

do not agree on the likelihood of change.
USDA Forest Service RMRS-GTR-383. 2018 21

Figure 3.1—Likelihood of vegetative change on national forests and grasslands in the Forest Service Southwestern Region. 

Orange and red areas are where projected future climatic conditions are likely to fall outside of the range of conditions 

that support current vegetation types. Green and blue areas are where projected future climatic conditions are within the 

range of conditions that support current vegetation. Abbreviations for national forests: ASF = Apache-Sitgreaves, CNF = 

Carson, CIF = Cibola, COF = Coconino, CNF = Coronado, GNF = Gila, KNF = Kaibab, LNF = Lincoln; PNF = Prescott; 

SNF = Santa Fe, TNF = Tonto.
22 USDA Forest Service RMRS-GTR-383. 2018

Disturbance Regimes

The landscape and communities that contain Southwestern Region forests and 

grasslands are subject to natural and anthropogenic disturbance. Climate change is 

likely to interact with forest and grassland disturbances to cause ecological changes, 

although much remains to be studied about relationships between climate change and 

disturbances (Dale et al. 2000). Interactions among disturbances, which may be exacerbated under climate change scenarios, can cause major ecological transitions (Allen 

2007). Understanding the potential ecological transitions can aid in identifying how 

ecosystem services may be affected by climate-induced changes to disturbance regimes.

Several types of disturbances are important in the Southwest and may be affected 

by climatic changes to forests and grasslands, including wildfire, insects and disease, 

floods, erosion and landslides, and noxious and invasive plants. To assess the potential 

for climate change to interact with disturbances and affect ecosystem service provision, 

the areal extent and severity of disturbance regimes is summarized for national forest 

Figure 3.2—Share of ranger district area in the Forest Service Southwestern Region in likelihood of vegetative change 

categories, and in high uncertainty. Bars to the left of the chart (taller red portions) indicate a higher proportion of area in 

each ranger district where vegetative change due to climate change is more likely. Bars to the right indicate ranger districts 

where a greater proportion of area has low or moderate likelihood of changing vegetation types due to climate change. 

The height of the black portion of each bar indicates the proportion of ranger district area where projections of vegetative 

change are uncertain. Abbreviations for national forests: ASF = Apache-Sitgreaves, CNF = Carson, CIF = Cibola, COF = 

Coconino, CNF = Coronado, GNF = Gila, KNF = Kaibab, LNF = Lincoln; PNF = Prescott; SNF = Santa Fe, TNF = Tonto.
USDA Forest Service RMRS-GTR-383. 2018 23

Table 3—Description and summaries of variables used in the exposure analysis.

Variable Description

Number 

of obs. Mean Min Max

vrisk Share of area rated high or very high likelihood of 

vegetative change, by ranger district

52 0.43 0.05 0.94

WHP Share of area rated high or very high wildfire hazard 

potential, by ranger district

52 0.40 0.02 0.82

IMDR Insect and disease mortality risk; share of area greater 

than 25% projected percent basal area loss of trees, by 

ranger district

52 0.11 0.0 0.44

vrisk_RMU Share of area rated high or very high likelihood of 

vegetative change, by range management unit

1,005 0.46 0.0 1.0

grazing_exposure_

RMU

Exposure of animal unit months (AUMs) to high or 

very high likelihood of vegetative change, by range 

management unit

1,005 713 0 15,269

AUM_RMU Number of permitted AUMs, by range management unit 1,005 1,994 5 22,464

grazing_exposure_RD Exposure of AUMs to high or very high likelihood of 

vegetative change, by ranger district

50 18,893 0 84,365

water_exposure_HUC Index of exposure of watersheds important for surface 

drinking water to vegetative changes, by 12-digit 

Hydrologic Unit Code (HUC) watershed

1,712 7.76 0 89

vrisk_HUC Share of area rated high or very high likelihood of 

vegetative change, by12-digit HUC watersheds

1,712 0.46 0 1

IMP_HUC Importance rating of 12-digit HUC watersheds for 

surface drinking water

1,712 19.7 0 89

water_exposure_RD Index of exposure of watersheds, by ranger district 52 9.45 0.19 37.0

units and ranger districts. These summaries provide a current snapshot of areas within 

the region where ecosystem service provision may be stressed by disturbances. The 

disturbance summaries are then overlaid with summaries of the likelihood of vegetative 

change described in the previous section to gauge the relative stress that climate change 

will exert on plant communities. Detailed geospatial data are available for current 

wildfire hazard (Fire Modeling Institute 2014) and risk of tree mortality due to insects 

and disease (Krist et al. 2014). Other disturbances are discussed qualitatively due to a 

lack of comprehensive geospatial data for the region. Eleven variables were used in the 

exposure analyses (table 3).

Wildfire

Wildfire is an endemic ecosystem process in the Southwest, and plays an important 

role in shaping ecosystem functions on national forests and grasslands. Wildfire has the 

potential to directly affect people as a hazard that threatens communities and critical infrastructure (e.g., power distribution lines), and is also a factor that affects the provision 

of several ecosystem services.

Wildfire directly impacts provision of timber and rangeland services and the 

availability and desirability of recreation opportunities. Fire also has direct and indirect 

effects—both positive and negative—on a range of nonmarket values provided by 

forests (Venn and Calkin 2011). High-severity wildfire tends to degrade most ecosystem 
24 USDA Forest Service RMRS-GTR-383. 2018

services and valued assets, and is particularly damaging for air quality, municipal watersheds, and built structures (Thompson et al. 2011). Critical species habitat, air quality, 

and moderate- to high-density development are at greatest risk of being degraded by 

fire, but fire-adapted ecosystems are expected to experience positive effects from fire 

overall (Thompson et al. 2011). 

Climate change is likely to alter wildfire regimes in the Southwest due to higher 

temperatures and earlier spring snowmelt and runoff (Dale et al. 2001; Westerling et al. 

2006), resulting in greater wildfire activity (Flannigan et al. 2000; Littell et al. 2009). 

Climate effects on vegetation may exacerbate wildfire risk through increased tree mortality due to warming and extreme fire behavior, water deficits, and drought stress (van 

Mantgem et al. 2009; Williams et al. 2010), although long-term effects may vary with 

changes in vegetation and fuel availability in the future. Due to the rapid response of fire 

regimes to climatic changes, climate effects on wildfire may overwhelm other direct effects on vegetation and species (Flannigan et al. 2000), and wildfire may act as a trigger 

for type conversion to another ERU or novel system.

Wildfire hazard is assessed for each forest unit and ranger district by examining the current likelihood and conditional severity of wildfires on national forests and 

grasslands by using the Wildfire Hazard Potential (WHP) geospatial data developed by 

the Forest Service’s Rocky Mountain Research Station, Fire Modeling Institute (Fire 

Modeling Institute 2014). WHP describes areas where wildfires are likely to occur that 

would be difficult for suppression activities to contain. High-hazard areas identified by 

WHP are characterized by relatively high likelihood of wildfire occurring and burning 

at high intensities that tend to be damaging for ecosystem services and functions (Fire 

Modeling Institute 2014). The WHP geospatial data are a continuous scale calculated at 

a 270-m2 (2,900-ft2) resolution; the continuous measure for each pixel is then classified 

into five hazard categories: Very low, low, moderate, high, and very high.

Wildfire hazard potential is summarized for each national forest ranger district by 

calculating the share of area in each district where wildfire hazard is classified as high 

or very high (fig. 3.3). Ranger districts in the region vary widely in the share of area 

exposed to high or very high wildfire hazard potential, from less than 2 percent (Jicarilla 

Ranger District in the Carson National Forest) to over 80 percent (Mogollon Rim 

Ranger District in the Coconino National Forest). However, most ranger districts have 

less than half of their area categorized as high or very high hazard.

Wildfire hazard potential provides a current snapshot of areas in the region that 

may be affected by severe wildfires. These areas may be more or less exposed to 

climate-related changes to vegetation that can alter fire regimes in the future. Wildfire 

hazard and likelihood of vegetative change are not strongly correlated with each other 

(correlation coefficient = –0.15). Geographically overlaying the two indicators can help 

identify specific districts that may be of greater concern due to both wildfire and climate 

change-related hazards. Three ranger districts have more than 50 percent of area at 

high or very high wildfire hazard and high or very high likelihood of vegetative change 

(fig. 3.4). Most of the remaining districts are of less concern, with less than 50 percent 

of area rated as high or very high wildfire hazard and less than 50 percent of area rated 

at high or very high likelihood of vegetative change.

The lower (lb) and upper bound (ub) of areal intersection of wildfire hazard potential and likelihood of vegetative change (vrisk) are calculated as:
USDA Forest Service RMRS-GTR-383. 2018 25

lbWHP,vrisk = max[0, –(1 – WHP – vrisk)]

ubWHP,vrisk = min[WHP,vrisk]

The assessment of wildfire hazard and likelihood of vegetative change is nonspatially explicit in that the two variables are summarized independently for each ranger 

district. Additionally, the effect of climate on wildfire hazard has not been assessed, and 

the interactions between changes in wildfire hazard and vegetative changes require more 

detailed modeling. However, the range of areal intersection can indicate which ranger 

districts are more or less likely to be at risk of both high wildfire hazard and climateinduced vegetative change. Most ranger districts have a maximum possible spatial 

intersection between wildfire hazard and vegetative change of less than 40 percent of 

total district area, and may have no intersection depending on the spatial pattern of each 

variable. Twelve ranger districts have a maximum possible spatial interaction greater 

than 40 percent, and all but one of these ranger districts have a minimum possible spatial interaction of at least 15 percent.

Figure 3.3—Scatter plot of climate-related likelihood of vegetative change and wildfire hazard potential in the Forest Service 

Southwestern Region, by national forest or grassland and ranger district. Points moving to the right and up indicate 

increasing share of ranger district area that is at high or very high likelihood of seeing vegetative changes and increasing 

share of area that is at high or very high current wildfire potential. Abbreviations for national forests: ASF = ApacheSitgreaves, CNF = Carson, CIF = Cibola, COF = Coconino, CNF = Coronado, GNF = Gila, KNF = Kaibab, LNF = Lincoln; 

PNF = Prescott; SNF = Santa Fe, TNF = Tonto.
26 USDA Forest Service RMRS-GTR-383. 2018

Insects and Disease

Forest insects and disease can have a positive or negative effect on forest health, 

although severe outbreaks can result in damaging effects to forests (Moser et al. 2009; 

Tkacz et al. 2008). Tree damage and mortality from insects and disease may change 

ecological functions and values derived from forests (Chornesky et al. 2005), and can 

degrade public and private benefits provided by forests (Holmes et al. 2008). For example, diseased and dead trees reduce aesthetic values of forests that accrue to nearby 

homeowners (Huggett et al. 2008). The economic costs associated with insects and 

disease can be substantial depending on the location, spatial scale, and type of outbreak 

(Ayres and Lombardero 2000). 

Insect and disease mortality risk (IDMR) in the Southwest is summarized by using 

data that describe the projected percent basal area loss (PBAL) due to mortality from 

insects and disease from the National Insect and Disease Forest Risk Assessment (Krist 

et al. 2014). These data indicate geospatial locations where, based on current forest conditions and without remediation, 25 percent or more of standing live basal area greater 

Figure 3.4—Potential areal intersection of high or very high wildfire hazard and high or very high likelihood of vegetative 

change in the Forest Service Southwestern Region, by ranger district. Ranger districts to the left of the chart have a greater 

proportion of area that is likely both to see vegetative changes and to have high current wildfire potential. Abbreviations 

for national forests: ASF = Apache-Sitgreaves, CNF = Carson, CIF = Cibola, COF = Coconino, CNF = Coronado, GNF = 

Gila, KNF = Kaibab, LNF = Lincoln; PNF = Prescott; SNF = Santa Fe, TNF = Tonto.
USDA Forest Service RMRS-GTR-383. 2018 27

than 1 inch in diameter would be likely to die over the next 15 years due to insects and 

diseases. PBAL data are in raster format at a 240-m2 (2,600-ft2) resolution. Each pixel 

in the raster data is given a PBAL rating between 0 and 100, corresponding to the PBAL 

projected for trees in that pixel. The percentage of area with at least 25 percent projected 

PBAL is reported for each forest unit and ranger district (which is the same threshold 

used by Krist et al. 2014 to identify areas at risk of uncharacteristic tree mortality). The 

PBAL raster data cover “treed land,” which includes all areas with a measurable tree 

presence; not all national forest and grassland areas contain treed land. The percentage 

area with at least 25 percent PBAL is calculated using the total treed land within the forest unit or ranger district as the denominator, and the total area with at least 25 percent 

PBAL as the numerator.

Climate may affect insect and disease outbreaks through multiple pathways, including (1) a direct effect of temperature and precipitation change on insect and disease 

activity, (2) indirect effects of climate on plant physiology and natural defenses, and (3) 

indirect effects of climate on natural competitors and enemies (Ayres and Lombardero 

2000). Effects of climate on insect and disease spread are ambiguous, depending on interannual variations in temperature, precipitation, and effects on competitors and natural 

defenses against insect and disease spread (Dale et al. 2001). However, ranger districts 

with a relatively high share of area that is at risk of insect- and disease-related mortality 

and climate-induced vegetative change can be interpreted as having greater exposure to 

an existing stressor (insects and disease) and greater stress on vegetation from climate.

Most forests and districts have less than 20 percent of area at high risk of insect 

and disease mortality. A few ranger districts, mostly concentrated in northern New 

Mexico in the Santa Fe and the Carson National Forests, have between 25 and 45 

percent of area at high insect and disease mortality risk (fig. 3.5). Overall, there is a 

negative correlation at the ranger district level between the likelihood of vegetative 

change and insect and disease mortality risk (correlation coefficient = –0.45); almost all 

of the ranger districts with greater than 50 percent of area at high or very high vegetative risk have relatively low mortality risk (<15 percent). Conversely, only one district 

(Sacramento Ranger District in the Lincoln National Forest) has high mortality risk on 

more than 20 percent of its acreage and at least 50 percent of its area at high or very 

high likelihood of vegetative change.

Summarized at the ranger district level, comparisons of insect and disease mortality and likelihood of vegetative change do not account for spatial intersections of the 

two variables; it is possible for most ranger districts that very little area is at high likelihood of both insect and disease mortality and vegetative change due to climate change 

(fig. 3.6). The lower and upper bound of areal intersection of high insect and disease 

mortality risk and high likelihood of vegetative change are calculated as:

lbIDMR,vrisk = max[0, –(1 – IDMR – vrisk)]

ubIDMR,vrisk = min[IDMR,vrisk]

Overall, most ranger districts have relatively low likelihood of experiencing both 

insect and disease risk and climate-induced vegetative change. Six ranger districts have 

potential areal intersection greater than 20 percent of total area; five of these are in 

either the Carson or the Santa Fe National Forests in northern New Mexico. Most ranger 
28 USDA Forest Service RMRS-GTR-383. 2018

districts have a maximum areal intersection of less than 10 percent of ranger district 

area. However, the assessment of areal intersection of stressors does not specifically 

account for how climate change may interact with insect and disease risk. The insect 

and disease assessment does not specifically account for projected changes in climate, 

and under future climate and vegetative changes the risk of insects and disease potentially would be different than the current assessment indicates. The overall negative 

correlation between likelihood of vegetative change and mortality risk deserves further 

exploration, although this is beyond the scope of this assessment. For example, it would 

be useful to examine whether a more detailed model of insect and disease mortality that 

incorporates climate change would yield similar findings, or whether there is correlation 

between areas currently experiencing vegetative change and tree mortality.

Floods, Landslides, and Noxious and Invasive Plants

Interactions between disturbances and climate change may also have indirect 

effects on other hazards and disturbances, such as floods, landslides, erosion, and the 

spread of invasive plants. Climate change is likely to lead to changes in hydrology and 

the frequency and severity of floods, erosion events, and landslides (Ryan and Vose 

2012). Severe and prolonged droughts are associated with severe fires and large erosion 

Figure 3.5—Scatter plot of climate-related likelihood of vegetative change and risk of basal area loss due to insects and 

disease in the Forest Service Southwestern Region, by forest and ranger district. The trend line indicates the average 

relationship between proportion of area with high likelihood of vegetative change and proportion of area with high 

projected basal area loss. The downward-sloping line indicates that ranger districts that have more land at high likelihood 

of vegetative change tend to have less area at risk of tree mortality due to insects and disease.
USDA Forest Service RMRS-GTR-383. 2018 29

events (Pierce et al. 2004), which can result in major ecological transitions (Allen 

2007). There is some evidence that climate change is associated with increased flooding 

risk, although several human and landscape factors also play a role and may confound 

the effects of a changing climate (Bronstert 2003).

The establishment and spread of invasive plants can alter fire regimes and ecosystem functions (Smith et al. 2000), affect the productivity and economic viability of 

rangelands that support livestock grazing (DiTomaso 2000), and reduce wildlife habitat 

quality (Masters and Sheley 2001). Climate change is expected to increase the establishment and spread of invasive plants, although the exact response of invasive and native 

plants to changing climatic and biological factors is not fully understood (Ryan and 

Vose 2012). Increased carbon dioxide (CO2) concentration may favor the productivity 

of invasive species over native species (Smith et al. 2000), and some invasive species 

(e.g., cheatgrass, Bromus tectorum) may be promoted by higher temperatures but not 

necessarily increased CO2 concentration (Blumenthal et al. 2016). Climate may affect 

invasive plants through several pathways, including altered opportunities for transport 

Figure 3.6—Potential areal intersection of basal area loss greater than 25 percent and high or very high likelihood of 

vegetative change in the Forest Service Southwestern Region, by ranger district. Ranger districts to the left of the chart have 

a greater proportion of area that is likely both to see vegetative changes and to have high projected tree mortality due to 

insects and disease. Note that because all of the bars extend to the bottom of the chart, it is possible in all ranger districts 

that there is no spatial overlap between high vegetative risk areas and high tree mortality areas. Abbreviations for national 

forests: ASF = Apache-Sitgreaves, CNF = Carson, CIF = Cibola, COF = Coconino, CNF = Coronado, GNF = Gila, KNF = 

Kaibab, LNF = Lincoln; PNF = Prescott; SNF = Santa Fe, TNF = Tonto.
30 USDA Forest Service RMRS-GTR-383. 2018

and introduction, changing climatic constraints (on native and invasive plants), the 

distribution and impact of existing invasions, and the effectiveness of control efforts 

(Hellmann et al. 2008). Projected declines in average productivity and increased variability in productivity (Reeves et al. 2014) also may provide opportunities for invasive 

species.

A challenge to assessing the effects of climate-related forest change on floods, 

landslides, erosion events, and invasive plants is a lack of regionwide data on their occurrence and severity. Further, multiple interacting factors that affect these hazards and 

disturbances may confound the effect of climate-related changes to forests. In general, 

areas at high risk of vegetative change (i.e., where the existing plant community may 

not be supported by the future climate), at high risk of damaging wildfires, and where 

insects and disease are likely to result in tree mortality are assumed to be more exposed 

to the risk of floods, landslides, erosion, and invasive plants. Although we do not directly assess the exposure to these hazards across the region, the next section assesses 

the intersection of likely vegetative change, wildfire hazard, and insect and disease mortality to identify areas in the region exposed to multiple climate-related stressors.

Intersecting Risk of Climate Change Effects and Disturbances

Combining the available geospatial data on disturbances (fire, insect and disease 

mortality) with the assessment of the likelihood of vegetative change provides additional insight into the areas of the region where multiple stressors may threaten the 

provision of ecosystem services. The expected share of area that is rated in the high or 

very high category of at least two of the three stressors (likelihood of vegetative change, 

wildfire hazard, and insect and disease mortality risk) is calculated for each ranger district (fig. 3.7). 

Expected intersections are calculated by interpreting each area share as the probability that a given acre is in the high or very high category for a given stressor. Thus, the 

probability that a given acre is in the high or very high category for all three stressors 

is the product of each area share for that ranger district: Pr(3) = vrisk × IMDR × WHP. 

The probability of an acre being in the high or very high category for two stressors is the 

sum of the pairwise products of the three area shares: Pr(2) = (vrisk × IMDR + vrisk × 

WHP + IMDR × WHP). 

Most ranger districts have relatively low expected area that is likely to be at high 

or very high risk of all three stressors; the highest value is less than 10 percent of ranger 

district area. Greater variation is evident for expected area affected by two stressors. 

Four ranger districts are expected to have at least 50 percent of area in the high or very 

high risk category for at least two stressors, yet about one-fifth of ranger districts are 

expected to have less than 10 percent of area in the high or very high risk category for at 

least two stressors. 

Uses and Roles of Ecosystems in the Southwest

The assessment of the likelihood of vegetative change and disturbances is useful 

for evaluating the exposure of Southwestern forests and grasslands to climate-related 

changes in the provision of ecosystem services. Vegetative change and changing disturbance regimes may alter the provision of water, recreation opportunities, grazing, and 
USDA Forest Service RMRS-GTR-383. 2018 31

commercial and noncommercial forest products. In turn forest regulating services (e.g., 

long-term storage of carbon, climate regulation, water filtration, soil stabilization) and 

forest supporting services (e.g., pollination, seed dispersal, nutrient cycling) can change. 

Forest- and grassland-supported cultural services (e.g., education, aesthetics, spiritual 

connection, recreation experiences, tourism opportunities) may also be affected. These 

relationships are broadly recognized, but spatial data (where available) can provide 

more detailed information on the extent of threat to many of these uses and the spatial 

distribution of areas that are relatively more or less exposed to climate-related changes. 

Forage for Livestock

Areas available for livestock grazing cover more than 15 million acres of national 

forests and grasslands in the Southwest (calculated by summing the total area in range 

management units where livestock operators may have permits to graze livestock). 

Public rangelands are an important source of forage for livestock operators in the western United States. Grazing on public lands supports employment and income in local 

Figure 3.7—Expected intersection of likelihood of vegetative change, wildfire hazard, and insect and disease mortality risk 

in the Forest Service Southwestern Region, by ranger district. The height of the red bars indicates the expected proportion 

of area in each ranger district where all three hazard variables are rated as high or very high. The height of the blue bars 

indicates the expected proportion of ranger district area where at least two of the three hazard variables are rated as high 

or very high. Abbreviations for national forests: ASF = Apache-Sitgreaves, CNF = Carson, CIF = Cibola, COF = Coconino, 

CNF = Coronado, GNF = Gila, KNF = Kaibab, LNF = Lincoln; PNF = Prescott; SNF = Santa Fe, TNF = Tonto.
32 USDA Forest Service RMRS-GTR-383. 2018

economies (discussed further in Chapter 4) and cultural traditions in Southwestern communities that date back to the earliest European settlement of the region (Atencio 2004). 

Rangelands also provide a variety of nongrazing ecosystem services, such as cultural 

heritage services, biodiversity, wildlife habitat, and offsite scenic and lifestyle amenities 

enjoyed by nearby residents and communities (Brown and MacLeod 2011; Havstad et 

al. 2007; Torell et al. 2005).

Increased CO2 concentrations and changes in temperature and precipitation directly affect the productivity of rangelands. Increased CO2 concentrations and longer 

growing seasons may increase productivity on rangelands (Baker et al. 1993), but higher 

temperatures and increased variability of precipitation, interactions with disturbances 

such as fire and invasive plants and hazards such as erosion and landslides, and increased human impacts may stress rangeland productivity in some areas (Chambers and 

Pellant 2008). Greater climate variability can also have an impact; for example, optimal 

livestock stocking rates may decline as precipitation becomes more variable (Ritten 

et al. 2010). In the Southwest, projections of the effects of climate change on forage 

quantity and cattle production indicate overall reductions in rangeland productivity and 

increased vulnerability of cattle production in the future (Reeves and Bagne 2016). 

An assessment of the exposure of forage for livestock to climate-related forest and 

grassland changes relies on identifying areas that are important for livestock grazing and 

most at risk of vegetative change. Most of the area within national forest and grassland 

boundaries is part of range management units (RMUs). Vegetative risk for RMUs is 

summarized by joining ERU polygons and vegetative risk ratings to RMU polygons 

defined by the Southwestern Region. For each RMU polygon, vegetative risk is defined 

as the share of area that is rated at high or very high risk of vegetative change, or: 

vrisk_RMUi = (acres_highi + acres_vhighi)/total_acresi. (1)

Similar to the forest- and ranger district-level summaries, both the numerator and 

denominator are calculated based on vegetative likelihood ratings that are of low or 

moderate uncertainty (see box 1 for details of the uncertainty ratings). 

To examine the exposure of grazing activities to vegetative risk due to climate 

change, RMU vegetative risk summaries are paired with the number of permitted animal 

unit months (AUMs) associated with each RMU. Grazing exposure to vegetative risk is 

calculated as the number of permitted AUMs on an RMU (AUM_RMUi) multiplied by 

the share of area at high or very high risk of vegetative change (vrisk_RMUi, calculated 

from equation 1), or: 

grazing_exposure_RMUi = vrisk_RMUi × AUM_RMUi. (2)

Permitted AUM data are gathered from individual livestock grazing permits through 

the Forest Service Natural Resource Manager iWeb data management application. Each 

RMU may be associated with one or more livestock grazing permits, which are summed 

to the relevant RMU. Grazing exposure is measured by using permitted AUMs, which 

summarize the agreed-upon number of animals, by species, and timing of grazing allowed under the permit. In any given year a permittee may be authorized to graze more 

or fewer AUMs, and permittees may graze a number of AUMs that differs (generally 

lower) from the authorized number. Permitted AUMs are used as a measure of long-term 
USDA Forest Service RMRS-GTR-383. 2018 33

expected carrying capacity of each RMU, even though the actual number of animals that 

graze an RMU may differ from the permitted number.

Total grazing exposure (fig. 3.8) amounts to more than 900,000 AUMs, or about 43 

percent of all permitted AUMs in the region. However, significant variation in grazing 

exposure exists within the region (fig. 3.9). Although all forest and grassland units have 

some areas of high exposure, the Coronado in Arizona accounts for a large share (about 

28 percent) of exposed AUMs in the region. Three Coronado ranger districts are among 

the five districts with the highest number of exposed permitted AUMs. Ranger districts 

on forests in northern New Mexico (the Santa Fe and the Carson) tend to have fewer 

exposed AUMs due to a combination of lower total numbers of permitted AUMs and 

lower average likelihood of vegetative change on RMUs in these forests. The finding of 

within-region variation in exposure is useful for interpreting broader-scale analyses of 

grazing vulnerability (e.g., Reeves and Bagne [2016]) that find interregional variation in 

vulnerability and overall higher vulnerability in the Southwest. However, the assessment 

Figure 3.8—Exposure of permitted animal unit months (AUMs) to climate-induced vegetative change on national forest and 

grassland range management units in the Forest Service Southwestern Region. Red and orange areas indicate high grazing 

exposure, or areas where a large number of permitted AUMs graze on land that is rated at high or very high likelihood of 

vegetative change. Green areas indicate low exposure, or areas where few AUMs are permitted to graze on land rated at 

high or very likelihood of vegetative change.
34 USDA Forest Service RMRS-GTR-383. 2018

here does not account for potential changes in rangeland productivity that are not captured in the likelihood of vegetative change.

Surface Water Supply

National forests and grasslands have long been recognized as an important source 

of surface water for downstream users. The historical establishment of many national 

forests was predicated on the protection of key watersheds (Steen 2004: Chapter 2). 

The combination of an arid climate in the Southwest, rapid urbanization and 

population growth, and a changing climate may stress water users. Recent changes in 

hydrology in the region are likely to have been caused by human activities, suggesting 

that future changes in climate will lead to reductions in water availability (Barnett et 

al. 2008). In general, climate change is likely to result in a transition to a drier climate 

with more severe droughts and reduced surface water runoff (MacDonald 2010; Seager 

et al. 2007), and reductions in water storage, downstream releases (e.g., to Texas and 

Mexico), and hydropower generation. Climate change is also likely to affect complex 

Figure 3.9—Permitted animal unit months (AUMs) on range management units with high and low exposure to vegetative 

change in the Forest Service Southwestern Region, by ranger district. Bar height indicates total permitted AUMs on all 

range management units (RMUs) managed by each ranger district. High-exposure AUMs (red bars) are calculated as the 

total authorized AUMs multiplied by the share of RMU area that is at high or very high likelihood of vegetative change 

(i.e., grazing_exposure_RMU from equation 2). For example, if 1,000 AUMs are permitted to graze on a ranger district 

where 50 percent of the land is rated at high or very high likelihood of vegetative change, exposed AUMs would equal 

500 AUMs (1,000 × 0.5). Abbreviations for national forests: ASF = Apache-Sitgreaves, CNF = Carson, CIF = Cibola, COF = 

Coconino, CNF = Coronado, GNF = Gila, KNF = Kaibab, LNF = Lincoln; PNF = Prescott; SNF = Santa Fe, TNF = Tonto.
USDA Forest Service RMRS-GTR-383. 2018 35

interactions between hydrology and indirect impacts of climate variability, including 

increased incidence of floods, droughts, low-flow conditions, erosion events, and landslides (Ryan and Vose 2012). 

The distribution and type of vegetation are important factors that determine surface 

water stream flows, although the magnitude and direction of effects of changes in vegetation depend on local and regional characteristics (Ryan and Vose 2012). Vegetation 

has an impact on the timing, magnitude, and seasonality of runoff (Brown et al. 2005), 

which affect water availability for downstream surface water users. 

Identification of watershed exposure to climate-related changes to national forest 

lands is based on the likelihood of vegetative change due to climate change and the importance of watersheds for surface water users. Likelihood of vegetative change is used 

as a proxy for climate impacts that may affect surface water supply. Greater likelihood 

of vegetative change is interpreted as an indicator of greater exposure to climate-related 

changes to surface water supply. However, we do not attempt to develop a detailed 

hydrological model of water supply responses to climatic changes, or discern whether 

climate-induced changes to vegetation on important watersheds is likely to increase or 

decrease supply in the future.

The importance of watersheds within national forest and grassland boundaries for 

municipal surface drinking water is summarized by using Forests-to-Faucets data from 

Weidner and Todd (2011). The 12-digit Hydrologic Unit Code (HUC) watershed polygons are joined to national forest and grassland boundaries, and vegetative risk ratings 

of ERU polygons are summarized for each HUC that intersects a national forest and 

grasslands area. Watersheds in Arizona and New Mexico are included, but the few watersheds associated with grasslands in Oklahoma and Texas are not included because the 

vegetative risk data do not cover these areas. Similar to the RMU summary and the national forest and ranger district summary, vegetative risk for each HUC is calculated as 

the share of area at high or very high vegetative risk. For the many 12-digit HUCs that 

are only partially within forest or grassland boundaries, vegetative risk is calculated by 

using only the ERU-level polygons that fall within the boundaries of both the 12-digit 

HUCs and forests or grasslands.

Importance of surface water sources for municipal drinking water is measured by 

using the ‘IMP’ index field in the Forests-to-Faucets data. The value of IMP for each 

12-digit HUC is a standardized index value between 0 and 100 that summarizes relative mean annual water supply, the flow of water (e.g., from upstream to downstream 

watersheds), and water demand (i.e., of the municipality where water is eventually 

consumed). 

Exposure of watersheds within national forests and grasslands to vegetative risk is 

calculated by multiplying the importance for surface drinking water (IMP_HUC) by the 

share of area in each HUC that is at high or very high likelihood of vegetative change, 

or:

water_exposure_HUCi = vrisk_HUCi × IMP_HUCi. (3)

vrisk_HUCi is calculated as:

vrisk_HUCi = (acres_highi

 + acres_vhighi)

/total_acresi,
36 USDA Forest Service RMRS-GTR-383. 2018

where acres_highi and acres_vhighi are the acreage within a given HUC that is at high 

and very high likelihood of vegetative change, respectively, and is the total acreage of a 

given HUC.

The calculation in equation 3 is consistent with the calculations of the exposure 

of watersheds to forest threats in Weidner and Todd (2011: equations 10 and 11), where 

IMP is multiplied by the percentage of area in each watershed that is forested and the 

percentage of area in each watershed that is highly threatened by development, insects 

and disease, or wildland fire. We extend this formulation by specifying vegetative risk as 

the threat to watershed health, and are concerned with the entire watershed area within 

national forest and grassland boundaries (rather than just the proportion of area that is 

forested) (fig. 3.10).

Surface drinking water exposure for each ranger district is calculated as the acreweighted average of water_exposure_HUCi for all HUCs that intersect the relevant 

geographic area. For example, average water exposure for ranger district j is calculated 

as:

Figure 3.10—Watershed exposure to likelihood of vegetative change (water exposure HUC) in the Forest Service 

Southwestern Region, by 12-digit hydrologic unit code (HUC). Red and orange areas indicate important watersheds 

with high exposure to climate-induced vegetative change. Green and blue areas indicate important watersheds with low 

exposure to vegetative change.
USDA Forest Service RMRS-GTR-383. 2018 37

water_exposure_RDj = Ʃ

i (aij × water_exposure_HUCij),

where aij is the share of area in ranger district j that is in watershed ij.

Several ranger districts exhibit low average exposure to climate-related changes 

to municipal watersheds (fig. 3.11). Twenty ranger districts have water exposure values 

less than 5, indicating that watersheds within those districts exhibit some combination 

of relatively low importance for surface drinking water and low likelihood of climateinduced vegetative change. Several of the low-exposure ranger districts are relatively 

remote, including most of the districts in the Gila National Forest.

The highest-exposure ranger districts tend to be associated with surface water 

sources that supply the Phoenix, Arizona, metropolitan area. For example, all of the districts in the Tonto National Forest are among the 10 most exposed ranger districts, and 

9 of the 10 most exposed RDs include watersheds that drain into the Salt-Verde Rivers 

system, which is an important surface water source for Phoenix.

Figure 3.11—Average exposure of watersheds to climate change-induced vegetative change in the Forest Service 

Southwestern Region, by ranger district. Higher bars indicate a greater exposure of important surface watersheds 

to climate-induced vegetative change. The index values are the product of the watershed importance index and the 

proportion of area rated at high or very high likelihood of vegetative change, summarized by ranger district. Abbreviations 

for national forests: ASF = Apache-Sitgreaves, CNF = Carson, CIF = Cibola, COF = Coconino, CNF = Coronado, GNF = 

Gila, KNF = Kaibab, LNF = Lincoln; PNF = Prescott; SNF = Santa Fe, TNF = Tonto.
38 USDA Forest Service RMRS-GTR-383. 2018

Significant variation in watershed importance and likelihood of vegetative change 

exists, even within ranger districts. For example, about 5 percent of watersheds are relatively important for surface drinking water but have low likelihood of climate-induced 

vegetative change. On the other hand, nearly 10 percent of watersheds are ranked as 

the lowest importance for surface drinking water (i.e., IMP_HUC = 0), but exhibit high 

or very high likelihood of vegetative change on at least 50 percent of land within the 

watershed.

Timber Products

Commercial timber production supports employment and income in local 

economies in the region (discussed further in Chapter 4), and noncommercial products 

provide subsistence and traditional values. In the Southwestern Region, total area of 

timberland has remained relatively steady since the mid-20th century, and net volume 

of growing stock on timberland has increased by about 59 percent (Smith et al. 2009: 

Appendix C, tables 10 and 20). Climate change is expected to affect the potential of 

forests to provide timber, although the exact nature of this impact is uncertain. Climate 

change is also likely to increase mortality of tree species (van Mantgem et al. 2009), 

and increased temperatures and drought stress are projected to result in increased tree 

mortality and forest decline in the Southwest (McDowell et al. 2015; Williams et al. 

2010). In the United States, climate itself is projected to have a relatively small effect on 

timber, with projected increases in timber inventories and harvests (Irland et al. 2001) 

and changes in forest types (Wear et al. 2013). 

Timber harvests in the United States are expected to remain steady or increase in 

the future, depending on future climate change and global economy scenarios (USDA 

Forest Service 2012a). Despite the potential for climate to result in increased mortality 

for tree species, the net effect of climate on the total benefits derived from timber is 

likely to be relatively small and positive (Sohngen and Mendelsohn 1998). However, 

the effect of climate change on timber species specific to the Southwest requires further 

study. 

Detailed geospatial data on areas available and suitable for timber do not exist on 

a regionwide basis. However, the Forest Service Forest Inventory and Analysis program 

provides forest unit-level estimates of timberland, defined as forest land exclusive of 

reserved lands that is capable of producing at least 20 cubic feet of wood per acre per 

year; forest land is defined as land that is at least 10 percent stocked by forest trees and 

not currently developed for nonforest use (Nelson and Vissage 2007) (table 4).

Although limited data availability precludes a detailed analysis of climate-change 

effects on suitable timberland, the ERU vegetative risk data can be leveraged to identify areas where timber vegetation types may be at higher likelihood of change due to 

climatic changes. From table 2, Spruce-Fir Forest (ERU code SFF), Mixed Conifer 

with Aspen (ERU code MCW), Mixed Conifer – Frequent Fire (ERU code MCD), and 

Ponderosa Pine Forest (ERU code PPF) ERUs are identified as vegetation types that 

support species appropriate for timber production. Summarizing the likelihood of vegetative change for these ERUs can provide an indication of exposure to climate-related 

vegetative change for areas with vegetation types suitable for timber production. This 

summary does not estimate timber suitability areas. The vegetative risk analysis does 
USDA Forest Service RMRS-GTR-383. 2018 39

not filter on other characteristics that may indicate suitability for timber production, 

such as reserved land status (e.g., Wilderness Areas) or slope. See Nelson and Vissage 

(2007) for a description of estimates of spatially explicit timberland area. 

In total, about 3.2 million acres of montane forest vegetation types are at high or 

very high risk of vegetative change (fig. 3.12), which represents slightly more than 50 

percent of all montane forest vegetation on national forests in the region. The highest 

exposure ranger districts tend to have large tracts of timber vegetation (>100,000 acres) 

and a high percentage of timber vegetation at high or very high likelihood of change. 

The 12 highest-exposure ranger districts each have more than 100,000 acres of timber 

vegetation and greater than 60 percent of timber vegetation types at high or very high 

likelihood of change. On the other hand, a handful of ranger districts have large timber 

vegetation areas but relatively low exposure, including districts in Santa Fe and Carson 

National Forests. 

Recreation

Outdoor recreation is an important benefit provided by national forests and grasslands in the Southwestern Region. National forests in the region receive an estimated 

13.8 million visits per year and represent a wide variety of recreation activities and sites 

(USDA Forest Service n.d.). Recreation may be exposed to climatic changes because 

nature and ecosystem characteristics are key features of the overall outdoor recreation 

experience. This section summarizes recreation exposure to climatic changes on each 

forest unit for broad climate-sensitive recreation categories.

Climatic conditions and environmental characteristics that depend on climate 

determine the availability of and demand for different outdoor recreation opportunities 

(Shaw and Loomis 2008). Changing climatic conditions may alter the supply of and 

demand for recreation opportunities, resulting in changes in the amount and pattern of 

Table 4—Forest land and timberlanda by national forest in the U.S. Forest Service Southwestern Region.

Forest unit Forest land (acres) Timberland (acres)

Timberland as share of 

forest land

Total, all forests 15,540,320 4,892,384 0.315

Apache-Sitgreaves 1,716,525 810,961 0.472

Carson 1,271,332 743,696 0.585

Cibola 1,206,497 304,242 0.252

Coconino 1,475,775 714,010 0.484

Coronado 1,239,542 48,899 0.039

Gila 2,907,867 720,252 0.248

Kaibab 1,331,613 488,486 0.367

Lincoln 843,542 181,802 0.216

Prescott 683,632 40,820 0.060

Santa Fe 1,538,155 724,279 0.471

Tonto 1,325,840 114,937 0.087

a Area in forest land and timberland are estimates based on sampling plots. Source: USDA Forest Service 2015a.
40 USDA Forest Service RMRS-GTR-383. 2018

recreation in the future. Climate change is projected to increase outdoor recreation participation in general, although variation in participation by specific activities is possible 

(Bowker et al. 2013). Benefits provided by recreation are expected to increase under 

climate change scenarios because increases in warm-weather activities are expected 

to outweigh losses in winter activities (Loomis and Crespi 2004; Mendelsohn and 

Markowski 2004).

The supply of and demand for recreation opportunities are sensitive to climate 

through two primary pathways: a direct effect of changes in temperature and precipitation on the availability and quality of recreation sites, and an indirect effect of climate 

on the characteristics and ecological condition of recreation sites (Loomis and Crespi 

2004; Mendelsohn and Markowski 2004; Shaw and Loomis 2008). Direct effects are 

important for winter activities that are dependent on seasonal temperatures and the 

amount, timing, and phase of precipitation (Englin and Moeltner 2004; Irland et al. 

2001; Stratus Consulting 2009). Warm-weather activities are also sensitive to direct 

effects of climate change; warmer temperatures are associated with increased outdoor 

Figure 3.12—Area in montane and other forest vegetation types in the Forest Service Southwestern Region, by likelihood of 

vegetative change and ranger district. The combined height of each bar is the total area (in acres) of each ranger district. 

The combined height of the green and red bars is the total area in each ranger district that is predicted to currently be in 

montane forest vegetation types. The height of the red bars indicates the area of montane forest vegetation that is rated 

at high or very high likelihood of vegetative change. Abbreviations for national forests: ASF = Apache-Sitgreaves, CNF = 

Carson, CIF = Cibola, COF = Coconino, CNF = Coronado, GNF = Gila, KNF = Kaibab, LNF = Lincoln; PNF = Prescott; 

SNF = Santa Fe, TNF = Tonto.
USDA Forest Service RMRS-GTR-383. 2018 41

recreation visits (Richardson and Loomis 2004; Scott et al. 2007), although extreme 

heat can reduce visitation. 

Indirect climate effects may be important for recreation activities that depend on 

additional ecosystem inputs, such as wildlife, vegetation, and surface water. Indirect 

effects have been identified for cold-water fishing (Jones et al. 2013), boating, camping, 

and hiking (Loomis and Crespi 2004), and activities that involve highly valued natural 

characteristics (e.g., glaciers) (Scott et al. 2007). The indirect climate effect on disturbances, and wildfire in particular, may also play a role in recreation behavior, although 

the effect may be ambiguous and vary over time (Duffield et al. 2013; Englin et al. 

1996, 2001; Hesseln et al. 2003, 2004; Loomis et al. 2001; Rausch et al. 2010; Starbuck 

et al. 2006).

To summarize the exposure of recreation to changes in climate, annual visitation 

to national forests and grasslands is estimated for broad primary activity categories. 

Primary activities are grouped into categories based on common effects of climate 

on the supply and demand of recreation opportunities drawn from previous research. 

National forest visitation for five primary categories is summarized: warm-weather 

activities, wildlife-related activities, snow-based winter activities, gathering forest products, and water-based activities (not including fishing) (table 5).

Overall, recreation visitation on national forests is expected to expand due to 

longer warm-weather seasons that would offset reduced winter recreation activities. 

However, effects will probably vary across the region. For example, several national 

forests currently have little or no snow-based winter recreation and thus have low exposure to climate effects on this category of recreation. On the other hand, these forests 

may have significant seasonal shifts in warm-weather recreation due to warming winter 

temperatures and increased likelihood of extreme temperatures in the summer. 

Annual recreation visitation estimates for national forests in the Southwest indicate high visitation for warm-weather activities at all the forests (table 6). Across the 

Southwest, abundant days with sunshine and areas that are snow- and ice-free for much 

of the year provide ample opportunities for warm-weather recreation. The response of 

warm-weather recreation to climate change is likely to vary across the region. Areas 

where extreme heat is more likely to occur in the future (e.g., low-elevation sites) and 

where the winter season is already short may see decreases in warm-weather recreation. 

Other areas that become relatively more desirable in the future (e.g., high-elevation sites 

with less extreme heat) may have increases in warm-weather recreation.

Only the Carson and the Santa Fe National Forests attract a significant amount 

of winter recreation. These forests may undergo a decrease in winter recreation in the 

future as climate change results in shorter snow season and reduced site quality due 

to smaller and shorter-duration snowpacks. Some recreationists may shift recreational 

demand to nonwinter activities as other opportunities expand; others may choose to engage in winter recreation activities on sites other than national forests or in other regions 

where winter site quality becomes relatively more desirable.

Wildlife recreation is relatively important on several national forests, particularly 

the Gila and the Apache-Sitgreaves. However, the effect of climate-related forest 

changes on these activities is ambiguous. The effect on national forest recreation depends on local effects of climate on wildlife habitat and populations, and on the quality 

of national forest recreation sites relative to opportunities on State-owned and privately 
42 USDA Forest Service RMRS-GTR-383. 2018

owned lands. For example, in New Mexico some private landowners have an incentive 

to maintain ranchlands to support game species through a State program that grants 

tradable hunting permits outside of the normal permit lottery system (New Mexico 

Department of Game and Fish 2016a,b). The relative attractiveness of private versus 

public lands for hunting depends in part on climate effects to habitat and the ability of 

landowners to make investments on their land that support game populations. 

Table 5—Climate-sensitive recreation activity categories and expected effects of climate on future recreation participation on 

Southwestern Region national forests. 

Activity

Sensitivity to direct and 

indirect climate changes

Expected effects of 

climate on participation

Warm-weather activities Participation typically occurs during warm 

weather; dependent on the availability of 

snow- and ice-free sites, dry weather with 

moderate daytime temperatures, and the 

availability of sites where air quality is not 

impaired by smoke from wildfires.

Warming temperatures (+); higher 

likelihood of extreme temperatures (-); 

increased incidence, area, and severity of 

wildfire (+/-);

increased smoke from wildfires (-) 

Hiking/walking

Viewing natural features

Developed camping

Bicycling

Other nonmotorized

Wildlife activities Wildlife is a significant input for these 

activities. Temperature and precipitation are 

related to habitat suitability through effects 

on vegetation, productivity of food sources, 

species interactions, and water quantity 

and temperature (for aquatic species). 

Disturbances (wildland fire, invasive 

species, and insect and disease outbreaks) 

may affect the amount, distribution, and 

spatial heterogeneity of suitable habitat.

Warming temperatures (+), higher 

incidence of low stream flows (fishing, -); 

reduced snow pack (hunting, -); increased 

incidence, area, and severity of wildfire 

(terrestrial wildlife, +/-); reduced cold-water 

habitat, incursion of warm-water tolerant 

species (fishing, -)

Hunting

Fishing

Viewing wildlife

Winter activities Participation depends on the timing and 

amount of precipitation as snow and 

cold temperatures to support consistent 

snow coverage. Inherently sensitive to 

climate variability and interannual weather 

patterns.

Warming temperatures (-); reduced 

precipitation as snow (-) Downhill skiing

Snowmobiling

Cross-country skiing

Gathering forest products Depends on availability and abundance of 

target species (e.g., berries, mushrooms), 

which are related to patterns of 

temperature, precipitation, and snowpack. 

Disturbances may alter the availability and 

productivity of target species in current 

locations, and affect opportunities for 

species dispersal in new locations.

Warming temperatures (+); increased 

incidence, area, and severity of wildfire 

(+/-)

Water-based activities, not 

including fishing

Participation requires sufficient water flows 

(in streams and rivers) or levels (in lakes 

and reservoirs). Typically considered a 

warm-weather activity, and depends on 

moderate temperatures and snow- and 

ice-free sites. Some participants may seek 

water-based activities as a refuge from heat 

during periods of extreme heat.

Warming temperatures (+); higher 

likelihood of extreme temperatures (-)

Note: Positive (+) and negative (-) signs indicate expected direction of effect on overall benefits derived from recreation activity.
USDA Forest Service RMRS-GTR-383. 2018 43 Table 6—Estimates of share of annual recreation visits to Southwestern Region national forests by primary activity category. Forest unit Warm-weather activities Snow-based winter activities Wildlife activities Gathering forest products Water-based activities (not incl. fishing) All Downhill skiing Cross-country skiing + snowmobiling Apache-Sitgreaves 0.408 0.0 0.0 0.0 0.246 0.005 0.017 Carson 0.309 0.426 0.332 0.094 0.115 0.023 0.010 Cibola 0.652 0.037 0.031 0.006 0.100 0.016 0.0 Coconino 0.662 0.054 0.045 0.009 0.060 0.005 0.015 Coronado 0.751 0.0 0.0 0.0 0.082 0.005 0.010 Gila 0.388 0.0 0.0 0.0 0.309 0.001 0.001 Kaibab 0.420 0.005 0.005 0.0 0.144 0.005 0.005 Lincoln 0.672 0.061 0.061 0.0 0.059 0.009 0.0 Prescott 0.652 0.0 0.0 0.0 0.095 0.003 0.022 Santa Fe 0.664 0.174 0.083 0.091 0.074 0.001 0.008 Tonto 0.330 0.0 0.0 0.0 0.140 0.0 0.089 Source: RMRS calculations of National Visitor Use Monitoring (NVUM) survey responses, round 2 (Apache-Sitgreaves, Carson, Lincoln, Santa Fe, and Tonto) and round 3 (Cibola, Coconino, Coronado, Gila, Kaibab, and Prescott) (USDA Forest Service 2015b). Visits are grouped into activity categories based on reported primary activity participated in during the visit. 
44 USDA Forest Service RMRS-GTR-383. 2018

Composite Exposure of Ecosystem Services to Climate Change

The assessments of ecosystem services and climate-related disturbances illustrate 

that ecological conditions and exposure to climate change vary widely within the region. 

Yet combining the assessments can help identify areas in the region where multiple ecosystem services may be exposed to climate change and other stressors. To summarize 

composite exposure of ecosystem services to climate-related changes, each ranger district is ranked based on its score on four exposure measures: intersecting climate-related 

stressors (likelihood of vegetative change, wildfire hazard, and insect and disease mortality), important surface watersheds, timber products, and livestock grazing resources. 

Recreation is not included as a ranked exposure measure because recreation visitation 

is not directly compared to downscaled climate projections that may affect benefits 

derived from recreation. For each of the four measures, ranger districts are given a score 

based on their quartile rankings, where the lowest exposure quartile receives a score of 

1, and the highest exposure quartile receives a score of 4. Summing the quartile scores 

across the four measures yields a composite exposure score (table 7, fig. 3.13). 

Few ranger districts exhibit high or low exposure across all measures. Canjilon 

Ranger District in the Carson National Forest ranks in the lowest quartile for all four 

measures, resulting in the lowest possible composite exposure score. Smokey Bear 

Ranger District in the Lincoln National Forest ranks in the highest quartile for all measures and has the highest composite score. Aside from these two districts, all districts 

fall somewhere between the extremes in the composite exposure ranking.

Summary

This chapter has attempted to assess the potential effects of climate-related 

changes to national forests and grasslands on the provision of ecosystem services in the 

Southwest. The assessment is based on a geospatial intersection of projected effects of 

climate change on vegetation, the existence of climate-related stressors (wildfire hazard 

and insect and disease mortality), and exposure of ecosystem services to climateinduced vegetative change. 

Broad-scale assessments of climate change impacts suggest that the Southwest will 

experience increases in temperature, more frequent and long-lasting drought, reduced 

surface water runoff and stream flows, more frequent and intense wildfires, and changing disturbance and natural hazard regimes. Yet significant heterogeneity of climate 

effects within the region is likely and exposure of climate-sensitive ecosystem services 

also shows within-region variation. For example, the Tonto Basin Ranger District in 

the Tonto National Forest ranks in the lowest exposure category for both intersecting 

stressors and timber products, but in the highest exposure category for municipal surface 

water and forage for livestock. The Wilderness Ranger District in the Gila National 

Forest shows the opposite results, even though the overall exposure score is similar to 

the Tonto Basin Ranger District. Although both ranger districts can be categorized as 

having moderate exposure to climate-related forest changes overall, differences in the 

sources of climate exposure and the ecosystem services exposed may be helpful for 

identifying adaptation and management priorities at a local level.
USDA Forest Service RMRS-GTR-383. 2018 45

Table 7—Climate change exposure quartile ranks, by forest unit and ranger district.

Quartile ranks

National forest unit and ranger district

Veg. 

change

Intersecting 

stressors

Surface 

water

Livestock 

grazing

Timber 

products

Total exposure rating

Apache-Sitgreaves (mean) 2.4 3 1.8 2.2 3 12.4

Alpine 1 3 2 1 3 10

Black Mesa 4 4 3 3 4 18

Clifton 2 2 1 4 2 11

Springerville 1 2 2 1 2 8

Lakeside 4 4 1 2 4 15

Carson (mean) 1.5 2.2 2.2 1.2 2.2 9.2

Canjilon 1 1 1 1 1 5

El Rito 2 3 2 1 2 10

Jicarilla 3 1 4 1 3 12

Camino Real 1 4 3 1 3 12

Tres Piedras 1 1 1 2 2 7

Questa 1 3 2 1 2 9

Cibola (mean) 3.3 1.5 2.3 3.5 3 13.5

Mt. Taylor 3 1 2 3 4 13

Magdalena 3 2 2 4 4 15

Mountainair 4 2 2 3 3 14

Sandia 3 3 3 - 2 n/a

Black Kettle - - - - - n/a

Kiowa-Rita Blanca 3 1 3 4 1 12

Coconino (mean) 2.7 3 3.7 2 3 14.3

Peaks 3 4 3 2 4 16

Mormon Lake 1 3 3 - 3 n/a

Red Rock 2 1 4 2 1 10

Mogollon Rim 3 4 4 2 4 17

Coronado (mean) 3.8 2.8 1.4 3.8 1.4 13.2

Douglas 3 2 1 4 2 12

Nogales 4 2 2 4 1 13

Sierra Vista 4 4 1 4 1 14

Safford 4 4 1 4 2 15

Santa Catalina 4 2 2 3 1 12

Gila (mean) 1.8 2.7 1.3 2.3 3.3 11.5

Black Range 2 3 3 3 4 15

Glenwood 2 3 1 3 3 12

Wilderness 3 4 1 1 4 13

Reserve 1 2 1 2 3 9

Silver City 2 3 1 3 3 12

Quemado 1 1 1 2 3 8
46 USDA Forest Service RMRS-GTR-383. 2018

Kaibab (mean) 4 3.3 2.7 2.3 4 16.3

Williams 4 4 4 3 4 19

North Kaibab 4 3 2 2 4 15

Tusayan 4 3 2 2 4 15

Lincoln (mean) 3.7 3 3.3 3.7 3 16.7

Smokey Bear 4 4 4 4 4 20

Sacramento 3 4 3 3 4 17

Guadalupe 4 1 3 4 1 13

Prescott (mean) 1.7 1 3.3 2.3 1.7 10

Chino Valley 2 1 4 4 2 13

Bradshaw 1 1 2 1 2 7

Verde 2 1 4 2 1 10

Santa Fe (mean) 1.6 3 2.4 1.6 2.6 11.2

Coyote 1 2 1 1 2 7

Cuba 1 2 2 1 2 8

Jemez 2 4 3 1 3 13

Pecos-Las Vegas 2 3 3 3 3 14

Española 2 4 3 2 3 14

Tonto (mean) 2.8 2 4 2.8 1.3 13

Cave Creek 3 1 4 3 1 12

Globe 3 3 4 4 1 15

Mesa 4 2 4 1 1 12

Payson 3 3 4 3 3 16

Pleasant Valley 2 2 4 2 1 11

Tonto Basin 2 1 4 4 1 12

Valles Calderaa 1 2 3 - 2 n/a

For each measure, a value of 1 indicates the lowest exposure quartile, while a value of 4 indicates highest exposure. Higher numbers for the 

total exposure rating indicate greater exposure to climate-related ecological changes. Numbers in italics for each forest are means of the 

values for each ranger district within that forest.

a A national preserve administered by the National Park Service.

Table 7—Continued.

Quartile ranks

National forest unit and ranger district

Veg. 

change

Intersecting 

stressors

Surface 

water

Livestock 

grazing

Timber 

products

Total exposure rating
USDA Forest Service RMRS-GTR-383. 2018 47

The exposure assessment is subject to several limitations. First, the assessment 

does not quantify all of the pathways that may lead to ecological changes in the future. 

The focus is on potential climate-induced vegetative change and interactions with 

existing climate-related disturbances. Other pathways, such as the effects of changing 

population distributions or direct effects of changes in temperature and precipitation on 

ecosystem services, may be correlated with vegetative change and disturbances. 

Second, this chapter specifically addresses exposure to only a handful of the 

ecosystem services that may be important in the region. More comprehensive or 

geographically detailed data are needed to conduct a similar assessment on additional 

ecosystem services. Some of these data may be available for particular forest units or 

ranger districts at the local level. 

Finally, projections of vegetative change are created by using a single emissions 

scenario (A1B) and global circulation model (CGCM3); results for specific geographic 

areas or ecosystem services may be sensitive to the choice of scenario and model to 

generate likelihood of vegetative change ratings. Neither the emissions scenario nor the 

future realization of climate-related ecological changes is known with certainty.

Figure 3.13—Composite risk scores for climate and disturbance vulnerability of ecosystem services in the Forest Service 

Southwestern Region, by ranger district. Orange and red areas (higher composite scores) indicate higher combined 

exposure; green and blue areas (lower composite scores) indicate lower combined exposure.
48 USDA Forest Service RMRS-GTR-383. 2018

Chapter 4: Economic Dependence on Ecosystem 

Services Supplied by Southwestern Region 

National Forests and Grasslands

Communities in the Southwest depend on ecosystem goods and services from 

national forests and grasslands in many ways and to various degrees. Patterns of use 

and trade of ecosystem services, along with other economic characteristics at the local 

and regional level, influence economic dependence. This chapter provides an analysis of 

economic dependence on a subset of the ecosystem goods and services listed in table 1.

Regional and subregional economic reliance on ecosystem services can be measured through market transactions and nonmarket expressions of value. Contributions 

to employment and income from market transactions of ecosystem goods are measured 

by using input-output (I-O) analysis conducted with IMPLAN (IMPLAN 2013). We 

examine variation across the region in employment and income that can be attributed 

to ecosystem services from Forest Service lands. This analysis provides insight into the 

economic reliance of local economies and sectors on ecosystem goods and services provided by national forests and grasslands in the region. The I-O analysis examines goods 

and services related to recreation, grazing, and forest products. 

This chapter also considers economic dependence on other ecosystem services in 

a qualitative way. These assessments evaluate the reliance on water provided by forests, firewood gathered for personal use and home heating, offsite amenity values, and 

cultural and spiritual values to the degree possible with available regionwide data and 

information.

Assessing Market Dependence on Forest Resource Sectors by Using 

Input-Output Analysis

The conceptual model of household production in Chapter 2 relies on the assumption that households use ecosystem goods and services from forests and grasslands to 

produce other utility-bearing goods and services. Many inputs from ecosystems are 

obtained or accessed through market transactions. The value of those transactions (in 

dollars) provides evidence of the amount of economic activity that depends on ecosystem services traded in markets. Further, well-being in the household production model 

depends in part on household income; examining employment and income derived from 

market transactions of ecosystem services indicates the degree to which household wellbeing is dependent on ecosystem-derived goods and services that may be affected by 

climatic changes.

Economic contributions of ecosystem services are measured by estimating the 

direct jobs and labor income generated by expenditures made by recreational visitors to 

national forest lands, livestock grazing on national forests and grasslands, and logging 

and processing of forest products derived from national forests. These broad economic 

sectors are selected because they are closely tied to ecosystem services provided by 

national forests and grasslands in the region and direct expenditure and activity data for 

these sectors are available for all forests in the region. Additional indirect and induced 
USDA Forest Service RMRS-GTR-383. 2018 49

multiplier effects on income and employment are generated by the direct expenditures 

in the I-O model due to relationships between economic sectors (e.g., input markets that 

support markets where direct expenditures occur). Indirect contributions occur when a 

firm purchases supplies and services (inputs) from firms in other sectors to produce its 

products (outputs); induced contributions occur as a result of household spending of 

income from direct and indirect employment. The direct and multiplier effects compose 

the total economic contribution to the local economy captured in the analysis. 

I-O analysis is a means of examining relationships within an economy, both 

between businesses and between businesses and final consumers; it captures all monetary market transactions for consumption in a given time period (Miernyk 1965). The 

resulting representation allows examination of the effect of a change in one or several 

economic activities on an entire economy, all else constant (see box 2).

IMPLAN is the I-O modeling tool most commonly used by the Forest Service. The 

IMPLAN modeling system allows the user to build regional economic models of one or 

more counties for a particular year. The models for this analysis used the 2012 IMPLAN 

data. IMPLAN translates changes in final demand for goods and services into resulting 

changes in economic effects, such as labor income and employment in the affected area. 

This analysis examines current contributions to regional economic models specific to 

each national forest and grasslands in the Southwestern Region. 

Estimating current economic contributions attributable to recreation, livestock 

grazing, and forest products can indicate the amount of market economic activity that 

may be sensitive to climate-related changes in the supply of ecosystem services. The 

contribution analysis can be paired with assessments of exposure (Chapter 3) and 

BOX 2—Using Input-Output Models for Regional Economic Impact Analysis 

Basic input-output (I-O) models are the most widely used tools of regional economic impact analysis and are 

well suited for the economic evaluation of resource use. The basic production relations of an I-O model are 

comprehensive with respect to all inputs, not just primary factors (capital and labor) (Rose et al. 2000). I-O 

analysis is well suited to the study of community vulnerability to climate change because of the interest in 

identifying the amount of economic activity that depends on ecosystem services. Specifically, we are interested 

in identifying communities in existing local economies that may be exposed to the risk of significant economic 

shocks due to changes in the provision of ecosystem services stemming from climate change. 

I-O models can provide a partial-equilibrium analysis of the amount of economic activity that is associated with 

different economic sectors. Because I-O models account for relationships between inputs and sectors in the 

economy, the models can describe how changes in one sector relate to employment and income in other sectors 

and the total economy. However, I-O models do not necessarily account for adjustments in the relationships 

between inputs and sectors that can occur given a change in one sector. The net economic effects of climate 

change will depend on market shifts to substitute inputs and final goods and services as climate change alters 

the supply of ecosystem services. A full accounting of these shifts would require computable general equilibrium 

(CGE) models, which are based on the simultaneous optimizing behavior of individual consumers and firms, 

subject to economic account balances and resource constraints (Rose et al. 2000). Shifts in behavior that alter 

market relationships may be part of the adaptive response of some communities in the region, which were 

examined separately from economic dependence in this study (see Chapter 5) and may involve management 

responses (see Chapter 6).

The I-O application in this study was conducted using IMPLAN, a proprietary system of I-O tools and data that is 

widely used among researchers and practitioners in the United States. Using the IMPLAN system is convenient 

for providing managers, policy makers, and researchers involved in climate change analysis with a common and 

replicable method for assessing vulnerability to changes in ecosystem service provision. IMPLAN consists of an 

extensive national and regional database, algorithms for generating I-O tables for any county or county group in 

the United States, and algorithms for performing impact analyses. IMPLAN has been used to examine impacts 

from climate change on numerous resources including agriculture, water, energy (Rosenberg 1993), forestry 

(Rose et al. 2000; Rosenberg 1993), and recreation (Richardson and Loomis 2004).
50 USDA Forest Service RMRS-GTR-383. 2018

adaptive capacity (Chapter 5) to draw inferences about the vulnerability of communities 

to reductions in well-being due to a changing climate. However, this approach does not 

explicitly model how projected climate-related changes to ecosystems will affect markets in the Southwest. 

An advantage of the contribution analysis approach is that it avoids making assumptions about the structure of the regional economy in the future as climate change 

affects ecosystem services (Rosenberg 1993). Similarly, assumptions about input substitution effects and the degree of interregional trading that results from the heterogeneous 

effects of climate change on ecosystem services can have large effects on economic 

impact analyses (Rose et al. 2000). Thus, the contribution analysis provides a current 

“snapshot” of how deeply ecosystem services are ingrained in the economy at present, but not necessarily what the economy will look like in the future under a changed 

climate.

The I-O analysis was conducted according to the following general steps. Each 

step is described in detail in the following sections.

• Step 1: Determine the set of economic subsectors that rely on recreation access, 

rangeland for forage, and forest products as ecosystem services inputs.

• Step 2: Specify groups of counties (forest economic impact areas) where direct 

expenditures occur for each national forest.

• Step 3: Obtain estimates of direct national forest and grassland activities associated 

with recreation, rangeland grazing, and forest products.

• Step 4: Run IMPLAN I-O model with the Forest Service Excel®-based tool 

Apheleia to obtain indirect and induced contributions to employment and income 

derived from direct expenditures in each broad sector for each forest economic 

impact area. 

Economic Subsectors Related to Recreation, Rangeland Grazing, and Forest Products

This section presents data sources and assumptions specific to recreation, livestock 

grazing, and forest resources that were applied in the market analysis of ecosystem services. This portion of the analysis addressed the extent to which employment and labor 

income in resource-related sectors is dependent on use of ecosystem goods and services. 

Recreation: Forest Service land within the Southwest provides a variety of recreation opportunities. Data on visitation across the region from the National Visitor 

Use Monitoring (NVUM) survey indicate about 13.8 million annual recreation visits to 

national forests and grasslands in the Southwest (USDA Forest Service n.d.). 

Analyses of expenditures reported by national forest visitors show the primary 

factor determining the amount spent by a visitor was the type of trip taken and not the 

specific activity or forest visited (White and Stynes 2008). The six types of recreation 

trips are defined as follows: 

Visitors who reside greater than 50 miles from the visited forest:

• Nonlocal residents on day trips,

• Nonlocal residents staying overnight at a Forest Service site, and

• Nonlocal residents staying overnight not at a Forest Service site.
USDA Forest Service RMRS-GTR-383. 2018 51

Visitors who live within 50 miles of the visited forest:

• Local residents on day trips,

• Local residents staying overnight at a Forest Service site, and

• Local residents staying overnight not at a Forest Service site.

The type of trip captures the unique spending patterns by whether visits were local or not (table 8). These shares describe the proportion of visits to a forest across all 

activities that can be categorized by each visit type. 

Traditional economic impact analysis often excludes spending from local residents 

because the recreation expenditures of local persons do not represent new money introduced into the economy. For example, if Forest Service-related opportunities were not 

present, residents would probably participate in other locally based activities and their 

money would still be spent in the local economy. However, climate change is expected 

to alter opportunities regardless of land ownership and administration, which will alter 

the availability of substitute recreation opportunities in the local area. Therefore, this 

analysis included local visitor expenditures to assess the total economic contribution of 

national forest and grassland recreation. Differences in economic contributions by local 

and nonlocal visitors are accounted for by reporting their expenditure profiles separately, 

as specified in White et al. (2013) (table 9).

Similarly, nonprimary visits, defined as visits where the national forest site was not 

the primary destination for a given recreational trip, were also included because climate 

change may affect decisions about recreation opportunities that are complementary 

to national forest sites. The primary recreation activity may have occurred off Forest 

Service land, but the consequences of climate change may also affect the adjacent 

Federal, State, and private lands.

Table 8—Forest visitation shares in the U.S. Forest Service Southwestern Region by trip type.

Nonlocal origin visits Local origin visits

National forest Day

Overnight – 

FS site

Overnight, 

non-FS site Day

Overnight – 

FS site

Overnight, 

non-FS site

Apache-Sitgreaves 0.08 0.27 0.12 0.50 0.02 0.01

Carson 0.11 0.07 0.25 0.54 0.02 0.01

Cibola 0.06 0.01 0.11 0.81 0 0.01

Coconino 0.11 0.06 0.25 0.56 0.02 0

Coronado 0.05 0.04 0.04 0.82 0.04 0.01

Gila 0.21 0.17 0.11 0.46 0.04 0.01

Kaibab 0.11 0.17 0.11 0.59 0.01 0.01

Prescott 0.06 0.10 0.04 0.76 0.04 0

Lincoln 0.15 0.16 0.13 0.54 0.02 0

Santa Fe 0.13 0.06 0.07 0.71 0.01 0.02

Tonto 0.06 0.08 0.02 0.76 0.07 0.01

Source: RMRS calculations of National Visitor Use Monitoring survey responses, round 2 (Apache-Sitgreaves, Carson, Lincoln, Santa Fe, 

and Tonto) and round 3 (Cibola, Coconino, Coronado, Gila, Kaibab, and Prescott) (USDA Forest Service 2015b). Nonprimary visits are 

included with local-origin day visits.
52 USDA Forest Service RMRS-GTR-383. 2018

Table 9—Visitor spending profiles per party per visit to national forests and grasslands in the Forest Service Southwestern 

Region, by trip segment type, in 2009 dollars.

Nonlocal origin visits Local origin visits

Spending category Day

Overnight 

– FS site

Overnight, 

non-FS site Day

Overnight 

– FS site

Overnight, 

non-FS site

Nonprimary

Motel 0.00 33.54 151.77 0.00 5.36 33.84 114.86

Camping 0.00 26.81 18.85 0.00 23.63 17.11 11.95

Restaurant 15.30 26.31 111.34 5.19 6.78 33.99 88.62

Groceries 8.63 55.65 68.29 6.31 67.30 54.54 43.36

Gas and oil 23.16 52.67 71.17 12.83 37.57 40.18 48.40

Other transportation 0.58 1.83 3.98 0.13 0.49 1.09 3.26

Entry fees 4.56 8.93 18.39 2.17 3.76 6.86 11.11

Recreation and 

entertainment

4.34 7.70 27.13 1.50 3.50 5.67 16.71

Sporting goods 2.94 12.19 15.18 4.16 11.23 12.85 6.44

Souvenirs and other 

expenses

3.15 7.80 28.10 0.72 2.85 6.87 25.83

Total 62.65 233.44 514.20 33.02 162.48 212.99 370.54

Source: White et al. (2013: table 8). Spending profile estimates exclude visitors whose primary activity was downhill skiing (see table 10).

Although the primary factor determining the amount of money spent is the type of 

trip and not the activity, this analysis examines visits associated with climate-sensitive 

recreation categories based on activity participation estimates for Southwestern Region 

forests. Climate-sensitive recreation activities are grouped into five categories: Warmweather activities, wildlife-related activities, snow-based winter activities, gathering 

forest products, and water-based activities (not including fishing). Downhill skiing is 

examined separately from other winter activities given the availability of expenditure 

profiles unique to skiing (table 10) and the status of this activity as the highest expenditure per visit across all activity types for all national forests (White and Stynes 2010). 

Within the Southwest downhill skiing occurs in the Carson, Cibola, Coconino, Kaibab, 

Lincoln, and Santa Fe National Forests. The most recent available NVUM estimates 

indicate about 600,000 downhill skiing visits occur in these forests annually.

Forest-specific national forest visitation shares by trip type (table 8) and visitation 

estimates for downhill skiing and the climate-sensitive recreation categories (tables 11 

and 12) were combined with expenditure profiles (tables 9 and 10) to generate estimated 

expenditures by recreation activity category and trip type. These estimates were then 

used in IMPLAN and Apheleia to calculate direct, indirect, and induced employment 

and income contributions for each forest in the region. 

Rangeland grazing: Grazing of livestock on Forest Service lands plays an important economic and social role for communities in the Southwest, and area residents 

identify with the tradition, land use, and history of this activity. In 2013, Pinal County, 

in southern Arizona, and Curry County, in northeastern New Mexico, were the region’s 

first and second largest cattle producers, containing 12 and 7 percent of the regional cattle inventory, respectively (about 2.8 million cattle and calves) (USDA 2013). Ranching 

on Southwestern Region forests includes both large and small operations. In some areas 
USDA Forest Service RMRS-GTR-383. 2018 53

small-scale noncommercial family herds for local use have been a tradition for centuries 

(Atencio 2004). 

The calculation of direct employment associated with cattle and sheep grazing on 

Forest Service lands followed protocol developed by economists at the Bureau of Land 

Management and Forest Service. First, the number of hired farm laborers was taken 

from the Census of Agriculture for the beef cattle ranching and sheep and goat farming 

subsectors. Second, unpaid and self-employed individuals are considered because the 

Census of Agriculture data do not include these individuals. The 2005–2009 American 

Community Survey includes information on the class of worker (e.g., self-employed, 

local government, unpaid family worker) by two-digit North American Industry 

Classification System industry. Industries included in the grazing sector are described in 

table 13.

To determine how Forest Service forage contributed to industry employment (hired 

laborers, unpaid, and self-employed individuals), the number of direct jobs per unit of 

forage was calculated. Data from the Census of Agriculture on total inventory of beef 

cows that calved, ewes 1 year or older, and all goats were used to calculate total forage 

requirements. Total cattle annual animal unit months (AUMs) required = total inventory 

× 12; Total sheep annual AUMs required = (sheep and lambs or goats × 12)/5. The ratio 

of employment to forage requirements was then used to calculate direct contributions 

from forests in the Southwestern Region by using data on AUMs that were authorized 

under a term grazing permit or lease in 2012. Indirect and induced contributions were 

calculated in IMPLAN by using analysis-by-parts, a method of calculating the impacts 

of a particular activity by separating out the various spending components of that activity and analyzing their specific impacts. This is done because production functions for 

cattle ranching and other animal production are not considered completely adequate for 

capturing indirect and induced contributions.

Table 10—Visitor spending profiles of downhill skiers per party per visit to national forests and grasslands 

in the Forest Service Southwestern Region, by trip segment type, in 2009 dollars.

Nonlocal origin visits Local origin visits

Spending category Day Overnight Day Overnight

Motel 0 237.37 0 36.34

Camping 0 0.62 0 18.37

Restaurant 22.52 158.95 13.57 31.47

Groceries 4.60 75.86 3.49 51.01

Gas and oil 20.73 50.38 9.99 47.45

Other transportation 0 3.10 0.01 1.12

Entry fees 45.98 145.32 20.82 6.56

Recreation and entertainment 31.00 84.33 13.13 5.40

Sporting goods 5.32 22.04 3.13 12.62

Souvenirs and other expenses 1.85 20.75 0.77 6.81

Total 132.00 798.72 64.91 217.14

Source: White et al. 2013: table 15.
54 USDA Forest Service RMRS-GTR-383. 2018

Table 11—Forest visitor estimates in the Forest Service Southwestern Region, by trip type and activity category, excluding 

downhill skiing.

Nonlocal origin visits Local origin visits

National forest and activity 

category Day

Overnight 

– FS site

Overnight, 

non-FS site Day

Overnight – 

FS site

Overnight, 

non-FS site

Apache-Sitgreaves

Warm weather 49,661 167,607 74,492 310,383 12,415 6,208

Winter (not incl. downhill skiing) 0 0 0 0 0 0

Wildlife 29,943 101,057 44,914 187,142 7,486 3,743

Gathering forest products 609 2,054 913 3,804 152 76

Water (not incl. fishing) 2,069 6,984 3,104 12,933 517 259

Carson

Warm weather 30,632 19,493 69,617 150,374 5,569 2,785 

Winter (not incl. downhill skiing) 9,318 5,930 21,178 45,745 1,694 847

Wildlife 11,400 7,255 25,909 55,964 2,073 1,036

Gathering forest products 2,280 1,451 5,182 11,193 415 207

Water (not incl. fishing) 991 631 2,253 4,866 180 90

Cibola

Warm weather 55,796 9,299 102,293 753,250 0 9,299

Winter (not incl. downhill skiing) 513 86 941 6,932 0 86

Wildlife 8,558 1,426 15,689 115,529 0 1,426

Gathering forest products 1,369 228 2,510 18,485 0 228

Water (not incl. fishing) 0 0 0 0 0 0

Coconino

Warm weather 208,813 113,898 474,576 1,063,049 37,966 0

Winter (not incl. downhill skiing) 2,839 1,548 6,452 14,452 516 0

Wildlife 18,926 10,323 43,013 96,349 3,441 0

Gathering forest products 1,577 860 3,584 8,029 287 0

Water (not incl. fishing) 4,731 2,581 10,753 24,087 860 0

Coronado

Warm weather 91,360 73,088 73,088 1,498,312 73,088 18,272

Winter (not incl. downhill skiing) 0 0 0 0 0 0

Wildlife 9,975 7,980 7,980 163,597 7,980 1,995

Gathering forest products 608 487 487 9,975 487 122

Water (not incl. fishing) 1,217 973 973 19,951 973 243

Gila

Warm weather 41,869 33,894 21,932 91,714 7,975 1,994

Winter (not incl. downhill skiing) 0 0 0 0 0 0

Wildlife 33,344 26,993 17,466 73,040 6,351 1,588

Gathering forest products 108 87 57 236 21 5

Water (not incl. fishing) 108 87 57 236 21 5
USDA Forest Service RMRS-GTR-383. 2018 55

Nonlocal origin visits Local origin visits

National forest and activity 

category Day

Overnight 

– FS site

Overnight, 

non-FS site Day

Overnight – 

FS site

Overnight, 

non-FS site

Kaibab

Warm weather 21,084 32,584 21,084 113,087 1,917 1,917

Winter (not incl. downhill skiing) 0 0 0 0 0 0

Wildlife 7,229 11,172 7,229 38,773 657 657

Gathering forest products 251 388 251 1,346 23 23

Water (not incl. fishing) 251 388 251 1,346 23 23

Lincoln

Warm weather 68,747 73,330 59,580 247,487 9,166 0

Winter (not incl. downhill skiing) 0 0 0 0 0 0

Wildlife 6,036 6,438 5,231 21,729 805 0

Gathering forest products 921 982 798 3,315 123 0

Water (not incl. fishing) 0 0 0 0 0 0

Prescott

Warm weather 32,103 53,505 21,402 406,636 21,402 0

Winter (not incl. downhill skiing) 0 0 0 0 0 0

Wildlife 4,678 7,796 3,118 59,249 3,118 0

Gathering forest products 148 246 98 1,871 98 0

Water (not incl. fishing) 1,083 1,805 722 13,721 722 0

Santa Fe

Warm weather 119,517 55,162 64,355 652,747 9,194 18,387

Winter (not incl. downhill skiing) 16,380 7,560 8,820 89,458 1,260 2,520

Wildlife 13,320 6,148 7,172 72,746 1,025 2,049

Gathering forest products 180 83 97 983 14 28

Water (not incl. fishing) 1,440 665 775 7,864 111 222

Tonto

Warm weather 95,056 126,742 31,685 1,204,049 110,899 15,843

Winter (not incl. downhill skiing) 0 0 0 0 0 0

Wildlife 40,327 53,769 13,442 510,809 47,048 6,721

Gathering forest products 0 0 0 0 0 0

Water (not incl. fishing) 25,636 34,182 8,545 324,728 29,909 4,273

Source: RMRS calculations of National Visitor Use Monitoring survey responses, round 2 (Apache-Sitgreaves, Carson, Lincoln, Santa Fe, 

and Tonto) and round 3 (Cibola, Coconino, Coronado, Gila, Kaibab, and Prescott) (USDA Forest Service 2015b). Visits are grouped into 

activity categories based on reported primary activity participated in during the visit. Activity and segment estimates are calculated as 

the product of total forest visitation, activity category visitation shares (see table 6), and forest-level segment shares. Segment shares are 

calculated for each forest and are not activity-specific.

Table 11—Continued.
56 USDA Forest Service RMRS-GTR-383. 2018

Forest Products: Commercial timber harvests from Forest Service lands in Arizona 

and New Mexico have varied over time with changing market and policy conditions. 

Trends on the use of forest products from the Southwestern Region were obtained from 

reports on cut and sold timber (USDA Forest Service 2013b). Timber harvests in much 

of the 1990s decreased sharply from the late 1980s on national forests throughout the 

Nation and in the Southwest (fig. 4.1) and remained at comparatively low levels during 

the 2000s, suggesting a change in the role of public forest land from traditional commodity use to ecosystem service protection. 

Timber harvest data from the cut and sold reports depicted in figure 4.2 were 

reported by forest and then used to estimate contributions in IMPLAN. Data used to 

estimate the direct effects from timber harvest and processing were provided by the 

University of Montana’s Bureau of Business and Economic Research (BBER) (Morgan 

et al. 2014). Industries included in the forest products sector are described in table 14. 

The national data are broken into multi-State regions and are considered more accurate than those available from IMPLAN. The Four Corners States (Arizona, Colorado, 

New Mexico, and Utah) were used as a reference area for this analysis given the 

concentration of processing facilities in Arizona and New Mexico that receive timber 

volume originating from Southwestern Region forests. The BBER data represent the 

results of mill censuses that correlate production, employment, and labor income. 

Table 12—Forest visitor estimates for downhill skiing in the Forest Service 

Southwestern Region.

National forest Day Overnight

Carson 164,558 134,639

Cibola 42,446 1,769

Coconino 80,004 49,035

Kaibab 1,118 1,164

Lincoln 32,034 9,569

Santa Fe 86,190 28,730

Source: RMRS calculations of National Visitor Use Monitoring survey responses, round 

2 (Apache-Sitgreaves, Carson, Lincoln, Santa Fe, and Tonto) and round 3 (Cibola, 

Coconino, Coronado, Gila, Kaibab, and Prescott) (USDA Forest Service 2015b). Visits 

are grouped into activity categories based on reported primary activity participated in 

during the visit.

Table 13—IMPLAN (IMPLAN 2013) two-digit NAICS sectors used to assess economic contribution of livestock grazing.

IMPLAN 

sector Description

Regional 

employment

Percentage of 

total regional 

employment

Total, all livestock grazing sectors 18,176 0.38%

11 Cattle ranching and farming 15,766 0.33%

14 Animal production, except cattle and poultry and eggs 2,410 0.05%
USDA Forest Service RMRS-GTR-383. 2018 57

Figure 4.1—Annual timber removed from national forest land in Arizona and New Mexico (blue line, right axis) and total 

from all national forest land in the United States (red line, left axis). (Data source: USDA Forest Service 2013b.)

Figure 4.2—Average cut timber volume and value in the Forest Service Southwestern Region, FY 2010–FY 2012. 

Bar height indicates cut volume from each forest in hundred cubic feet (CCF, left axis); the points indicate 

the total market value (dollars, right axis) of cut timber from each forest. (Data source: USDA Forest Service 

2013b.)
58 USDA Forest Service RMRS-GTR-383. 2018

Delineation of Forest Area County Groupings for Analysis of Economic Contributions

The economic contributions from Southwestern Region forests depend on the economic characteristics of the area examined. The unit of analysis for assessing economic 

contributions of ecosystem services is the forest economic impact area, which is a 

grouping of counties that exhibit functional economic integrity relative to the ecosystem 

goods and services examined.

Defining the economic impact areas followed Forest Service protocols (Retzlaff 

2009). The first step in delineating analysis areas is to identify the counties where 

market transactions associated with recreation, range, and timber are likely to occur. 

We then further refined broad areas based on additional criteria developed by the Forest 

Service Ecosystem Management Coordination Office. These specialists’ method assumes that the location of market transactions is represented by data on the NVUM 

visitor market area and the location of range permittees and timber bid winners.

The relevant market area for recreation was based on travel distances reported in 

the NVUM survey. To be included in an economic impact area, a county must be 

(1) classified as part of the 50 percent market area for the forest (group of counties from 

which at least 50 percent of forest visits originated), (2) within 50 road miles (leading to 

a nearby town) from the forest boundary, and (3) contiguous with the rest of the counties in the study area; in addition, if the county was in a classified metropolitan area, 

(4) it must contain Forest Service land. The fourth criterion allows for inclusion of 

Office of Management and Budget-designated metropolitan counties with stronger 

recreational ties to particular forest units. Many metropolitan counties may contribute to 

Table 14—IMPLAN (IMPLAN 2013) sectors used to assess economic contributions of timber and forest products.

IMPLAN 

sector Description

Regional 

employment

Percentage of 

total regional 

employment

Total, all timber and forest products sectors 27,418 0.57%

 15 Forestry, forest products, and timber tract production 80 <0.01%

 16 Commercial logging 382 0.01%

 19 Support activities for agriculture and forestry 20,842 0.43%

 95 Sawmills and wood preservation 258 0.01%

 96 Veneer and plywood manufacturing 18 <0.01%

 97 Engineered wood member and truss manufacturing 892 0.02%

 98 Reconstituted wood product manufacturing 93 <0.01%

 99 Wood windows and doors and millwork manufacturing 1,604 0.03%

100 Wood container and pallet manufacturing 837 0.02%

102 Prefabricated wood building manufacturing 295 0.01%

103 All other miscellaneous wood product manufacturing 297 0.01%

104 Pulp mills 17 <0.01%

105 Paper mills 465 0.01%

106 Paperboard mills 47 <0.01%

107 Paperboard container manufacturing 1,291 0.03%
USDA Forest Service RMRS-GTR-383. 2018 59

the 50-percent visitor market area for a particular forest (given their larger populations 

than rural counties adjacent to a national forest), but market transactions associated with 

forest recreation make up smaller portions of their overall economic activity. In addition, inclusion of these counties would dilute important local economic relationships 

between forest recreation and adjacent rural counties.

Both range and timber data were obtained from the Forest Service reporting tool 

I-Web, which records the addresses as well as grazing head-months and cut timber 

volume for grazing permittees and timber bidders, respectively. A county is included in 

the economic impact area for a grassland or forest if it contains permittees or timber bid 

winners that utilized at least 5 percent of all authorized AUMs or timber sale volume for 

that grassland or forest unit.

For those counties where direct expenditures for range, timber, or recreation were 

identified, a laborshed analysis was performed by using data on where workers live 

taken from the decennial census. The main criterion used in this analysis is that counties must provide at least 25 percent of local jobs to forest analysis areas. Applying this 

protocol yields 12 analysis areas unique to the labor market and recreation, range, and 

timber resources provided by each forest unit in the region (table 15).

Contributions to individual counties may accrue from multiple forests. These 

forest contributions are unique and thus no double-counting occurs. For example, contributions to employment in Gila County are tied to unique recreation visitation in the 

Apache-Sitgreaves, the Coconino, and the Tonto National Forests. Total contributions 

(direct, indirect, and induced) to analysis areas from Forest Service resource use were 

not distinguished at the individual county level.

Table 15—Definitions of forest economic impact areas.

National forest (NF) or national 

grasslands (NG) unit Counties included in analysis area

Apache-Sitgreaves NF Apache (AZ), Coconino (AZ), Gila (AZ), Graham (AZ), Greenlee (AZ), Maricopa (AZ), 

Navajo (AZ)

Carson NF Alamosa (CO), Conejos (CO), Mora (NM), Rio Arriba (NM), San Miguel (NM), Taos 

(NM)

Cibola NF Bernalillo (NM), Catron (NM), Cibola (NM), Harding (NM), McKinley (NM), Rio Arriba 

(NM), Sandoval (NM), Santa Fe (NM), Socorro (NM), Torrance (NM), Union (NM)

Cibola NG Harding (NM), Mora (NM), Union (NM), Cimarron (OK), Roger Mills (OK), Dallam (TX), 

Gray (TX), Hemphill (TX)

Coconino NF Coconino (AZ), Gila (AZ), Maricopa (AZ), Navajo (AZ), Pinal (AZ), Yavapai (AZ)

Coronado NF Cochise (AZ), Pima (AZ), Pinal (AZ), Santa Cruz (AZ)

Gila NF Apache (AZ), Catron (NM), Grant (NM)

Kaibab NF Coconino (AZ), Mohave (AZ), Navajo (AZ), Yavapai (AZ), Garfield (UT), Kane (UT), 

Washington (UT)

Lincoln NF Chaves (NM), Eddy (NM), Lincoln (NM), Otero (NM), El Paso (TX)

Prescott NF Maricopa (AZ), Yavapai (AZ)

Santa Fe NF McKinley (NM), Mora (NM), Rio Arriba (NM), Sandoval (NM), San Miguel (NM), Santa 

Fe (NM)

Tonto NF Coconino (AZ), Gila (AZ), Maricopa (AZ), Pinal (AZ)
60 USDA Forest Service RMRS-GTR-383. 2018

Forest Area Economic Contributions to Employment and Income of National Forest 

Climate-Sensitive Recreation, Rangeland Grazing, and Forest Products

Economic contributions associated with uses on Southwestern Region forests 

(climate-sensitive recreation, rangeland grazing, and timber) (table 16) were estimated 

with the IMPLAN I-O model, resource data, and the assumptions described earlier. 

Forests with relatively high contributions of resource sectors to total area income 

and employment include Carson and Gila national forests and the Cibola National 

Grasslands (consisting of grasslands in eastern New Mexico, Texas, and Oklahoma that 

are administered by Cibola National Forest and included as a separate unit for the contribution analysis). Economic activity related to national forests and grasslands accounts 

for between 0.9 and 1.7 percent of employment in these economic impact areas.

Other forests contribute more in terms of absolute income and employment, but 

the contributions account for a smaller share of all income and employment in those 

areas. The highest absolute contributions to income and employment are from the 

Coconino, Apache-Sitgreaves, and Tonto National Forests. These forests each contain 

Maricopa County, Arizona in their respective economic impact areas, which explains the 

low contribution relative to total income and employment: Maricopa County includes 

the Phoenix metropolitan area. Yet recreation, grazing, and timber activities in these 

forests account for more than 40 percent of employment and more than 50 percent of 

income contributed by all national forests in the region.

Overall, the contributions from recreation account for most of the employment 

and income derived from natural resource activities in national forests and grasslands. 

Although certain forests have larger economic contributions derived from grazing (e.g., 

Cibola Grasslands, the Gila, the Prescott) or timber (e.g., the Kaibab), recreation activities are the largest contributor to national forest-related employment for six forests, and 

the largest contributor to income for nine forests. Warm-weather recreation activities 

account for the majority of economic contributions in all forests except for the Carson, 

where downhill skiing accounts for the largest economic contribution (table 17).

Dependence on Forest Resources not Measured in Market Transactions

The Importance of Water to Communities and Industry Sectors 

We examined regional dependence on water supplied from Southwestern Region 

forests in order to demonstrate the Forest Service’s role in providing this ecosystem 

service. Estimates of water supply from forests in the region indicate that the Forest 

Service plays an integral role in supplying water, for a variety of uses, across the 

Southwest. 

Water supply estimates that were obtained from Brown et al. (2008) define water 

supply as estimated precipitation minus evapotranspiration. Their estimates are based on 

data for 1953 through 1994, which they note “may not represent future hydrologic conditions.” Because estimates were given for all land ownerships, they provide a reliable 

depiction of dependence on water from Forest Service lands across broad scales, such 

as national forests in the Southwest. It is important to acknowledge that these sources of 

supply are only a portion of the total supply from surface diversions that originate from 

outside the immediate area and sometimes outside the State. For example, the Central 
USDA Forest Service RMRS-GTR-383. 2018 61

Table 16—Average annual total (direct, indirect, and induced) employment and labor income contributed by activities on 

national forests (NFs) and grasslands (NGs) in the Forest Service Southwestern Region, by forest analysis area and resource 

sector.

Climate-sensitive 

recreation Grazing Timber

Total – All 

activities

% Total 

empl.

Employment (full- and part-time jobs)

Number % Number % Number %

Apache-Sitgreaves NF 561 45.9 288 23.5 374 30.6 1,223 0.05

Carson NF 771 77.7 202 20.4 18 1.8 992 1.68

Cibola NF 338 55.6 238 39.1 32 5.3 608 0.10

Cibola NG 4 1.4 285 98.6 0 0.0 289 0.93

Coconino NF 1,553 81.4 238 12.5 117 6.1 1,908 0.08

Coronado NF 387 36.6 671 63.5 0 0.0 1,057 0.17

Gila NF 108 16.2 545 81.6 14 2.1 668 1.57

Kaibab NF 97 26.0 166 44.5 111 29.8 373 0.11

Lincoln NF 251 41.8 262 43.6 88 14.6 601 0.12

Prescott NF 185 36.6 278 54.9 43 8.5 506 0.02

Santa Fe NF 384 69.7 146 26.5 22 4.0 551 0.24

Tonto NF 747 65.1 379 33.0 20 1.7 1,147 0.05

Labor income (thousands of 2012 dollars)

Income % Income Share Income %

Apache-Sitgreaves NF 21,881 53.5 4,588 11.2 14,400 35.2 40,869 0.03

Carson NF 19,165 84.9 2,541 11.3 878 3.9 22,584 1.08

Cibola NF 10,273 68.4 3,047 20.3 1,702 11.3 15,022 0.05

Cibola NG 116 3.0 3,761 97.0 - 0.0 3,876 0.27

Coconino NF 56,967 87.0 3,787 5.8 4,722 7.2 65,477 0.05

Coronado NF 12,270 53.1 10,846 46.9 - 0.0 23,116 0.08

Gila NF 2,530 23.2 7,910 72.5 467 4.3 10,907 0.66

Kaibab NF 2,834 34.1 2,159 25.9 3,330 40.0 8,323 0.06

Lincoln NF 6,972 48.4 3,261 22.6 4,180 29.0 14,413 0.06

Prescott NF 7,286 53.7 4,494 33.1 1,800 13.3 13,580 0.01

Santa Fe NF 10,776 78.0 1,785 12.9 1,248 9.0 13,808 0.14

Tonto NF 29,979 80.8 6,135 16.5 1,004 2.7 37,118 0.03
62 USDA Forest Service RMRS-GTR-383. 2018

Arizona Project provides a considerable amount of water from the Colorado River every 

year to Arizona counties.

National forests in Arizona and New Mexico are estimated to provide 55 percent 

and 46 percent, respectively, of the water supply originating in those States in an average year (table 18). These shares are similar to the 11 contiguous Western States, where 

national forest lands provide 51 percent of the water supply. The contributions from 

Arizona and New Mexico national forests are, however, relatively greater than the contributions of the remaining 48 States, where 18 percent of water supply originates from 

national forest lands (Brown and Froemke 2009).

Table 17—Average annual total (direct, indirect, and induced) employment and labor income associated with climatesensitive recreation on national forests (NFs) and grasslands (NGs) in the Forest Service Southwestern Region, by forest 

analysis area and activity.

Warmweather 

activities

Downhill 

skiing

Snow-based 

winter 

activities (not 

incl. downhill 

skiing)

Wildlife 

activities

Gathering 

forest 

products

Water-based 

activities (not 

incl. fishing)

Employment (full- and part-time jobs)

Apache-Sitgreaves NF 308 0 0 237 4 13

Carson NF 131 532 40 54 10 4

Cibola NF 321 7 2 0 8 0

Cibola NG 4 0 0 0 0 0

Coconino NF 1,191 191 16 119 9 27

Coronado NF 337 0 0 43 2 4

Gila NF 54 0 0 54 0 0

Kaibab NF 67 0 0 28 1 1

Lincoln NF 195 33 0 20 3 0

Prescott NF 152 0 0 27 1 5

Santa Fe NF 234 85 32 29 0 3

Tonto NF 415 0 0 220 0 112

Labor income (thousands of 2012 dollars)

Apache-Sitgreaves NF 11,795 - - 9,450 145 491

Carson NF 3,388 12,912 1,031 1,473 252 110

Cibola NF 9,772 210 47 - 243 -

Cibola NG 116 - - - - -

Coconino NF 43,761 6,695 595 4,593 331 992

Coronado NF 10,597 - - 1,461 71 141

Gila NF 1,237 - - 1,286 3 3

Kaibab NF 1,944 - - 843 23 23

Lincoln NF 5,450 846 - 603 73 -

Prescott NF 5,934 - - 1,124 27 200

Santa Fe NF 6,702 2,204 919 859 10 81

Tonto NF 16,435 - - 9,112 - 4,432
USDA Forest Service RMRS-GTR-383. 2018 63

Examining water supply estimates from individual forest units provides insight on 

human dependence within Arizona and New Mexico. For national forests in Arizona, 

15 percent of total water supply to the State originates from the Apache-Sitgreaves; in 

New Mexico 17 percent of total water supply originates from the Santa Fe (table 18). 

Concerns exist about the accuracy of water supply estimates for national forest units at 

smaller spatial scales. However, estimates were developed because they may be useful 

for large-scale planning purposes (Brown and Froemke 2009).

The U.S. Geological Survey (USGS) estimates that in 2010, water withdrawals 

from groundwater and surface water totaled 8,412 million cubic meters per year (Mm3/

yr) (297 billion cubic feet per year [ft3/yr]) in Arizona and 4,367 Mm3/yr (154 billion 

ft3/yr) in New Mexico (Maupin et al. 2014). These estimates of water withdrawals at 

the State level are useful alongside supply estimates, across all ownerships, at the State 

level from Brown et al. (2008) (these analyses use the same definition for water supply: 

Estimated precipitation minus evapotranspiration). Supply estimates across all ownerships are 4,709 Mm3/yr (166 billion ft3/yr) in Arizona and 5,486 Mm3/yr (194 billion 

ft3/yr) in New Mexico; estimated supply exceeds annual withdrawal in New Mexico. 

Supply estimates specific to national forests and grasslands (2,536 Mm3/yr or 90 billion ft3/yr in Arizona and 2,540 Mm3/year or 90 billion ft3/yr in New Mexico) suggest 

Southwestern Region lands are an important source of water for both States. 

Firewood Gathered for Personal or Residential Use

Harvesting firewood from national forests supports both heritage values and economic well-being, and national forests are a major source of firewood in the Southwest. 

Firewood provides fuel for cooking and winter heating, offers economic opportunities, 

Table 18—Estimated volume of surface water supplied by national forests and grasslands in the Forest Service Southwestern 

Region.

National forest State

Water volume (million 

cubic meters per year)

Water volume (billion 

cubic feet per year)

Share of state total 

supply (percent)

Apache-Sitgreaves AZ 693 24.5 15

Coconino AZ 465 16.4 10

Coronado AZ 469 16.6 10

Kaibab AZ 263 9.3 6

Prescott AZ 182 6.4 4

Tonto AZ 464 16.4 10

Total, Arizona forests 2,536 89.5 55

Carson NM 736 26.0 13

Cibola NM 119 4.2 2

Gila NM 486 17.2 9

Lincoln NM 276 9.7 5

Santa Fe NM 923 32.6 17

Total, New Mexico forests 2,540 89.7 46

Source: Brown and Froemke 2009.
64 USDA Forest Service RMRS-GTR-383. 2018

and contributes to traditional ceremonies. For some in the region, gathering firewood 

also strengthens ties to ancestral lands.

Firewood gathering is particularly important in northern New Mexico, as indicated 

by the large volumes cut in the Carson and Santa Fe National Forests (fig. 4.2). For centuries, Hispano communities in northern New Mexico have relied on firewood as their 

primary fuel and as a part of their cultural heritage (Raish 2000). High poverty rates in 

the region underscore the importance of affordable fuel sources, such as firewood.

In addition to high firewood dependence in northern New Mexico, these data show 

the importance of firewood gathering to tribes in Arizona. The three Arizona counties 

with high firewood dependence also contain large shares of tribal lands (table 19): 

Nearly 40 percent of Coconino County and about two-thirds of both Apache and Navajo 

Counties are owned by tribes (USGS 2012).

Communities with high dependence on wood heating may be vulnerable to 

changes in the availability of firewood. Firewood availability may be affected if climate 

change induces vegetative change through fire or disease, affects the price of alternative 

fuel sources, and alters the demand for firewood.

Cultural and Spiritual Values Associated with Southwestern Region National Forests and 

Grasslands

Numerous sites and resources on national forests and grasslands contribute to cultural and spiritual values. In addition to firewood, special forest products such as boughs 

and plants are collected for medicinal and ceremonial uses (USDA Forest Service 2008, 

2009b). The collection of these materials in the region predates the establishment of the 

national forests and is an integral part of individuals’ connection to the land. Climate 

change may influence the availability of these products and the characteristics of important sites.

Sites such as the San Francisco Peaks in the Coconino National Forest, Mount 

Graham in the Coronado National Forest, and the White Mountains in the Lincoln 

National Forest have particular spiritual importance for tribes in the region. Although 

climate change may not alter the presence or spiritual importance of these sites, it may 

affect ecological health and access to the sites. Such changes may inhibit enjoyment of 

sacred sites in the forests. In addition, paleontological resources, heritage resources, and 

research areas in the region offer opportunities for scientific discovery of national or 

global importance (USDA Forest Service 2011).

Cultural and spiritual values associated with national forest land may be difficult—and undesirable—to quantify and monetize. These are nonmarket values: goods 

and services that lack markets, and therefore, prices. The lack of prices, however, does 

not reflect a lack of value. The spiritual and cultural services provided by forests in the 

Southwest contribute to resilience, health, and quality of life for the individuals and 

communities that rely on them.

Conclusions

The analyses in this chapter demonstrated the economic contributions from national forests and grasslands in the region that are derived from climate-sensitive ecosystem 
USDA Forest Service RMRS-GTR-383. 2018 65

Table 19—Use of firewood for home heating, by county.

County

Percentage of 

households heating 

with wood County

Percentage of 

households heating 

with wood

Arizona New Mexico

Apache County 57.8 Bernalillo County 1.8

Cochise County 2.6 Catron County 48.3

Coconino County 17.4 Chaves County 1.6

Gila County 9.8 Cibola County 23.9

Graham County 4.2 Colfax County 13.9

Greenlee County 5.2 Curry County 1.7

La Paz County 1.8 De Baca County 7.8

Maricopa County 0.2 Doña Ana County 1.5

Mohave County 2.6 Eddy County 0.6

Navajo County 33.5 Grant County 13.7

Pima County 0.6 Guadalupe County 13.7

Pinal County 0.5 Harding County 19.0

Santa Cruz County 1.7 Hidalgo County 10.5

Yavapai County 4.5 Lea County 0.5

Yuma County 0.4 Lincoln County 14.2

Los Alamos County 2.0

Luna County 3.8

McKinley County 37.0

Mora County 48.8

Otero County 8.3

Quay County 5.1

Rio Arriba County 18.7

Roosevelt County 3.6

San Juan County 14.1

San Miguel County 27.6

Sandoval County 5.5

Santa Fe County 4.9

Sierra County 4.6

Socorro County 18.2

Taos County 29.2

Torrance County 21.5

Union County 5.2

Valencia County 6.8

Source: U.S. Census Bureau 2012.
66 USDA Forest Service RMRS-GTR-383. 2018

services. Forests in the region contribute to employment and income generated by 

activities in recreation, grazing, and timber sectors.

About 10,000 full- and part-time jobs and about $270 million in labor income are 

generated by climate-sensitive activities derived from national forests and grasslands, 

which represent about 0.2 percent of total employment and 0.1 percent of total labor 

income in the region. On average, climate-sensitive activities account for less than 

one-half of 1 percent of the region’s total economic activity, although in some forest 

areas (e.g., the Carson and the Gila) the contribution from climate-sensitive activities is 

between 1.0 and 1.5 percent of employment and income. In half of national forests and 

grasslands in the region recreation is the largest contributor to employment and income 

among climate-sensitive activities. Livestock grazing is the largest contributor in the 

remaining national forests and grasslands.

National forests and grasslands in the region also provide a significant portion 

of water supplied to residential and industrial users, provide firewood for home heating, and are home to numerous culturally and spiritually important sites. About half of 

surface water supply in the Southwest is derived from national forests and grasslands, 

and forests in the region are an important source of supply for households that heat their 

homes primarily with firewood.

The economic analyses in this report did not model how climate change is likely 

to alter the economic contributions of forests in the region. However, the results can be 

used to indicate the relative importance of different ecosystem services in each forest 

for supporting economic activities and well-being. The analyses demonstrated the broad 

importance of recreation opportunities and surface water supply that are derived from 

forests in the region. But the degree to which other ecosystem services are important 

varies across the region. For example, livestock grazing is relatively important for several forests, and certain counties in the region that have a large national forest land area 

have many households that rely on firewood for home heating.
USDA Forest Service RMRS-GTR-383. 2018 67

Chapter 5: An Indicator Analysis of Adaptive 

Capacity at the County Level

In a household production model, adaptive capacity describes the degree to which 

households can alter the allocation of inputs to mitigate a decrease in well-being in 

response to an exogenous decrease in the quantity or quality of ecosystem services. 

The ability to make such changes is probably not uniform across households in the 

Southwest. A uniform reduction in the provision of ecosystem goods and services may 

affect many Southwestern households, but households with greater flexibility to make 

production changes may face smaller decreases in well-being.

The ability of households to adapt to ecological changes may be derived from 

several sources. Household socioeconomic conditions may play a role to the extent 

that labor market outcomes, wealth, education, and other observable characteristics are 

associated with the ability to adjust household activities in response to changes in the 

supply of ecosystem services. Community-level characteristics, such as social capital, 

community facilities, and strong governance and institutions, may also help households 

adapt. Adaptive capacity may be a function of both internal and external characteristics 

and factors (Murphy et al. 2015).

The method for assessing adaptive capacity applied here is to summarize household indicators of socioeconomic conditions at the county level to describe factors that 

may be related to household adaptive capacity in each county. This approach yields 

insight into the observable characteristics that may relate to adaptive capacity for the 

average household, and can indicate where in the Southwest there may be a greater 

or lesser concentration of households that would have difficulty adapting to changing 

ecological conditions. However, this approach is limited in that it does not account for 

social interactions among community members or facilities and institutions that may 

aid households in responding to change. Nor does this approach account for variation 

in socioeconomic characteristics within counties (e.g., where there is high income 

inequality). Other assessment methods, such as case studies, scenario building, and 

participatory methods, may overcome some of the limitations of an indicator method 

(Fischer et al. 2013; Murphy et al. 2015), and could be applied as complementary 

assessments.

An Indicator Approach to Assessing Adaptive Capacity

Several factors may be related to a household’s capacity to adapt to ecological changes. Household assets may be an important component of adaptive capacity 

(Heltberg et al. 2009). These could include income and wealth, and various forms of 

human capital such as knowledge, skills, and abilities. Health status, education, and traditional knowledge may also be important factors contributing to a household’s ability 

to adapt to changing ecological conditions.

Income has been identified as a primary factor governing household well-being 

and is believed to be negatively correlated with household vulnerability (Yohe and 

Tol 2002). As income rises, households are anticipated to become less vulnerable to 
68 USDA Forest Service RMRS-GTR-383. 2018

climate-related changes in the provision of forest ecosystem services. Higher household 

income is believed to reduce vulnerability for a number of reasons including:

• Overall greater well-being, indicating a lower likelihood of experiencing negative 

outcomes;

• Greater ability to absorb increases in the costs of ecosystem inputs;

• Greater ability to use substitutes for ecosystem services in the production of 

household well-being, such as increased time or equipment to offset changes in the 

supply of ecosystem services; and

• Greater ability to substitute other goods that do not use ecosystem services as an 

input (or, use less of them); for example, higher-income households may have a 

larger menu of substitute goods because they are willing and able to pay higher 

prices for substitutes.

Related to income, household wealth and poverty status may also indicate a household’s ability to adjust production and consumption to avoid reductions in well-being. 

Characteristics that are associated with labor market outcomes may be important 

determinants of vulnerability. Educational attainment is associated with earning potential and labor market status. In general, labor markets tend to support more employment 

opportunities for households with high levels of education relative to those with less 

education. Age could be associated with vulnerability, although the direction of the 

relationship is not clear. Younger workers may have greater labor market potential and 

flexibility, but lack of experience may limit immediate labor market options. Older 

workers with more experience may have greater earning potential but less labor market 

flexibility. 

Other life-cycle characteristics, such as the presence of children, may also indicate 

the degree of flexibility in responding to ecological changes or other shocks. Retirees 

and other elderly households are generally assumed to be more vulnerable, perhaps 

due to dependence on access to healthcare, limited labor market potential, and fixed 

incomes. However, fixed incomes also imply some level of certainty that can insulate 

households against negative outcomes.

Health is an important human capital factor that can affect a household’s ability to 

work and earn income and engage in other activities that involve ecosystem services. 

Similarly, coverage by health insurance may indicate a household’s ability to obtain 

health care and avoid financial problems associated with major health issues.

Several characteristics (e.g., being a member of a racial or ethnic minority) are 

often associated with poor economic outcomes or higher likelihood of experiencing 

a reduction in well-being. Commonly applied vulnerability indices (e.g., Cutter et al. 

2003) incorporate demographic characteristics to capture these associations. Yet these 

associations can often be measured directly (e.g., by measuring income). However, 

demographic characteristics can also be associated with otherwise unobserved labor 

market potential. For example, discrimination in the labor market can decrease the 

returns to education for some races, which may be difficult to account for in other ways. 

In general, demographic characteristics are not used in this study to indicate socioeconomic adaptive capacity unless those characteristics are related to human capital factors 

(e.g., age as a proxy measure for potential labor market experience).
USDA Forest Service RMRS-GTR-383. 2018 69

Because demographic characteristics and vulnerability are related to the notion of 

environmental justice, it is important to distinguish between environmental justice and 

vulnerability assessments. The goal of a vulnerability assessment is to identify households that have a high likelihood of reductions in well-being; environmental justice 

assessments are used to examine whether and how members of groups that have traditionally been socioeconomically disadvantaged may be affected by Federal management 

actions. Although members of traditionally disadvantaged demographic groups may be 

vulnerable to climate change, association with historically underserved populations does 

not necessarily imply greater vulnerability. However, there may be cases when high 

vulnerability is correlated with membership in a disadvantaged group.

Summarizing Socioeconomic Characteristics by Using Indices

Summarizing socioeconomic status to assess vulnerability draws on common index approaches from the vulnerability literature. In the simplest form, these approaches 

gather data at the appropriate administrative level (counties, in this case) that are 

thought to be related to components of vulnerability. For example, Cutter et al. (2003) 

and Cutter and Finch (2008) develop a Social Vulnerability Index (SVI) that uses a 

principal components analysis to summarize 42 socioeconomic variables into 11 factor 

scores. These scores are then summed for each county in the study area to calculate a 

single SVI score for each county. Other standardized indices that are constructed in a 

similar manner include the Climate Vulnerability Index (Pandey and Jha 2012) and the 

Livelihood Vulnerability Index (Safi et al. 2012). 

Although measures may be similar, they often differ slightly in construction and 

interpretation. The assessment of adaptive capacity explores three indices that use characteristics observable at the county level to serve as indicators of adaptive capacity: a 

mean (average) index based on values of each indicator relative to the sample average, 

an index based on values of each indicator relative to the minimum (worst-case) values, 

and an index based on an observation’s rank for each indicator.

The mean index creates a standardized value for each indicator based on the value 

of each observation relative to the sample average. This type of index has been used 

to rank counties based on the presence of climate amenities (McGranahan 1999). For 

observation i of indicator j (Xij), the standardized measure is calculated as:

Mean_Indexij = (Xij – Xj

)

 σj

,

where Xj is the sample mean of indicator j, and σj is the standard deviation of indicator j. 

The standardized value for each indicator is positive when the raw value of an indicator 

for a given observation is greater than the sample mean and negative when the raw value is less than the sample mean. The standardized value for each indicator is unbounded 

above and below the mean, meaning that it can take any positive or negative value. The 

unit of measurement for the standardized indicator is standard deviations: The number 

of standard deviations the observation is from the sample mean. These indicators can be 

compiled into a single vulnerability index by estimating the mean of each standardized 

indicator for each observation in the sample, or: 
70 USDA Forest Service RMRS-GTR-383. 2018

Mean_Index_Sumi = Ʃj Std_Indexij, J

where J is the total number of indicators that make up the set of socioeconomic characteristics of interest.

The relative-to-minimum index is a standardized scale ranging from zero to one 

that summarizes each indicator based on the value of each observation relative to the 

minimum sample value (or maximum sample value for indicators where higher values 

indicate worse socioeconomic outcomes, such as poverty rates). This is a common type 

of index for climate vulnerability studies (e.g., Pandey and Jha 2012; Safi et al. 2012). 

The standardized value for each indicator is calculated as:

Min_Indexij = 

(Xij – Xj )

 (Xj – Xj )

min

max min

Values of the minimum index for a given indicator are interpreted as a relative 

comparison to the worst-case value in the sample. Like the mean vulnerability index, a 

single vulnerability index is created by calculating the mean of each standardized indicator for a given observation in the sample. 

Adger et al. (2004) argue that indices based on standardized values can be problematic for interpretation of vulnerability. Instead, they develop a ranking-based index 

that does not rely on a standardization calculation. Following the approach of Adger et 

al. (2004), county observations of each indicator are ranked from the worst-case outcome to the best-case outcome. Each observation is then assigned to a quintile category 

(1–5) based on the ranking. The quintile index is then constructed by calculating the 

sum of the quintile scores for each indicator. 

Data

Data used to construct the indices were drawn from publicly available U.S. 

Census products summarized for each county in Arizona and New Mexico, plus counties in Utah, Colorado, Texas, and Oklahoma that were included in the forest analysis 

areas (described in Chapter 4). County-level data were used to describe representative 

households for each county, rather than observations of individual households that are 

not publicly available. Counties are convenient observational units because data for a 

wide array of detailed socioeconomic indicators are readily available at the county level. 

Smaller geographic units (e.g., Census block groups) could provide additional spatial 

detail, but Census privacy and disclosure requirements limit the availability of some 

indicators at the subcounty level.

A core set of nine indicators was identified to summarize adaptive capacity 

(table 20). These variables were categorized into indicators of income and earning 

potential (median income, poverty rate, receipt of public assistance), labor market 

outcomes (unemployment rate, labor force participation), and human capital (education 

attainment, health insurance coverage, population under age 18, and population age 65 

and over). 
USDA Forest Service RMRS-GTR-383. 2018 71

Results

The three index measures used in this analysis are fairly consistent in how they 

describe county-level socioeconomic status. According to these measures, counties that 

consistently show low adaptive capacity (lower index scores) include Apache, Santa 

Cruz, La Paz, Navajo, and Yuma in Arizona; Luna, McKinley, Cibola, and Guadalupe 

in New Mexico; and El Paso in Texas (table 21). The counties that tend to show higher 

adaptive capacity (higher index scores) are the urban counties (except for El Paso in 

Texas, and Doña Ana in New Mexico), including Maricopa and Pima in Arizona (which 

contain Phoenix and Tucson, respectively), and Sandoval, Santa Fe, and Bernalillo in 

New Mexico (which contain Rio Rancho, Santa Fe, and Albuquerque, respectively). Los 

Alamos County in New Mexico consistently had the highest index values, indicating the 

county was the least socioeconomically vulnerable to changes in the provision of forest 

ecosystem services.

Counties characterized by a large share of lands in national forests and grasslands 

do not have consistently high or low adaptive capacity (fig. 5.1). Although several counties with low adaptive capacity measures have a high share of national forest land (e.g., 

Santa Cruz County in Arizona and Rio Arriba County in New Mexico), several other 

counties with a large proportion of national forest land have more moderate to high 

Table 20—Socioeconomic indicators gathered at the county level to develop adaptive capacity indices (observations = 58 

counties).

Indicator Description Source Mean Minimum Maximum

Health uninsured

3-year average share of 

population without health 

insurance

Small Area Health 

Insurance Estimates, 

2008–2010 (U.S. Census) 0.239 0.050 0.357

No HS diploma

Share of age 25 and older 

population without a high 

school diploma

American Community 

Survey 5-year (U.S. Census) 0.180 0.014 0.302

Public assistance

Share of households receiving 

public assistance or food 

stamps/SNAPa benefits

American Community 

Survey 5-year (U.S. Census) 0.133 0.028 0.244

Labor force 

participation

Labor force participation rate: 

Share of age 16 and older 

civilian population in the labor 

force

American Community 

Survey 5-year (U.S. Census) 0.567 0.356 0.733

Unemployment

Unemployment rate: Share of 

age 16 and older civilian labor 

force not employed

American Community 

Survey 5-year (U.S. Census) 0.084 0.009 0.188

Income

Median household income 

(2011 $)

American Community 

Survey 5-year (U.S. Census) 41,764 26,152 104,914

Poverty rate

Share of families below the 

Federal poverty threshold

American Community 

Survey 5-year (U.S. Census) 14.5 1.60 28.4

Under 18

Share of population age 18 and 

under 2010 Decennial Census 0.247 0.132 0.317

Over 65

Share of population age 65 and 

older 2010 Decennial Census 0.163 0.089 0.326

a Supplemental Nutrition Assistance Program
72 USDA Forest Service RMRS-GTR-383. 2018

Table 21—Adaptive capacity index valuesa by county in the Forest Service Southwestern Regionb. For all index measures, 

higher values indicate higher average relative adaptive capacity.

County Mean index Minimum index Quintile rank index

Apache (AZ) -12.5 1.73 15

Cochise (AZ) 2.67 4.70 30

Coconino (AZ) 5.90 5.42 38

Gila (AZ) -0.712 4.07 25

Graham (AZ) -2.53 3.68 25

Greenlee (AZ) 2.28 4.59 30

La Paz (AZ) -5.45 3.15 19

Maricopa (AZ) 5.64 5.30 36

Mohave (AZ) -0.640 4.07 26

Navajo (AZ) -5.92 3.00 19

Pima (AZ) 3.90 4.97 34

Pinal (AZ) 2.30 4.63 32

Santa Cruz (AZ) -7.01 2.82 17

Yavapai (AZ) 3.12 4.84 32

Yuma (AZ) -5.03 3.19 20

Alamosa, CO 0.749 4.40 30

Conejos, CO -1.91 3.86 24

Bernalillo (NM) 5.54 5.33 36

Catron (NM) -0.832 4.16 27

Chaves (NM) -1.46 3.97 23

Cibola (NM) -6.30 3.00 20

Colfax (NM) 2.24 4.71 31

Curry (NM) 0.925 4.43 29

De Baca (NM) -2.32 3.86 22

Doña Ana (NM) -3.12 3.66 20

Eddy (NM) 3.53 4.89 35

Grant (NM) 0.877 4.41 26

Guadalupe (NM) -6.46 3.07 20

Harding (NM) 0.161 4.41 27

Hidalgo (NM) -4.57 3.37 18

Lea (NM) -0.36 4.15 27

Lincoln (NM) 2.89 4.84 32

Los Alamos (NM) 19.9 7.81 41

Luna (NM) -9.65 2.36 13

McKinley (NM) -8.76 2.58 15

Mora (NM) 3.54 5.00 33

Otero (NM) -.600 4.17 24

Quay (NM) -1.85 3.93 24

Rio Arriba (NM) -0.908 4.07 24
USDA Forest Service RMRS-GTR-383. 2018 73

adaptive capacity (e.g., Coconino and Yavapai Counties in Arizona and Lincoln County 

in New Mexico). 

An advantage of constructing the indices at the county level is that the counties 

can be aggregated at the forest level (table 22) by using the definitions of the forest 

impact analysis areas described in Chapter 4. Across all three index measures, counties 

in the economic analysis area for the Gila and the Lincoln have lower adaptive capacity scores relative to counties in other forest areas. The forest areas with the highest 

adaptive capacity scores include the Prescott, Tonto, Apache-Sitgreaves, and Coconino. 

These forest areas are dominated by the inclusion of the most populous county in the 

region, Maricopa, which has relatively high adaptive capacity rankings. 

Summary

Adaptive capacity, as measured by three indices that summarize socioeconomic 

data at the county level, appears to vary substantially across the region. Results indicate 

that in some counties households on average have access to substantial resources and 

Roosevelt (NM) -1.09 4.08 23

San Juan (NM) 1.31 4.53 30

San Miguel (NM) -1.05 4.07 24

Sandoval (NM) 7.23 5.59 40

Santa Fe (NM) 6.13 5.45 37

Sierra (NM) -2.71 3.77 27

Socorro (NM) -4.24 3.46 20

Taos (NM) -0.130 4.29 25

Torrance (NM) -3.92 3.51 19

Union (NM) 4.74 5.24 34

Valencia (NM) -1.29 3.98 24

Cimarron (OK) -1.79 3.98 24

Roger Mills (OK) 5.29 5.28 34

Dallam (TX) 1.94 4.71 31

El Paso (TX) -6.24 3.06 21

Gray (TX) 0.532 4.37 26

Hemphill (TX) 5.44 5.31 33

Kane (UT) 7.30 5.66 38

Washington (UT) 4.18 5.00 33

a Each index is reported as a summation, meaning that the nine standardized indicators or quintile ranks are summed 

to create the index. (The overall ranking for each type of index does not depend on whether a summation or average is 

used to calculate the final index values.)

b Note: El Paso (Texas) and counties in Colorado and Utah are not geographically within the Southwestern Region, but 

are included because they appear in at least one forest economic impact area (see Chapter 4).

Table 21—Continued.

County Mean index Minimum index Quintile rank index
74 USDA Forest Service RMRS-GTR-383. 2018

Table 22—Household population-weighted values of adaptive capacity indices for national forests (NFs) 

and grasslands (NGs) in the Forest Service Southwestern Region, by forest economic impact area. For all 

index measures, higher values indicate higher average relative adaptive capacity of counties included in 

each forest analysis area.

Forest unit Mean index Minimum index Quintile rank index

Apache-Sitgreaves NF 5.01 5.17 35.2

Carson NF -0.46 4.19 25.3

Cibola NF 4.48 5.12 34.5

Cibola NG 2.02 4.68 29.1

Coconino NF 4.97 5.17 35.1

Coronado NF 3.20 4.83 32.8

Gila NF -6.86 2.87 19.8

Kaibab NF 1.69 4.54 30.0

Lincoln NF -4.62 3.37 22.5

Prescott NF 5.49 5.27 35.8

Santa Fe NF 3.49 4.92 33.1

Tonto NF 5.31 5.23 35.6

Note: Some counties appear in multiple forest economic impact areas.

Figure 5.1—Summary index of adaptive capacity (quintile rank index) in the Forest Service Southwestern Region, by 

county. Red and orange counties (lower ranks) indicate lower adaptive capacity as measured by socioeconomic 

variables. Green counties (higher ranks) indicate greater adaptive capacity as measured by socioeconomic variables.
USDA Forest Service RMRS-GTR-383. 2018 75

assets and can draw on high levels of human capital to adapt to ecological changes to 

forests and grasslands. In other counties, low measures suggest that adaptive capacity is 

more limited for the average household. 

The indicator approach used here provides a broad snapshot of socioeconomic 

conditions across the region. But it does not incorporate other information that may be 

important for assessing adaptive capacity, such as information about social capital or 

social processes within communities (Fischer et al. 2013). Further, some within-county 

variation in socioeconomic conditions may be masked when data are summarized at the 

county level.

The analysis of adaptive capacity can be used in conjunction with the analyses 

in the previous chapters to assess whether socioeconomic conditions may exacerbate 

or mitigate exposure and sensitivity to ecological changes. The analysis in Chapter 3 

indicates that the Coconino National Forest contains two ranger districts (Mogollon Rim 

and Peaks) where at least 50 percent of area is considered to be at high hazard for two or 

three climate-related stressors (Chapter 3, fig. 3.7). Among national forests in the region, 

the Coconino also contributes the most to employment and income (Chapter 4, table 

16). These two results suggest that the Coconino has relatively high exposure to climaterelated changes to the provision of ecosystem services, and nearby communities have 

high sensitivity to changes in the supply of ecosystem services. However, the Coconino 

National Forest analysis area contains counties ranked among the highest in adaptive 

capacity. In this case, high exposure and sensitivity may be mitigated by socioeconomic 

conditions that are consistent with the ability to avoid negative consequences associated 

with ecological changes.
76 USDA Forest Service RMRS-GTR-383. 2018

Chapter 6: Incorporating Socioeconomic 

Vulnerability Assessment Information Into Forest 

Planning and Adaptation Activities

The data and analyses in the preceding chapters were designed to provide public 

land managers in the Southwest with information to better understand the relationships among ecosystem services, socioeconomic well-being, and climate change. This 

information can be used to plan for the effects of climate change on national forests 

and grasslands and their impacts on people and communities who rely on ecosystem 

services that are derived from these lands. Proposed actions by organizations seeking to 

adapt to climate change can be evaluated along three dimensions (Adger et al. 2005): 

(1) Effectiveness: Does the action achieve the intended result? (2) Efficiency: Does the 

action achieve the result using the fewest resources? and (3) Equity: Does the action 

promote well-being of vulnerable groups? This chapter addresses inclusive decisionmaking, decision support tools, the potential role of partnerships, and the evaluation of 

tradeoffs in managing for climate change vulnerability. 

This chapter also discusses the management implications of climate-related 

changes to forests in the Southwestern Region and the potential for the socioeconomic 

vulnerability assessment to provide information to forest and grassland managers planning for climate effects. Specific results for particular forest units are not discussed in 

detail here; summaries are provided by forest unit in the Appendix. The focus is how 

managers can support adaptation to the projected climate-related changes that affect 

socioeconomic well-being. Although specific proposals to respond to climate change in 

the region are not developed here, this chapter is meant as a guide for using the analyses 

presented in previous chapters to inform adaptation activities.

Using the Socioeconomic Vulnerability Assessment Results for Forest 

Planning and Adaptation

The primary contribution of the vulnerability assessment is to describe the various 

ways that people in the Southwest depend on national forests and grasslands and the 

potential role of climate change in altering ecosystem services. This information can be 

used in two ways. First, national forests across the region can be compared to indicate 

where climate-related changes to certain ecosystem services are expected to have the 

largest or smallest socioeconomic effects. This type of comparison can help regionwide 

planning and prioritization efforts. 

For example, the importance of snow-based recreation activities in northern New 

Mexico national forests (the Carson, Santa Fe) suggests that higher winter temperatures 

and less reliable snowpack will have a larger socioeconomic impact in those areas compared with other forests where little winter recreation occurs. Note also that counties in 

northern New Mexico, particularly Rio Arriba and Taos, tend to have lower than average 

adaptive capacity measures (see Chapter 5 and figure 5.1). Compared with, for example, 

northern Arizona counties around the Kaibab National Forest, people and communities 

that depend on winter recreation in northern New Mexico may fare worse under future 
USDA Forest Service RMRS-GTR-383. 2018 77

climate scenarios. This type of information may help frame managers’ approach and 

decisions about the relative needs for adaptive management as they set regionwide priorities related to recreation.

A second use of the vulnerability assessment is at the local level by national forests 

and, in some cases, individual ranger districts (see box 3). During development of forest 

plans, comparisons between forests may be less important than understanding the most 

important ecological and economic stressors to expect in the future. Forest managers 

can use the assessment to help identify sources of climate-related change that may affect well-being of nearby communities. For some ecosystem services, climate change 

may have relatively little impact on well-being. For other ecosystem services, even if 

projected climate-related changes are minor, changes in service provision may meaningfully affect people and communities.

For example, ranger districts in the Coronado National Forest tend to have relatively high exposure to climate-related changes to livestock grazing. Most animal unit 

months permitted in the Coronado are on grazing allotments that are at high or very high 

likelihood of vegetative change. However, the exposure of surface drinking water to 

climate change is relatively low in the forest. Although the assessment did not develop 

comparable metrics for how much people will be affected for a given change in each 

ecosystem service, the comparison between grazing and surface water for the Coronado 

suggests that climatic changes are likely to have a relatively large impact on grazing 

activities. 

BOX 3—Socioeconomic Vulnerability and Forest Planning Under the 2012 Planning Rule

Direction related to climate change can be found throughout the 2012 Planning Rule and its directives for 

management planning on National Forest System lands (USDA Forest Service 2012b). The rule stipulates that 

managers assess “system drivers, including dominant ecological processes, disturbance regimes, and stressors, 

such as natural succession, wildland fire, invasive species, and climate change; and the ability of terrestrial and 

aquatic ecosystems on the plan area to adapt to change” (USDA Forest Service 2012b: 21263).

Social and economic considerations play a key role in the new planning rule and its directives. Language in the 

rule requires consideration of forest contributions to social and economic sustainability. The directives indicate 

that these contributions include those from ecosystem services and multiple uses of national forests and 

grasslands (USDA Forest Service 2012b: 21265). Examples of ecosystem services and multiple uses are:

• Recreational settings and opportunities for recreation activities,

• The provision of fresh water for downstream uses,

• Forage for domestic livestock grazing,

• Volume of timber or biomass offered for sale,

• Opportunities for hunting and fishing, and

• Water quality and aquatic organisms sustained by watersheds in properly functioning condition.

The directives state that the drivers and stressors affecting key ecosystem services should be identified (USDA 

Forest Service 2012b: 21263). Further, the directives and planning rule require “that the plan contain other plan 

components for integrated resource management to provide for multiple use as necessary” (USDA Forest Service 

2012b: 21266).

The analysis of likelihood of vegetative change developed by Triepke et al. (2014) and applied in this study 

(Chapter 3), provides a rigorous approach to meeting the climate change-related requirements under the 

2012 Planning Rule and its directives, such as the assessment of stressors and disturbance regimes. Further, 

consideration of economic dependence and community vulnerability (chapters 4 and 5) provides a framework to 

meet requirements for the assessment of the supply and demand of ecosystem services and how they benefit 

people and communities.
78 USDA Forest Service RMRS-GTR-383. 2018

Management Approaches to Planning for Climate-Related Changes to 

Forests and Grasslands

There is no single way for public land managers to respond to climate change or 

incorporate vulnerability assessment information into the planning process. We have 

summarized prominent management approaches, with the recognition that multiple 

approaches may be needed, and no single strategy will fit across all potential situations. 

These approaches include: inclusive decisionmaking, adaptive management frameworks, partnerships and innovative funding, and evaluating tradeoffs. The analyses in 

this report that form the vulnerability assessment can provide a contextual backdrop for 

these approaches, although results from the assessment may be used differently in each 

case.

Inclusive Decisionmaking

Where the costs of an action would be borne by vulnerable populations, alternative 

actions or mitigation measures may be considered along with an evaluation of uncertainty (Millar et al. 2007). Although Executive Order 12898 (Federal actions to address 

environmental justice in minority populations and low-income populations, February 

11, 1994) already requires the evaluation of environmental justice in Federal decisions, 

vulnerability to climate change may not align with traditional metrics that focus on 

income, race, and ethnicity. Adaptive capacity and dependence on forest and grassland 

resources are not typically used as criteria in identifying populations for environmental justice purposes. Therefore, community vulnerability to climate change may not be 

adequately addressed by the tools of environmental justice. One means of addressing 

vulnerability is through the involvement of marginalized individuals and communities 

in decisionmaking.

As described in Chapter 5, socioeconomic vulnerability is linked to lower levels 

of social and human capital. Participation in public decisionmaking about land is more 

likely among individuals who understand the regulatory process, are part of networks 

that share information on opportunities to participate, have the time and resources to 

attend public meetings, and can interpret technical findings. In other words, socioeconomically vulnerable populations are less likely to influence public decisionmaking 

(Adger 2003). Therefore, in addressing vulnerability to climate change, managers may 

need to adopt new approaches to public involvement. Two of these approaches are discussed next.

Participatory action research (PAR) seeks to involve individuals and communities 

in the decisions that affect their well-being. PAR can identify local information not 

included in the assessment results, develop likely future scenarios, and provide input to 

managers (Sheppard et al. 2011). Treating climate change adaptation as an exclusively 

expert-driven process may hamper the effectiveness of local action (Shaw et al. 2009). 

These authors emphasize that the co-production of knowledge can drive ownership of 

both problems and solutions. The use of PAR, therefore, may encourage community participation in decisionmaking and implementation. The downscaled scenarios provided 

in this report can serve as a starting point for community involvement in developing 

knowledge and identifying desired future conditions. 
USDA Forest Service RMRS-GTR-383. 2018 79

The community visioning process promotes inclusive decisionmaking about public 

resources to align management with community well-being. This process encourages 

communities to identify desired outcomes and plan achievable ways to attain them 

(Green et al. 2010; Okubo 2000). The State of Oregon uses community visioning widely, with four basic components: (1) A community profile, (2) a trend statement, (3) a 

vision statement, and (4) an action plan (Green et al. 2010). This model is used increasingly to address climate change adaptation (Sheppard et al. 2011). In areas vulnerable 

to climate change, community visioning can be a useful tool to educate individuals on 

the science and implications of climate change as well as to involve them in designing 

and supporting adaptive strategies. The process may produce an additional benefit of 

expanding social capital, and, therefore, adaptive capacity (Okubo 2000). Community 

visioning may help to prioritize adaptation actions to secure critical ecosystem services. 

Through the process, communities produce measures of success, which may be incorporated into the evaluation of tradeoffs. 

Past efforts to encourage local participation in climate change adaptation and 

mitigation have been hampered by the lack of information specific to local communities 

(Sheppard et al. 2011). This report provides county-level findings on both ecological 

and socioeconomic vulnerability to climate change, which makes the risks—and opportunities—more salient to individuals and communities. Findings in this report may serve 

as a starting point for the PAR and community visioning processes. 

Adaptive Management Frameworks

Adaptive management approaches involve a cycle of planning, acting, monitoring, 

and evaluating (Stankey et al. 2005). Adaptive management recognizes that both causes 

and effects of social and environmental change are uncertain, especially in how changes 

are manifested locally (Millar et al. 2007). The two processes discussed here, scenario 

planning and structured decisionmaking, help to incorporate and address uncertainty, 

particularly as it relates to vulnerability. For instance, monitoring and validation, as part 

of adaptive management, are key elements of scenario planning (Weeks et al. 2011). 

Scenario planning is a management tool that is particularly useful in environments 

of high uncertainty and low control. This type of planning is used to develop and test 

management actions under multiple potential futures. The National Park Service has 

used collaborative scenario planning to address climate change uncertainty (Weeks et al. 

2011). The development of multiple future scenarios allows for variation in ecological 

and socioeconomic conditions. 

Scenario planning can help managers incorporate uncertainty related to social, political, and economic variables. Similar to community visioning, scenario planning can 

improve community understanding of the complexity of climate change (Sheppard et 

al. 2011). Indeed, scenario planning can be used in tandem with a community visioning 

process. Information in Chapter 3 provides a starting point for scenario planning related 

to the condition of future ecosystem services; chapters 4 and 5 provide a starting point 

for scenario planning related to socioeconomic conditions. 

The implications of the future scenarios can guide adaptation actions. The prioritization of actions may depend on the perceived likelihood of each scenario, the 
80 USDA Forest Service RMRS-GTR-383. 2018

implications for vulnerable populations, and the presence of actions that produce positive outcomes under multiple scenarios.

Structured decisionmaking (SDM) combines scientific knowledge and social 

values to generate management actions (Wilson and Arvai 2011). An effective process 

allows diverse stakeholders to (1) understand the problem, (2) share their values and 

concerns, and (3) evaluate the tradeoffs of various actions (Wilson and Arvai 2011). 

Before developing and evaluating management strategies, an SDM approach to climate 

change requires the assessment of system vulnerabilities (Ohlson et al. 2005). 

Information in chapters 3, 4, and 5 could be used as key inputs into an SDM 

process in the Southwest. Following the development of potential actions, SDM makes 

the inevitability of tradeoffs explicit, which can help in conversations among managers, 

scientists, and the public. For example, chapters 3 through 5 provide information on 

potential ecological changes, dependence on ecosystem services, and socioeconomic 

conditions. This information fosters an effective SDM process by helping stakeholders 

understand the problem and giving them context to share their values and concerns 

about the potential management actions. 

Further, the collaborative evaluation of tradeoffs can lessen the desire of some 

participants to consider only their particular concern (Wilson and Arvai 2011). The use 

of a tradeoff matrix allows participants to see how a change in their objectives (e.g., 

minimizing cost) affects another objective (e.g., ecosystem health). The evaluation of 

tradeoffs is discussed in greater detail at the end of this section. 

Partnerships and Innovative Funding

The U.S. Department of Agriculture and the Forest Service have promoted an “alllands” approach to forest management (USDA 2009; USDA Forest Service 2010). This 

approach seeks collaboration across jurisdictions to improve ecosystem health across 

the landscape. Climate change and its effects on ecosystem services are not bound by 

political and administrative designations. Land use changes that occur on private and 

other public lands will affect national forests and grasslands, and vice versa. Vulnerable 

populations are likely to be affected by decisions that occur across jurisdictions. The alllands approach, therefore, is particularly relevant when considering responses to climate 

change vulnerability. 

Forest Service managers cannot dictate actions on private or non-Forest Service 

public lands. Similarly, Forest Service managers will not undertake inappropriate actions for the sake of consistency with other management actions on the landscape. The 

all-lands approach places a premium on partnerships and the potential for public-private 

collaboration to improve ecosystem resilience and the provision of critical ecosystem 

services. 

Partnerships with the private sector, State and local agencies, and not-for-profit 

and community-based groups may help to overcome resource constraints. For example, 

in 2010 the Rocky Mountain Region of the Forest Service and Denver Water worked as 

partners to provide $33 million of restoration treatment in high-risk and high-value watersheds in the Denver metropolitan area (Stanton and Zwick 2010). In 2012, voters in 

the City of Flagstaff (Arizona) approved a $10 million bond measure to fund restoration 

activities in the Coconino National Forest in the city’s two watersheds to reduce the risk 
USDA Forest Service RMRS-GTR-383. 2018 81

of catastrophic wildfire and subsequent flooding (City of Flagstaff 2012). Elsewhere in 

the Southwest, the Santa Fe Municipal Watershed Restoration Project is funding forest 

restoration in the Santa Fe National Forest to protect water quality and reduce the risk of 

forest fire in the watershed above the City of Santa Fe’s intake (City of Santa Fe 2013). 

In all of these cases, the beneficiaries of ecosystem services, such as municipal water 

users, pay for activities to improve or maintain the delivery of these services. With declining budgets and increasing wildfire costs, external funding through partnerships can 

allow for proactive forest management to mitigate and adapt to climate change. 

One type of partnership used by national forests—stewardship contracting—

may be an opportunity to manage forests in response to climate change. One type of 

stewardship contract permits the removal of timber and biomass in exchange for forest 

restoration services (USDA Forest Service 2009a). The 2014 Farm Bill permanently 

authorized stewardship contracting; other types of stewardship contracts that do not 

involve the exchange of goods for services are described in Moseley et al. (2015). The 

authority for stewardship provides another mechanism for completing forest restoration 

activities despite budget limitations. Whereas traditional commercial harvesting of timber on lands managed by the Forest Service requires that revenue in excess of costs be 

returned to the U.S. Treasury, stewardship contracting keeps the revenue in the forest to 

support restoration activities that may otherwise be too costly to implement. As a result, 

stewardship contracts may promote economic, ecological, sociological, recreational, and 

administrative benefits (Hausbeck 2007). 

Forest partnerships are not unidirectional. In addition to working with State and 

local agencies to fund restoration activities on Forest Service-managed lands, the 

Forest Service may support State and private forest owners through the Cooperative 

Forestry program. One aim of this program is to prepare forest owners and managers to 

participate in emergent ecosystem service markets, such as through the sale of offsets 

in carbon markets. By the end of fiscal year 2012, 29 States had agreements with the 

Forest Service to help private forest landowners market ecosystem services (USDA 

Forest Service 2013a). 

Enabling private landowners to participate in ecosystem service markets creates 

incentives to maintain forest land, which can support both climate change mitigation 

(i.e., carbon sequestration) and adaptation (e.g., protection of highly valued resources, 

such as water). Most of the approximately 750 million acres of U.S. forest land is 

privately owned (USDA Forest Service 2012a). Fragmentation and intensive land use 

are more likely on private forest land (USDA Forest Service 2012a). Rapid population 

growth in the Southwest will increase the importance of urban forests, which provide 

ecosystem services that promote health and well-being (USDA Forest Service 2012a). 

As a result, creating incentives for preservation of private forest land may be particularly important for adaptation to climate change. 

Evaluating Tradeoffs

In evaluating the tradeoffs of management actions in response to climate change, 

several considerations may affect decisionmaking. First, consequences of climate 

change may occur at spatial or temporal scales not addressed in most plans or project 

analyses. Second, management actions with co-benefits (i.e., benefits other than climate 
82 USDA Forest Service RMRS-GTR-383. 2018

change mitigation or adaptation) may be desirable. Finally, distributional implications 

may be explicitly accounted for in the assessment of tradeoffs. 

Some management actions may produce negative consequences, necessitating 

the consideration of tradeoffs. Sometimes, the effects of management actions appear 

immediately in the study area and are relatively easy to identify and mitigate. However, 

some management actions may produce spillover effects that occur outside of the spatial 

or temporal scale of the project analysis. These effects may be more difficult to identify 

and mitigate, particularly if the effects occur outside of lands managed by the Forest 

Service. This report provides analyses at spatial scales extending beyond lands managed 

by the Forest Service, which may be helpful in identifying potential consequences of 

climate change for vulnerable communities.

The temporal scale of climate change exceeds the typical temporal scale of land 

management plans and project-level analyses. Whereas a forest plan might project influence over the next 20 years, the timescale for changes in climate may be considerably 

longer. Under these circumstances, the application of standard discount rates (e.g., 

4 percent) may discourage investment in adaptation actions because current costs are 

weighed more heavily than future benefits (Goulder and Williams 2012). Furthermore, 

climate change is influenced by the behavior of people around the world. Actions taken 

by individuals and land managers in the Southwest may have consequences in other 

regions or countries, and vice versa. Resource specialists may adopt longer temporal 

and broader spatial scales in their analyses, but these approaches are likely to generate 

greater uncertainty as socioeconomic, political, and ecological changes become more 

difficult to estimate.

Given the risk and uncertainty in applying adaptation strategies, managers may 

prioritize “no-regrets” actions in evaluations of tradeoffs. No-regrets actions are those 

where benefits exceed costs, regardless of the extent of climate change (City of London 

2010). Such actions may deliver co-benefits; for instance, they may promote adaptation to climate change and improve ecosystem health, regardless of climate outcomes. 

Many forest restoration and fuels reduction projects could be categorized as no-regrets 

activities. Although such activities are not guaranteed to produce the highest benefit-cost 

ratio, they are expected to improve outcomes under all potential climate scenarios. This 

report provides useful information for identifying and carrying out no-regrets actions 

with consideration of the equity effects to vulnerable communities.

Socioeconomic vulnerability is not a familiar metric in many conventional tradeoff 

studies, where efficiency is the main objective. Evaluations may include multiple criteria, such as cost, time, and number of acres protected or restored. However, equity may 

also serve as an evaluation criterion. Distributional considerations may be explicitly 

incorporated into a tradeoff matrix by assigning higher weight to effects to vulnerable 

communities than effects to more resilient communities. Weights can reflect a concern 

with burdening communities that are least able to adapt to environmental and economic 

changes (OECD 2006). For Southwestern Region planners and managers who are 

interested in incorporating distributional considerations into decisionmaking, this report 

identifies the counties with the lowest adaptive capacity and the types of decisions that 

may disproportionately affect those communities. This information can be used as an 

input in determining whether and how distributional weights should be applied in the 

evaluation of tradeoffs.
USDA Forest Service RMRS-GTR-383. 2018 83

Caveats and Limitations to the Assessment

There are several limitations to the assessment, both in terms of the scope of information provided and how it can be used by managers. By necessity the analyses did not 

address all ecosystem services provided by forests and grasslands in the region. Because 

of a lack of available data or clear conceptual links between climatic changes and the 

provision of certain ecosystem services, the assessment could not provide information 

about all socioeconomic effects of climate-related changes to these ecosystems.

The assessment also is not well suited for developing benefit-cost analyses for 

specific projects, or for predicting the value of proposed adaptation strategies. That is, 

from the analyses we cannot say that climate change will affect well-being derived from 

an ecosystem service by a certain amount, or whether an investment to mitigate negative 

effects of climate change will improve well-being by a certain amount.

Although the assessment is useful for identifying places and ecosystem services 

that may be affected by climate change, not enough information is available to determine whether management interventions will successfully mitigate changes or avoid 

negative consequences. The assessment may be used to flag high-priority socioeconomic impacts of climate change, but more work is required to determine the most effective 

and desirable management approaches.

Finally, what is known or projected about future climate scenarios, how ecosystems will be affected by climatic changes, and the effect on the provision of ecosystem 

services involve considerable uncertainty. Some dimensions uncertainty can at least be 

evaluated by examining results under multiple future scenarios (e.g., different emissions 

scenarios that drive global circulation models) or different assumptions (e.g., which 

socioeconomic characteristics are closely related to adaptive capacity). But we know 

very little about other sources of uncertainty, such as how some ecosystems will respond 

to climate characteristics outside of previously observed ranges, and how people will 

respond to ecological changes that have not previously been observed. 
84 USDA Forest Service RMRS-GTR-383. 2018

References

Adger, W. Neil. 2003. Social capital, collective action, and adaptation to climate change. 

Economic Geography. 79(4): 387–404.

Adger, W. Neil. 2006. Vulnerability. Global Environmental Change. 16(3): 268–281.

Adger, W. Neil; Arnell, N.W.; Tompkins, E.L. 2005. Successful adaptation to climate change 

across scales. Global Environmental Change. 15(2): 77–86.

Adger, W. Neil; Brooks, Nick; Bentham, Graham; [et al.]. 2004. New indicators of vulnerability 

and adaptive capacity. Tech. Rep. 7. Norwich, UK: Tyndall Centre for Climate Change 

Research.

Alig, Ralph J.; Stone, Edward E.; White, Eric M. 2011. Land value changes and carbon 

sequestration as an ecosystem service in a climate-changed environment. In: Alig, Ralph 

J., ed. Effects of climate change on natural resources and communities: A compendium 

of briefing papers. Gen. Tech. Rep. PNW-GTR-837. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 

Allen, Craig D. 2007. Interactions across spatial scales among forest dieback, fire, and erosion in 

northern New Mexico landscapes. Ecosystems. 10(5): 797–808.

Allen, C.D.; Macalady, A.K.; Chenchouni, H.; [et al.]. 2010. A global overview of drought and 

heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology 

and Management. 259(4): 660–684.

Atencio, Ernest. 2004. Of land and culture: Environmental justice and public lands ranching 

in Northern New Mexico. Santa Fe, NM: Quivira Coalition and the Northern New Mexico 

Group of the Sierra Club. http://quiviracoalition.org/images/pdfs/723-Of_Land_and_Culture.

pdf [Accessed April 5, 2016].

Ayres, Matthew P.; Lombardero, Maria J. 2000. Assessing the consequences of global change for 

forest disturbance from herbivores and pathogens. Science of the Total Environment. 262(3): 

263–286.

Bagne, Karen E.; Finch, Deborah M. 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.

Baker, B.B.; Hanson, J.D.; Bourdon, R.M.; Eckert, J.B. 1993. The potential effects of climate 

change on ecosystem processes and cattle production on U.S. rangelands. Climatic Change. 

25(2): 97–117.

Barbier, Edward B. 2000. Valuing the environment as input: Review of applications to 

mangrove-fishery linkages. Ecological Economics. 35(1): 47–61.

Barnett, T.P.; Pierce, D.W.; Hidalgo, H.G.; [et al.]. 2008. Human-induced changes in the 

hydrology of the western United States. Science. 319(5866): 1080–1083.

Blumenthal, D.M.; Kray, J.A.; Ortmans, W.; [et al.]. 2016. Cheatgrass is favored by warming but 

not CO2 enrichment in a semi-arid grassland. Global Change Biology. 22(9): 3026–3038.

Bockstael, Nancy E.; McConnell, Kenneth E. 1981. Theory and estimation of the household 

production function for wildlife recreation. Journal of Environmental Economics and 

Management. 8(3): 199–214.

Bowker, J.M.; Askew, Ashley E.; Poudyal, Neelam; [et al.]. 2013. Climate change and outdoor 

recreation participation in the Southern United States. In: Vose, J.M.; Klepzig, K.D., eds. 

Climate change adaptation and mitigation management options: A guide for natural resource 

managers in the southern forest ecosystems. Boca Raton, FL: CRC Press. 32 p.

Boyd, J.; Banzhaf, S. 2007. What are ecosystem services? The need for standardized 

environmental accounting units. Ecological Economics. 63(2-3): 616–626.

Bronstert, A. 2003. Floods and climate change: Interactions and impacts. Risk Analysis. 23(3): 

545–557.
USDA Forest Service RMRS-GTR-383. 2018 85

Brown, A.E.; Zhang, L.; McMahon, T.A.; [et al.]. 2005. A review of paired catchment studies 

for determining changes in water yield resulting from alterations in vegetation. Journal of 

Hydrology. 310(1): 28–61.

Brown, Joel; MacLeod, Neil. 2011. A site-based approach to delivering rangeland ecosystem 

services. The Rangeland Journal. 33(2): 99–108.

Brown, Thomas C.; Bergstrom, John C.; Loomis, John B. 2007. Defining, valuing, and providing 

ecosystem goods and services. Natural Resources Journal. 47(2): 329–376.

Brown, Thomas C.; Froemke, Pamela. 2009. Estimated mean annual contribution to water supply 

from units of the National Forest System (NFS) of the U.S. Forest Service. Unpublished 

report on file with: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station, Fort Collins, CO. http://www.fs.fed.us/rm/value/docs/water_supply_national_forests.

pdf [Accessed April 5, 2016].

Brown, Thomas C.; Hobbins, Michael T.; Ramirez, Jorge A. 2008. Spatial distribution of 

water supply in the coterminous United States. Journal of the American Water Resources 

Association. 44(6): 1474–1487.

Chambers, Jeanne C.; Pellant, Mike. 2008. Climate change impacts on northwestern and 

intermountain United States rangelands. Rangelands. 30(3): 29–33.

Chornesky, Elizabeth A.; Bartuska, Ann M.; [et al.]. 2005. Science priorities for reducing the 

threat of invasive species to sustainable forestry. BioScience. 55(4): 335–348.

City of Flagstaff. 2012. Flagstaff watershed protection project: Executive summary and 

implementation plan. Flagstaff, AZ: City of Flagstaff; U.S. Department of Agriculture, 

Coconino National Forest, Flagstaff Ranger District. http://www.flagstaff.az.gov/

DocumentCenter/View/41236 [Accessed April 5, 2016].

City of London. 2010. Rising to the challenge: The City of London climate change adaptation 

strategy. London, UK: City of London Corporation. https://www.cityoflondon.gov.uk/services/

environment-and-planning/sustainability/Documents/climate-change-adaptation-strategy2010-update.pdf [Accessed April 5, 2016].

City of Santa Fe. 2013. Santa Fe municipal watershed plan, 2010–2029. Santa Fe, NM: City of 

Santa Fe, Water Division. www.santafenm.gov/document_center/document/780 [Accessed 

April 5, 2016].

Cole, Kenneth L. 2010. Vegetation response to early Holocene warming as an analog for current 

and future changes. Conservation Biology. 24(1): 29–37.

Comer, P.J.; Young, B.; Schulz, K.; [et al.]. 2012. Climate change vulnerability and adaptation 

strategies for natural communities: Piloting methods in the Mojave and Sonoran deserts. 

Report to the U.S. Fish and Wildlife Service. Arlington, VA: NatureServe. https://www.fws.

gov/refuges/whm/pdfs/NatureServe_HCCVI_Report.pdf [Accessed August 25, 2016].

Cutter, Susan L.; Boruff, Bryan J.; Shirley, W. Lynn. 2003. Social vulnerability to environmental 

hazards. Social Science Quarterly. 84(2): 242–261.

Cutter, Susan L.; Finch, Christina. 2008. Temporal and spatial changes in social vulnerability to 

natural hazards. Proceedings of the National Academy of Sciences. 105(7): 2301–2306.

Daily, Gretchen C. 1997. Introduction: What are ecosystem services? In: Daily, Gretchen, ed. 

Nature’s services: Societal dependence on natural ecosystems. Washington, DC: Island Press.

Dale, Virginia H.; Joyce, Linda A.; McNulty, Steve; [et al.]. 2000. The interplay between climate 

change, forests, and disturbances. Science of the Total Environment. 262(3): 201–204.

Dale, Virginia H.; Joyce, Linda A.; McNulty, Steve; [et al.]. 2001. Climate change and forest 

disturbances. BioScience. 51(9): 723–734.

DiTomaso, J.M. 2000. Invasive weeds in rangelands: Species, impacts, and management. Weed 

Science. 48(2): 255–265.

Duffield, J.W.; Neher, C.J.; Patterson, D.A.; [et al.]. 2013. Effects of wildfire on national 

park visitation and the regional economy: A natural experiment in the Northern Rockies. 

International Journal of Wildland Fire. 22(8): 1155–1166.
86 USDA Forest Service RMRS-GTR-383. 2018

Emerich, Christopher T.; Cutter, Susan L. 2011. Social vulnerability to climate-sensitive hazards 

in the southern United States. Weather, Climate, and Society. 3(3): 193–208.

Englin, Jeffrey; Boxall, Peter C.; Chakraborty, [et al.]. 1996. Valuing the impacts of forest fires 

on backcountry forest recreation. Forest Science. 42(4): 450–455.

Englin, Jeffrey; Loomis, John; González-Cabán, Armando. 2001. The dynamic path of 

recreational values following a forest fire: A comparative analysis of States in the 

Intermountain West. Canadian Journal of Forest Research. 31(10): 1837–1844.

Englin, Jeffrey; Moeltner, Klaus. 2004. The value of snowfall to skiers and boarders. 

Environmental and Resource Economics. 29(1): 123–136.

Enquist, Carolyn; Gori, Dave. 2008. Implications of recent climate change on conservation 

priorities in New Mexico. Santa Fe, NM: The Nature Conservancy and Wildlife Conservation 

Society. http://nmconservation.org/dl/NMClimateChange_report1_527.pdf [Accessed August 

25, 2016]. 

Fire Modeling Institute. 2014. Wildfire hazard potential (WFP) for the conterminous United 

States (270-m GRID), v2014 classified. Missoula, MT: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. http://www.firelab.org/document/

classified-2014-whp-gis-data-and-maps [Accessed April 5, 2016].

Fischer, A. Paige; Paveglio, Travis; Carroll, Matthew; [et al.]. 2013. Assessing social 

vulnerability to climate change in human communities near public forests and grasslands: A 

framework for resource managers and planners. Journal of Forestry. 111(5): 357–365.

Fisher, Monica. 2004. Household welfare and forest dependence in Southern Malawi. 

Environment and Development Economics. 9(2): 135–154.

Fisher, Monica; Chaudhury, Moushumi; McCusker, Brent. 2010. Do forests help rural 

households adapt to climate variability? Evidence from Southern Malawi. World 

Development. 38(9): 1241–1250.

Flannigan, Michael D.; Stocks, Brian J.; Wotton, B.M. 2000. Climate change and forest fires. 

Science of the Total Environment. 262(3): 221–229.

Freeman, A. Myrick, III; Herriges, Joseph A.; Kling, Catherine L. 2014. The measurement of 

environmental and resource values: Theory and methods. 3rd ed. New York: RFF Press.

Friggens, M.M.; Bagne, K.E.; Finch, D.M.; [et al.]. 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. 

Gallopin, Gilberto C. 2006. Linkages between vulnerability, resilience, and adaptive capacity. 

Global Environmental Change. 16(3): 293–303.

Garfin, G.; Franco, G.; Blanco, H.; [et al.]. 2014. Chapter 20: Southwest. In: Melillo, J.M., 

Richmond, T.C., Yohe, G.W., eds. Climate change impacts in the United States: The Third 

National Climate Assessment. Washington, DC: Global Change Research Program: 462–486.

Goulder, L.H.; Williams, R.C. 2012. The choice of discount rate for climate change policy 

evaluation. Resources for the Future Discussion Paper 12-43. Washington, DC: Resources 

for the Future. http://www.rff.org/files/sharepoint/WorkImages/Download/RFF-DP-12-43.pdf

[Accessed April 5, 2016].

Green, G.; Haines, A.; Halebsky, S. 2010. Building our future: A guide to community visioning. 

Madison, WI: Cooperative Extension Publishing. http://oconto.uwex.edu/files/2010/08/g3708-

buildingourfuture-aguidetocommunityvisioning.pdf [Accessed April 5, 2016].

Gutzler, D.S. 2013. Regional climatic considerations for borderlands sustainability. Ecosphere. 

4(1): 7 p.

Gutzler, D.S.; Robbins, T.O. 2010. Climate variability and projected change in the western 

United States: Regional downscaling and drought statistics. Climate Dynamics. 37(5-6): 

835–849.

Hausbeck, K. 2007. The little engine that could: The success of the stewardship contracting 

authority. William & Mary Environmental Law and Policy Review. 32(1): 33–55.
USDA Forest Service RMRS-GTR-383. 2018 87

Havstad, Kris M.; Peters, Debra P.C.; Skaggs, Rhonda; [et al.]. 2007. Ecological services to and 

from rangelands of the United States. Ecological Economics. 64(2): 261–268.

Hellmann, J.J.; Byers, J.E.; Bierwagen, B.G.; [et al.]. 2008. Five potential consequences of 

climate change for invasive species. Conservation Biology. 22(3): 534–543.

Heltberg, Rasmus; Bennett Siegel, Paul; Lau Jorgensen, Steen. 2009. Addressing human 

vulnerability to climate change: Toward a ‘no-regrets’ approach. Global Environmental 

Change. 19(1): 89–99.

Hesseln, Hayley; Loomis, John B.; González-Cabán, Armando. 2004. The effects of fire on 

recreation demand in Montana. Western Journal of Applied Forestry. 19(1): 47–53.

Hesseln, Hayley; Loomis, John B.; González-Cabán, Armando; [et al.]. 2003. Wildfire effects 

on hiking and biking demand in New Mexico: A travel cost study. Journal of Environmental 

Management. 69(4): 359–368.

Holmes, Thomas P.; Prestemon, Jeffrey P.; Abt, Karen L. 2008. An introduction to the economics 

of forest disturbance. In: Holmes, Thomas P.; Prestemon, Jeffrey P.; Abt, Karen L., eds. The 

economics of forest disturbances. Springer Science & Business Media: 3–14.

Huggett, Robert J.; Murphy, Elizabeth A.; Holmes, Thomas P. 2008. Forest disturbance impacts 

on residential property values. In: Holmes, Thomas P.; Prestemon, Jeffrey P.; Abt, Karen L., 

eds. The economics of forest disturbances. Springer Science & Business Media: 209–228.

Huntley, Brian. 1991. How plants respond to climate change: Migration rates, individualism and 

the consequences for plant communities. Annals of Botany. 67(supplement 1): 15–22.

IMPLAN. 2013. IMpacts for PLANning (IMPLAN). Minnesota IMPLAN Group, Inc. Version 

3.1.1001.13, Copyright 2013. www.implan.com [Accessed April 5, 2016]. 

Intergovernmental Panel on Climate Change [IPCC]. 2007. Summary for policymakers. In: 

Parry, M.L.; Canziani, O.F.; Palutikof, J.P.; [et al.], eds. Climate change 2007: Impacts, 

adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge 

University Press.

Irland, L.C.; Adams, D.; Alig, R.; [et al.]. 2001. Assessing socioeconomic impacts of climate 

change on U.S. forests, wood-product markets, and forest recreation. BioScience. 51(9): 

753–764.

Jones, R.; Travers, C.; Rodgers, C.; [et al.]. 2013. Climate change impacts on freshwater 

recreational fishing in the United States. Mitigation and Adaptation strategies for Global 

Change. 18(6): 731–758.

Kardol, Paul; Todd, Donald E.; Hanson, Paul J.; [et al.]. 2011. Long-term successional forest 

dynamics: Species and community responses to climatic variability. Journal of Vegetation 

Science. 21(4): 627–642.

Kelly, P.M.; Adger, W.N. 2000. Theory and practice in assessing vulnerability to climate change 

and facilitating adaptation. Climatic Change. 47(4): 325–352. 

Krist, F.J., Jr.; Ellenwood, J.R.; Woods, M.E.; [et al.]. 2014. 2013–2027 National Insect and 

Disease Forest Risk Assessment. FHTET-14-01. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Forest Health Technology Enterprise Team. 199 p. 

Littell, J.S.; McKenzie, D.; Peterson, D.L.; [et al.]. 2009. Climate and wildfire area burned in the 

western U.S. ecoprovinces, 1916–2003. Ecological Applications. 19(4): 1003–1021.

Loomis, John; Crespi, J. 2004. Estimated effects of climate change on selected outdoor 

recreation activities in the United States. In: Mendelsohn, R.; Neumann, J., eds. The impact of 

climate change on the United States economy, Cambridge, UK: Cambridge University Press: 

289–314.

Loomis, John; González-Cabán, Armando; Englin, Jeffrey. 2001. Testing for differential effects 

of forest fires on hiking and mountain biking demand and benefits. Journal of Agricultural and 

Resource Economics. 26(2): 508–522.

Luers, A.L. 2005. The surface of vulnerability: An analytical framework for examining 

environmental change. Global Environmental Change. 15(3): 214–223.
88 USDA Forest Service RMRS-GTR-383. 2018

MacDonald, G.M. 2010. Water, climate change, and sustainability in the Southwest. PNAS. 

107(50): 21256–21262.

Masters, R.A.; Sheley, R.L. 2001. Principles and practices for managing rangeland invasive 

plants. Journal of Range Management. 54(5): 502–517.

Maupin, M.A.; Kenny, J.F.; Hutson, S.S.; [et al.]. 2014. Estimated use of water in the United 

States in 2010. Circular 1405. Reston, VA: U.S. Department of the Interior, U.S. Geological 

Survey. 56 p. http://dx.doi.org/10.3133/cir1405 [Accessed April 5, 2016].

McCabe, G.J.; Wolock, D.M. 1999. General-circulation-model simulations of future snowpack 

in the western United States. Journal of the American Water Resources Association. 35(6): 

1473–1484.

McCarthy, James J. 2001. Climate Change 2001: Impacts, adaptation, and vulnerability: 

Contribution of Working Group II to the third assessment report of the Intergovernmental 

Panel on Climate Change. Cambridge, UK: Cambridge University Press. 18 p.

McDowell, N.G.; Williams, A.P.; Xu, C.; [et al.]. 2015. Multi-scale predictions of massive 

conifer mortality due to chronic temperature rise. Nature Climate Change. 6(3): 295–300.

McGranahan, David A. 1999. Natural amenities drive rural population change. Agricultural 

Economic Report No. 781, Washington, DC: U.S. Department of Agriculture, Economic 

Research Service. 

McMichael, Anthony; Scholes, Robert; Hefny, Manal; [et al.]. 2005. Chapter 3: Linking 

ecosystem services and human well-being. In: Capistrano, D.; Samper, C.K.; Lee, M.J.; [et 

al.], eds. Ecosystems and human well-being: Multiscale assessments, Volume 4. Washington, 

DC: Island Press.

Mendelsohn, Robert; Markowski, M. 2004. The impact of climate change on outdoor recreation. 

In: Mendelsohn, R.; Neumann, J., eds. The impact of climate change on the United States 

economy. Cambridge, UK: Cambridge University Press: 267–288.

Miernyk, William H. 1965. The elements of input-output analysis. New York: Random House.

Millar, C.I.; Stephenson, N.L.; Stephens, S.L. 2007. Climate change and forests of the future: 

Managing in the face of uncertainty. Ecological Applications. 17(8): 2145–2151.

Morgan, T.A.; Keegan, C.; Brandt, J.P. 2014. Employment and labor income direct response 

coefficients for the U.S. forest products industry. Missoula, MT: Bureau of Business and 

Economic Research, University of Montana-Missoula. http://www.bber.umt.edu/pubs/forest/

workforce/DRC_report_DRAFT.pdf [Accessed April 5, 2016].

Morris, Daniel; Walls, Margaret. 2009. Climate change and outdoor recreation resources. 

Research Backgrounder. Washington, DC: Resources for the Future. http://www.rff.org/

files/sharepoint/WorkImages/Download/RFF-BCK-ORRG_ClimateChange.pdf [Accessed 

September 8, 2015].

Moseley, C.; Davis, E.J.; Bixler, R.P. 2015. Innovative contracting: A guide for collaborative 

groups and community partnership. Quick Guide. Eugene, OR: Ecosystem Workforce 

Program, Institute for a Sustainable Environment. 20 p. http://ewp.uoregon.edu/sites/ewp.

uoregon.edu/files/QG_InnovativeContracting.pdf [Accessed May 5, 2017].

Moser, W. Keith; Barnard, Edward L.; Billings, Ronald F.; [et al.]. 2009. Impacts of nonnative 

invasive species on US forests and recommendations for policy and management. Journal of 

Forestry. 107(6): 320–327.

Murphy, Daniel J.; Wyborn, Carina; Yung, Laurie; [et al.]. 2015. Key concepts and methods in 

vulnerability and adaptive capacity. Gen. Tech. Rep. RMRS-GTR-328. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 24 p.

Nelson, D.R.; Adger, W.N.; Brown, K. 2007. Adaptation to environmental change: Contributions 

of a resilience framework. Annual Review of Environment and Resources. 32(1): 395–419.

Nelson, M.D.; Vissage, J. 2007. Mapping forest inventory and analysis forest land use: 

Timberland, reserved forest land, and other forest land. In: McRoberts, R.E., Reams, G.A., 

Van Deusen, P.C., McWilliams, W.H., eds. Proceedings of the seventh annual forest inventory 

and analysis symposium; 2005 October 3–6. Gen. Tech. Rep. WO-77. Washington, DC: U.S. 

Department of Agriculture, Forest Service.
USDA Forest Service RMRS-GTR-383. 2018 89

New Mexico Department of Game and Fish. 2016a. Antelope (E-PLUS) private lands program. 

[Online licensing system]. Santa Fe, NM: New Mexico Department of Game and Fish. http://

www.wildlife.state.nm.us/hunting/maps/antelope-private-lands/ [Accessed December 27, 

2017].

New Mexico Department of Game and Fish. 2016b. Elk (E-PLUS) private lands program. 

[Online licensing system]. Santa Fe, NM: New Mexico Department of Game and Fish. http://

www.wildlife.state.nm.us/hunting/maps/elk-private-lands/ [Accessed December 27, 2017].

Ohlson, D.W.; McKinnon, G.A.; Hirsch, K.G. 2005. A structured decision-making approach to 

climate change adaptation in the forest sector. The Forestry Chronicle. 81(1): 97–103.

Okubo, D. 2000. The community visioning and strategic planning handbook. Denver, CO: 

National Civic League Press. 

Organisation for Economic Cooperation and Development (OECD). 2006. Cost-benefit analysis 

and the environment: recent developments. Paris, France: OECD Publishing. 

Overpeck, Jonathan; Garfin, Gregg; Jardine, Angela; [et al.]. 2013. Chapter 1: Summary for 

decision makers. In: Garfin, G.; Jardine, A.; Merideth, R.; [et al.], eds. Assessment of climate 

change in the Southwest United States. Washington, DC: Island Press.

Pandey, Rajiv; Jha, Shashidhar Kumar. 2012. Climate vulnerability index—Measure of climate 

change vulnerability to communities: A case of rural lower Himalaya, India. Mitigation and 

Adaptation Strategies for Global Change. 17(5): 487–506.

Pattanayak, Subhrendu; Sills, Erin O. 2001. Do tropical forests provide natural insurance? The 

microeconomics of non-timber forest product collection in the Brazilian Amazon. Land 

Economics. 77(4): 595–712.

Paveglio, Travis; Moseley, Cassandra; Carroll, Matthew S.; [et al.]. 2015. Categorizing the 

social context of the wildland urban interface: Adaptive capacity for wildfire and community 

“archetypes.” Forest Science. 61(2): 298–310.

Pierce, Jennifer L.; Meyer, Grant A.; Jull, A.J. Timothy. 2004. Fire-induced erosion and 

millennial-scale climate change in northern ponderosa pine forests. Nature. 432(7013): 87–90.

Pramova, Emilia; Locatelli, Bruno; Djoudi, Houria; [et al.]. 2012. Forests and trees for social 

adaptation to climate variability and change. Wiley Interdisciplinary Reviews: Climate 

Change. 3(6): 581–596.

Raish, Carol. 2000. Environmentalism, the Forest Service, and the Hispano communities of 

northern New Mexico. Society and Natural Resources. 13: 489–508. 

Rausch, Michael; Boxall, Peter C.; Verbyla, Arunas P. 2010. The development of fire-induced 

damage functions for forest recreation activity in Alberta, Canada. International Journal of 

Wildland Fire. 19(1): 63–74.

Reeves, M.C.; Bagne, K.E. 2016. Vulnerability of cattle production to climate change on 

U.S. rangelands. Gen. Tech. Rep. RMRS-GTR-343. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 39 p.

Reeves, M.C.; Moreno, A.L.; Bagne, K.E.; [et al.]. 2014. Estimating climate change effects 

on net primary production of rangelands in the United States. Climatic Change. 126(3-4): 

429–442.

Rehfeldt, G.E.; Crookston, N.L.; Cuauhtémoc, S.; [et al.]. 2012. North American vegetation 

model for land-use planning in a changing climate: A solution to large classification problems. 

Ecological Applications. 22: 119–141. 

Retzlaff, Michael. 2009. Delineating economic areas: Identifying areas for impact analysis in the 

USDA-Forest Service. Online presentation. http://lehd.ces.census.gov/doc/workshop/2009/

Retzlaff.pdf [Accessed April 5, 2016].

Richardson, R.B.; Loomis, John B. 2004. Adaptive recreation planning and climate change: A 

contingent visitation approach. Ecological Economics. 50(1): 83–99.

Ritten, J.P.; Frasier, W.M.; Bastian, C.T.; [et al.]. 2010. Optimal rangeland stocking decisions 

under stochastic and climate-impacted weather. American Journal of Agricultural Economics. 

92(4): 1242–-1255.
90 USDA Forest Service RMRS-GTR-383. 2018

Root, Terry L.; Price, Jeff T.; Hall, Kimberly R.; [et al.]. 2003. Fingerprints of global warming on 

wild animals and plants. Nature. 421(6918): 57–60.

Rose, A.; Cao, Y.; Oladosu, G. 2000. Simulating the economic impact of climate change in the 

Mid-Atlantic Region. Climate Research. 14: 175–183.

Rosenberg, N.J. 1993. Towards an integrated assessment of climate change: The MINK Study. 

Boston: Kluwer Academic Publishers. 173 p.

Ryan, M.G.; Vose, J.M. 2012. Chapter 2: Effects of climatic variability and change. In: Vose, 

J.M.; Peterson, D.L.; Patel-Weynand, T., eds. Effects of climatic variability and change on 

forest ecosystems: A comprehensive science synthesis for the U.S. forest sector. Gen. Tech. 

Rep. PNW-GTR-870. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station.

Safi, Ahmad Saleh; Smith, William James, Jr.; Liu, Zhnongwei. 2012. Rural Nevada and climate 

change: Vulnerability, beliefs, and risk perception. Risk Analysis. 32(6): 1041–1059.

Scott, D.; Jones, B.; Konopek, J. 2007. Implications of climate and environmental change for 

nature-based tourism in the Canadian Rocky Mountains: A case study of Waterton Lakes 

National Park. Tourism Management. 28(2): 570–579.

Seager, R.; Ting, M.; Held, I.; [et al.]. 2007. Model projections of an imminent transition to a 

more arid climate in southwestern North America. Science. 316(5828): 1181–1184.

Shaw, A.; Sheppard, S.; Burch, S.; [et al.]. 2009. Making local futures tangible: Synthesizing, 

downscaling, and visualizing climate change scenarios for participatory capacity building. 

Global Environmental Change. 19: 447–463. 

Shaw, D.; Loomis, John. 2008. Frameworks for analyzing the economic effects of climate change 

on outdoor recreation and selected estimates. Climate Research. 36(3): 259–269.

Sheppard, S.; Shaw, A.; Flanders, D.; [et al.]. 2011. Future visioning of local climate change: 

A framework for community engagement and planning with scenarios and visualisation. 

Futures. 43: 400–412.

Smit, Barry; Wandel, Johanna. 2006. Adaptation, adaptive capacity and vulnerability. Global 

Environmental Change. 16(3): 282–292.

Smith, S.D.; Huxman, T.E.; Zitzer, S.F.; [et al.]. 2000. Elevated CO2 increases productivity and 

invasive species success in an arid ecosystem. Nature. 408(6808): 79–82.

Smith, W. Brad; Miles, Patrick D.; Perry, Charles H.; [et al.]. 2009. Forest resources of 

the United States, 2007. Gen Tech. Rep. WO-78. Washington, DC: U.S. Department of 

Agriculture, Forest Service. 336 p.

Sohngen, Brent; Mendelsohn, Robert. 1998. Valuing the impact of large-scale ecological change 

in a market: The effect of climate change on U.S. timber. The American Economic Review. 

88(4): 686–710.

Stankey, G.H.; Clark, R.N.; Bormann, B.T. 2005. Adaptive management of natural resources: 

theory, concepts, and management institutions. Gen. Tech. Rep. PNW-GTR-654. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

Stanton, T.; Zwick, S. 2010. Why Denver spends water fees on trees. Ecosystem Marketplace. 

http://www.ecosystemmarketplace.com/articles/why-denver-spends-water-fees-on-trees/

[Accessed April 5, 2016].

Starbuck, C.M.; Berrens, Robert P.; McKee, Michael. 2006. Simulating changes in forest 

recreation demand and associated economic impacts due to fire and fuels management 

activities. Forest Policy Economics. 8(1): 52–66.

Steen, H.K. 2004. The U.S. Forest Service: A history. Seattle, WA: University of Washington 

Press.

Stephenson, Nathan L. 1998. Actual evapotranspiration and deficit: Biologically meaningful 

correlates of vegetation distribution across spatial scales. Journal of Biogeography. 25(5): 

855–870. 
USDA Forest Service RMRS-GTR-383. 2018 91

Stratus Consulting. 2009. Climate change in Park City: An assessment of climate, 

snowpack, and economic impacts. Report prepared for The Park City Foundation. 

Washington, DC: Stratus Consulting, Inc. http://www.powdr.com/wp-content/uploads/

ParkCityClimateChangeAssessment.pdf [Accessed April 5, 2016].

Thompson, M.P.; Calkin, D.E.; Finney, M.A.; [et al.]. 2011. Integrated national-scale assessment 

of wildfire risk to human and ecological values. Stochastic Environmental Research and Risk 

Assessment. 25(6): 761–780.

Thornton, P.K.; Jones, P.G.; Owiyo, T.; [et al.]. 2008. Climate change and poverty in Africa: 

Mapping hotspots of vulnerability. African Journal of Agricultural and Resource Economics. 

2(1): 24–44.

Tkacz, Borys; Moody, Ben; Castillo, Jaime V.; [et al.]. 2008. Forest health conditions in North 

America. Environmental Pollution. 155(3): 409–425.

Torell, L. Allen; Rimbey, Neil R.; Ramírez, Octavio A.; [et al.]. 2005. Income earning potential 

versus consumptive amenities in determining ranchland values. Journal of Agricultural and 

Resource Economics. 39(3): 537–560.

Triepke, F.J.; Wahlberg, M.M.; Muldavin, E.H.; [et al.]. 2014. Assessing climate change 

vulnerability for ecosystems of the southwestern U.S. Unpublished report on file with: U.S. 

Department of Agriculture, Forest Service, Southwestern Region, Albuquerque, NM.

U.S. Census Bureau. 2012. American community survey, 2007 Albuquerque, NM 2011 5-year 

Estimates. U.S. Department of Commerce, Census Bureau, American Fact Finder. http://

factfinder2.census.gov [Accessed November 22, 2013].

U.S. Department of Agriculture [USDA]. 2009. Agriculture Secretary Vilsack presents national 

vision for America’s forests. Release No. 0382.09. Washington, DC: U.S. Department of 

Agriculture, Office of Communications. http://www.usda.gov/wps/portal/usda/usdamediafb?c

ontentid=2009/08/0382.xml&printable=true&contentidonly=true [Accessed April 5, 2016].

U.S. Department of Agriculture [USDA]. 2013. National Agricultural Statistical Service. 

Cattle and sheep inventory survey results for 2013. Quickstats 2.0. Washington, DC: U.S. 

Department of Agriculture. http://quickstats.nass.usda.gov/ [Accessed April 5, 2016].

USDA Forest Service. 2008. Coconino National Forest: Economic and social sustainability 

assessment. Flagstaff, AZ: U.S. Department of Agriculture, Forest Service, Coconino National 

Forest. http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb5335119.pdf [Accessed 

April 5, 2016].

USDA Forest Service. 2009a. Apache-Sitgreaves National Forest: Economic and social 

sustainability assessment. Albuquerque, NM: U.S. Department of Agriculture, Forest Service, 

Southwestern Region. http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev7_019978.

pdf [Accessed April 5, 2016].

USDA Forest Service. 2009b. Stewardship contracting: Basic stewardship contracting concepts. 

Washington, DC: U.S. Department of Agriculture, Forest Service. http://www.fs.fed.us/

restoration/documents/stewardship/stewardship_brochure.pdf [Accessed April 5, 2016].

USDA Forest Service. 2010. An all-lands approach to conservation. Speech by Tom Tidwell, 

January 13, 2010, Little Rock, AR. http://www.fs.fed.us/speeches/all-lands-approachconservation [Accessed April 5, 2016].

USDA Forest Service. 2011. Socioeconomic sustainability report Kiowa, Rita Blanca, Black 

Kettle, and McClellan Creek National Grasslands. Albuquerque, NM: U.S. Department 

of Agriculture, Forest Service, Southwestern Region. 44 p. http://a123.g.akamai.

net/7/123/11558/abc123/forestservic.download.akamai.com/11558/www/nepa/65916_

FSPLT2_056760.pdf [Accessed April 5, 2016]. 

USDA Forest Service. 2012a. Future of America’s forest and rangelands: Forest Service 2010 

Resources Planning Act Assessment. Gen. Tech. Rep. WO-87. Washington, DC: U.S. 

Department of Agriculture, Forest Service. 198 p.

USDA Forest Service. 2012b. National Forest System Land Management Planning, 36 CFR Part 

219. Federal Register. 77(68): 21162–21276.
92 USDA Forest Service RMRS-GTR-383. 2018

USDA Forest Service. 2013a. Fiscal year 2014 budget justification. Washington, DC: U.S. 

Department of Agriculture, Forest Service. http://www.fs.fed.us/aboutus/budget/2014/

FY2014ForestServiceBudgetJustificationFinal041613.pdf [Accessed April 5, 2016].

USDA Forest Service. 2013b. Forest Service cut and sold reports for all convertible products 

by region, State, and National Forest, 1980 to 2012. Washington, DC: U.S. Department of 

Agriculture, Forest Service. http://www.fs.fed.us/forestmanagement/products/sold-harvest/

cut-sold.shtml [Accessed April 5, 2016].

USDA Forest Service. 2015a. Forest Inventory EVALIDator Web-application 1.6.0.01. St. Paul, 

MN: U.S. Department of Agriculture, Forest Service, Northern Research Station. http://apps.

fs.fed.us/Evalidator/evalidator.jsp [Accessed April 13, 2015].

USDA Forest Service. 2015b. National visitor use monitoring program. Washington, DC: U.S. 

Department of Agriculture, Forest Service, Recreation, Heritage and Wilderness Resources. 

http://www.fs.fed.us/recreation/programs/nvum/ [Accessed March 24, 2015].

USDA Forest Service. [n.d.]. Calculations of national visitor use monitoring survey data, round 

3. [Proceed to the NVUM Results application.] http://www.fs.fed.us/recreation/programs/

nvum/ [Accessed March 12, 2016].

U.S. Geological Survey [USGS]. 2012. Gap Analysis Program, Protected Areas Database of the 

United States version 1.3. Economic Profile System – Human Dimensions Toolkit. http://

gapanalysis.usgs.gov/padus/data/metadata/ [Accessed April 5, 2016].

van Mantgem, P.J.; Stephenson, N.L.; Byrne, J.C.; [et al.]. 2009. Widespread increase of tree 

mortality rates in the western United States. Science. 323(5913): 521–524.

Venn, T.J.; Calkin, D.E. 2011. Accommodating non-market values in evaluation of wildfire 

management in the United States: Challenges and opportunities. International Journal of 

Wildland Fire. 20(3): 327–339.

Völker, M.; Waibel, H. 2010. Do rural households extract more forest products in times of crisis? 

Evidence from the mountainous uplands of Vietnam. Forest Policy and Economics. 12(6): 

407–414.

Wahlberg, M.M; Triepke, F.J.; Robbie, W.A.; [et al.]. 2016. Ecological response units of the 

Southwestern United States. Unpublished technical report on file with: U.S. Department of 

Agriculture, Forest Service, Southwestern Region, Albuquerque, NM. 203 p.

Wear, David N.; Huggett, Robert; Li, Ruhong; [et al.]. 2013. Forecasts of forest conditions in 

U.S. regions under future scenarios: A technical document supporting the Forest Service 2010 

RPA assessment. Gen. Tech. Rep SRS-170. Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 120 p.

Weeks, D.; Malone, P.; Welling, L. 2011. Climate change scenario planning: A tool for managing 

parks into uncertain futures. Park Science. 28(1): 26–33.

Weidner, E.; Todd, A. 2011. From the forest to the faucet: Drinking water and forests in the U.S. 

Unpublished report on file with: U.S. Department of Agriculture, Forest Service, Ecosystem 

Services and Markets, Washington, DC. http://www.fs.fed.us/ecosystemservices/FS_Efforts/

forests2faucets.shtml [Accessed April 5, 2016].

Westerling, A.L.; Hidalgo, H.G.; Cayan, D.R.; [et al.]. 2006. Warming and earlier spring increase 

western U.S. forest wildfire activity. Science. 313(5789): 940–943.

White, Eric M.; Goodding, Darren B.; Stynes, Daniel J. 2013. Estimation of National Forest 

spending averages from National Visitor Use Monitoring: Round 2. Gen. Tech. Rep. PNWGTR-883. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 71 p.

White, Eric M.; Stynes, Daniel J. 2008. National Forest visitor spending averages and the 

influence of trip-type and recreation activity. Journal of Forestry. 106(1): 17–24.

White, Eric M.; Stynes, Daniel J. 2010. Updated spending profiles for National Forest recreation 

visitors by activity. Unpublished project report on file with: M. Hand, U.S. Department of 

Agriculture, Forest Service, Washington, DC.

Williams, A.P.; Allen, C.D.; Macalady, A.K.; [et al.]. 2013. Temperature as a potent driver of 

regional forest drought stress and tree mortality. Nature Climate Change. 3(3): 292–297.
USDA Forest Service RMRS-GTR-383. 2018 93

Williams, A.P.; Allen, C.D.; Millar, C.I.; [et al.]. 2010. Forest responses to increasing aridity and 

warmth in the southwestern United States. PNAS. 107(50): 21289–21294.

Wilson, R.S.; Arvai, J.L. 2011. Structured decision making: Using decision research to improve 

stakeholder participation and results. Oregon Sea Grant publication ORESU-H-11-001. 

Corvallis, OR: Oregon State University. 

Yang, W.; Dietz, T.; Kramer, D.B.; [et al.]. 2015. An integrated approach to understanding 

the linkages between ecosystem services and human well-being. Ecosystem Health and 

Sustainability. 1(5): 1–12.

Yohe, Gary; Tol, Richard S.J. 2002. Indicators for social and economic coping capacity—

Moving toward a working definition of adaptive capacity. Global Environmental Change. 

12(1): 25–40.
94 USDA Forest Service RMRS-GTR-383. 2018

Appendix—Summaries of Forest-Level Socioeconomic 

Vulnerability Assessments

The analyses of exposure, sensitivity, and adaptive capacity can be used to explore 

how well-being may be affected by changes to the provision of ecosystem services. 

Although the analyses are not meant to be combined into a single vulnerability metric 

for each national forest and grassland, it is possible to get a broader understanding of 

vulnerability by examining the analysis results for each forest unit. 

A difficulty in creating forest-level vulnerability summaries is that the analyses 

were conducted at varying spatial and administrative scales (e.g., county, ranger district, forest unit, watershed). The following summaries do not attempt to aggregate or 

downscale results to a common scale. Instead, the results are examined for each forest to 

provide a broad view of socioeconomic vulnerability. For example, if a national forest 

and its ranger districts consistently appear to have low exposure, low sensitivity, and 

high adaptive capacity relative to other forests, then the forest is likely to exhibit low 

socioeconomic vulnerability. Given the multidimensionality of the analyses, results may 

point in different directions or suggest high vulnerability for some ecological changes 

(e.g., timber resources) and low vulnerability for others (e.g., water supply). A full discussion of these nuances is not feasible here; the summaries are meant as starting points 

for further exploration and detailed forest-level analysis.

Finally, we emphasize that the vulnerability assessment does not account for all 

factors that relate to socioeconomic vulnerability to ecological changes. Other ecological changes not examined in this report, and unobserved socioeconomic factors, may be 

important determinants of vulnerability that are not captured here.

Apache-Sitgreaves

Apache-Sitgreaves National Forest exhibits average exposure to ecological changes, although there is high variability in exposure across ranger districts. The Alpine and 

Springerville Ranger Districts tend to have low exposure measures, while Black Mesa 

and Lakeside Ranger Districts both rank in the upper quartiles of exposure. Primary 

sources of high exposure for these ranger districts are vegetative change, intersecting 

stressors, and timber.

Economic sensitivity is relatively low for the Apache-Sitgreaves. Relatively little 

employment and income in counties within the forest economic impact area are derived 

from resource sectors, probably reflecting the prominence of Maricopa County (Phoenix 

metropolitan area) in the regional economy.

Counties in the Apache-Sitgreaves economic impact area are rated relatively high 

on adaptive capacity measures. As with economic activity, the presence of Maricopa 

County plays an important role in adaptive capacity measures. However, there is great 

variability across counties within the Apache-Sitgreaves economic impact area. Navajo 

and Apache Counties rank relatively low on adaptive capacity, whereas Maricopa and 

Coconino rank relatively high.

Overall vulnerability may be low to moderate for Apache-Sitgreaves National 

Forest due to moderate exposure, low sensitivity, and high overall adaptive capacity. 
USDA Forest Service RMRS-GTR-383. 2018 95

But within-forest variability suggests that some areas could experience greater socioeconomic pressures due to ecological changes. These are likely to be areas that depend on 

ecosystem services from the Black Mesa and Lakeside Ranger Districts in the eastern 

portion of the forest, and those communities with limited economic connections to the 

larger metropolitan area in Maricopa County. 

Carson

Ranger districts in Carson National Forest tend to exhibit relatively low exposure 

to climate-related ecological change. Canjilon and Tres Piedras rank in the lowest 

or second lowest quartile for all exposure measures. The only instances of exposure 

ranking in the top quartile in the forest is for intersecting stressors in the Camino Real 

Ranger District and important surface water supply exposure in the Jicarilla Ranger 

District.

The Carson is among the forests with the highest sensitivity in the region. It is the 

only forest where economic contributions from resource sectors account for more than 

1 percent of both employment and income in the economic impact area. A large portion 

of this contribution is derived from recreation activities, of which the most sensitive is 

winter recreation. Potential reduced likelihood of viable ski seasons may negatively affect recreation for the forest, but other types of warm-weather activities may increase in 

the future, potentially offsetting negative consequences of decreased winter recreation.

Adaptive capacity is moderate for counties in the Carson economic impact area. 

However, average adaptive capacity probably masks variation between counties, and 

even within counties. 

Overall vulnerability is likely to be moderate for Carson National Forest. Although 

it appears to be highly sensitive to ecological changes and to have average to low 

adaptive capacity, exposure to climate-related ecological changes is low. That is, the 

likelihood of degradations in the provision of ecosystem services is low, although if 

drastic ecological changes do occur communities have a higher than average likelihood 

of being negatively affected.

Cibola

Cibola National Forest exhibits relatively high exposure, primarily driven by high 

likelihood of vegetative change on grazing allotments. The likelihood of vegetative 

change overall is higher than average in all ranger districts in the forest, although intersecting stressors and important surface watershed exposure tend to be moderate. In the 

grassland areas, we could not completely assess exposure due to data limitations. 

Economic sensitivity to ecological changes is low, although there is an important 

distinction between the grasslands and the other ranger districts. Resource sector employment and income are relatively important in counties that make up the economic 

impact area for the Cibola grasslands. In contrast, resource sectors for the other districts 

are relatively less economically important, probably due to the presence of Bernalillo 

County (Albuquerque metropolitan area) in the economic impact area for the nongrassland districts.
96 USDA Forest Service RMRS-GTR-383. 2018

Adaptive capacity is relatively high for the nongrassland ranger districts, and 

moderate but above average for the grasslands. The Cibola is spread across a large area 

in central New Mexico, and its economic impact area is composed of 11 counties. Given 

the range of socioeconomic conditions across these counties, it is likely that a high degree of heterogeneity exists for adaptive capacity.

Overall vulnerability is likely to be moderate to high for Cibola National Forest, 

with vulnerability probably higher for the grasslands areas. Given the large geographic 

area, more localized information about potential ecological changes and socioeconomic 

conditions could help future assessments.

Coconino

Coconino National Forest has relatively high exposure to climate-induced ecological changes. All ranger districts in the forest exhibit higher than average surface water 

exposure, and the Mogollon Rim Ranger District ranks higher than average on all exposure analyses except grazing exposure. Relatively low exposure to vegetative change 

is evident in the Mormon Lake and Red Rock Ranger Districts, although most other 

exposures are high for these areas.

Economic sensitivity is relatively low for the forest based on share of employment 

and income contributed by climate-sensitive resource sectors. As with other forests in 

central Arizona, the presence of Maricopa County plays a large role in keeping economic sensitivity low.

Adaptive capacity is high for the economic impact area associated with Coconino. 

As with sensitivity, Maricopa County is an important driver of this result, and variation 

across the economic impact area is likely. For example, Navajo County is ranked among 

the counties with the lowest adaptive capacity in the region.

Vulnerability is low overall for Coconino National Forest. Although there is higher 

than average likelihood of ecosystem service impacts due to climate change, low overall 

sensitivity and high adaptive capacity may provide a buffer against negative outcomes 

for communities that rely on ecosystem services from the forest. Some localized effects 

may be more severe, particularly for communities in Navajo County that rely on forest 

ecosystem services and have lower adaptive capacity. 

Coronado

Coronado National Forest exhibits moderate to high exposure to ecological 

changes, although this result is likely due to variation in exposure for different ecosystem services. All Coronado ranger districts exhibit high exposure to vegetative change 

and grazing, but relatively low exposure for surface water. Timber is also rated as low 

exposure because there is limited timberland in the forest. 

Sensitivity is moderate to low for the forest, with a low share of employment and 

income derived from resource sectors. Grazing and recreation contribute nearly equally 

to labor income for the forest economic impact area. However, given high exposure for 

grazing and ambiguous effects of climate on recreation, grazing sectors may be more 

likely to suffer economic impacts due to climate. 
USDA Forest Service RMRS-GTR-383. 2018 97

Adaptive capacity is moderate to high. The Coronado is the only forest in Arizona 

where Maricopa County is not included in the economic impact area. Two counties in 

the economic impact area are on the extremes of socioeconomic status: Pima County 

(which includes Tucson) ranks relatively high, whereas Santa Cruz County is among the 

counties ranked lowest on socioeconomic measures.

Overall vulnerability is likely to be moderate for Coronado National Forest. 

Certain ecosystem services, such as forage for livestock, have a higher likelihood of 

change and are likely to have greater economic impacts for communities that rely on 

these services. Although the area is on average likely to be able to cope with ecological 

changes, some areas (particularly in Santa Cruz County) may experience greater than 

average effects from moderate ecological changes.

Gila

Exposure to ecological changes is moderate for Gila National Forest. Although 

exposure rankings are high for the Black Range Ranger District, the Quemado and 

Reserve Ranger Districts have among the lowest relative exposure rankings, particularly 

for vegetative change and surface water exposure.

Counties in the Gila economic impact area are likely to be highly sensitive to 

ecological changes. The Gila is the most economically dependent on climate-sensitive 

resources in the region, with more than 1.5 percent of employment derived from 

resource sectors in the Gila economic impact area. Grazing activity is the largest contributor to resource employment and income in the area.

Adaptive capacity is low for counties that are associated with Gila National Forest. 

The indices for socioeconomic conditions are lower (indicating lower adaptive capacity) 

for the Gila than for any other forest in the region. Although indicators of socioeconomic conditions in Catron and Grant Counties in New Mexico are near the regional 

average, indicators for Apache County in Arizona are among the lowest in the region.

Socioeconomic vulnerability is likely to be high for Gila National Forest due to 

high sensitivity and low adaptive capacity. Although the exposure analyses suggest that 

the likelihood of climate-induced degradations to ecosystem service provision is close to 

the regional average, ecological changes may have a large negative effect on well-being 

for nearby communities. This effect may be felt especially acutely by communities in 

eastern Arizona, which may have limited capacity to adapt to changes and limited connections to large metropolitan areas in the region.

Kaibab

Kaibab National Forest is among the forests with the highest exposure in the 

region based on the factors studied in this report. All of the ranger districts in the forest 

rank in the highest quartile for exposure to vegetative change and changes to timber 

production. The Williams Ranger District also is among the districts with the highest 

exposure for intersecting stressors and important surface watersheds.

Economic sensitivity to ecological change is low in the Kaibab. About 1/10th of 

1 percent of employment in the Kaibab economic impact area is derived from forest 

resource sectors, with grazing accounting for the largest employment contribution. 
98 USDA Forest Service RMRS-GTR-383. 2018

However, exposure to climate change of grazing resources is moderate to low for the 

Kaibab. 

Adaptive capacity is moderate for the counties in the Kaibab economic impact 

area. Overall, socioeconomic indicators are slightly above average for these counties 

compared to the rest of the region, but significant variation exists across counties. 

Navajo County is among the counties with the lowest ratings on socioeconomic indicators, yet Coconino County in Arizona and Kane County in Utah are among the counties 

rated highest on socioeconomic indicators.

Overall vulnerability is likely to be moderate to low for Kaibab National Forest. 

High exposure is mitigated by relatively low economic sensitivity and average adaptive capacity. Certain economic sectors, such as grazing and timber sectors that rely 

on access to the Kaibab, may be at risk of ecological changes that result in economic 

disruption. Limited adaptive capacity may also put some communities, especially in 

Navajo County, at greater risk of negative consequences from ecological changes.

Lincoln

Lincoln National Forest is rated as the forest with the highest exposure. For both 

the Smokey Bear and Sacramento Ranger Districts all of the exposure measures are 

above average or in the top quartile, indicating high exposure across all ecosystem services. The Guadalupe Ranger District exhibits lower exposure for intersecting stressors 

and timber products, but higher than average exposure for the other measures (vegetative change, important surface watersheds, and grazing resources).

Economic sensitivity is relatively low, with about 1/10th of 1 percent of employment derived from climate-sensitive forest resource sectors. Recreation and grazing 

sectors are the primary resource sectors contributing to employment in the forest 

economic impact area; these two sectors account for 85 percent of resource sector employment and 71 percent of resource sector labor income. Climate effects on recreation 

activities are ambiguous, but all three ranger districts are rated at above average or high 

exposure to potential climate effects to grazing resources.

Adaptive capacity is low, with socioeconomic indicators in the economic impact 

area counties well below the regional average. The Lincoln economic impact area 

includes El Paso County in Texas, which is among the counties with the lowest ratings 

on socioeconomic indicators in the region. Because it is also populous, this county pulls 

down the overall average for the economic impact area even though a change to forest 

ecosystem services is likely to have minimal impact in the metropolitan economy associated with El Paso.

Overall vulnerability for Lincoln National Forest is likely to be moderate due to 

high exposure but limited sensitivity and moderate adaptive capacity outside of El Paso 

County. 

Prescott

Prescott National Forest exhibits overall low exposure of ecosystem services to 

ecological changes. Exposure measures are below average or in the lowest quartile for 

most ecosystem services, except for important surface watersheds in the Chino Valley 
USDA Forest Service RMRS-GTR-383. 2018 99

and Verde Ranger Districts. Higher watershed exposure is likely because multiple watersheds in the forest are upstream suppliers of surface water for Maricopa County.

Economic sensitivity is low for Prescott National Forest. Forest resource sectors 

contribute a smaller share of employment and income to the economic impact area than 

any other national forest. This is partly due to the inclusion of Maricopa County in the 

economic impact area, although total employment in the resource sectors is relatively 

small.

Adaptive capacity is high for Prescott National Forest. However, only two counties 

are included in this area: Maricopa and Yavapai. Both are rated as relatively high on 

socioeconomic indicators, with Maricopa County being the main driver of the adaptive 

capacity measures due to its large population.

Vulnerability is likely to be low for Prescott National Forest due to low exposure, 

low sensitivity, and high adaptive capacity. However, the quantitative measures may obscure localized vulnerabilities due to the presence of Maricopa County in the economic 

impact area. Communities not well connected with the Phoenix metropolitan area and 

those reliant on grazing may be more vulnerable. Further, the presence of important surface watersheds in the forest that are likely to serve greater Phoenix suggests additional 

potential vulnerabilities to climate-related ecological changes.

Santa Fe

Exposure to ecological changes is moderate for Santa Fe National Forest. The 

forest covers a large area in five ranger districts, and exhibits significant variation in exposure. Above-average exposure is measured for intersecting stressors in the Jemez and 

Española Ranger Districts, while the lowest exposure ecosystem services are vegetative 

change and grazing resources in the Coyote and Cuba Ranger Districts, and also grazing 

resources in the Jemez Ranger District.

Economic sensitivity is moderate for Santa Fe National Forest. About one-fourth 

of 1 percent of employment in the Santa Fe economic impact area is derived from forest resource sectors. Recreation is the largest climate-sensitive forest resource sector, 

accounting for 70 percent of employment and 78 percent of labor income in climatesensitive resource sectors. 

Adaptive capacity is high for the Santa Fe, with most counties in the economic 

impact area being rated high for socioeconomic indicators. The exception is McKinley 

County, which is among the counties rated lowest on socioeconomic status measures 

(but it has a relatively small population and hence a smaller effect on the overall Santa 

Fe adaptive capacity measures). 

Vulnerability is moderate to low for the Santa Fe. Some pockets of greater vulnerability could arise, however, in communities with limited adaptive capacity (e.g., in 

McKinley County) or those sensitive to economic changes. Climate effects on recreation 

tend to be ambiguous, but climate effects on winter recreation tend to be negative. The 

Santa Fe has higher sensitivity for recreation due in part to winter recreation, which 

could raise vulnerability.
100 USDA Forest Service RMRS-GTR-383. 2018

Tonto

Overall exposure of ecosystem services to ecological change is moderate for Tonto 

National Forest. However, for all ranger districts in the forest exposure for important 

surface water supply is high because watersheds in the forest supply surface water for 

Maricopa County. 

Economic sensitivity is relatively low for the Tonto. Relative to other forests, total 

employment and income from climate-sensitive forest resource sectors are high, but 

make up a small share of total employment and income in the area due to the presence 

of Maricopa County in the economic impact area. Recreation is the most important forest resource sector, accounting for 65 percent of employment and more than 80 percent 

of labor income derived from climate-sensitive forest resources.

Adaptive capacity is high for the counties in the Tonto economic impact area. As 

with other forests where Maricopa County is included in the economic impact area, the 

relatively high socioeconomic ratings and high population there tend to prop up adaptive capacity measures.

Overall vulnerability for Tonto National Forest is likely to be low due to low 

sensitivity and high adaptive capacity. Higher exposure for important watersheds that 

serve greater Phoenix is a potential source of vulnerability. Communities in Gila County 

in Arizona, which is rated lower on socioeconomic measures, may be more vulnerable 

than other counties associated with Tonto National Forest.

To learn more about RMRS publications or search our online titles:

RMRS web site at: https://www.fs.fed.us/rmrs/rmrs-publishing-services

In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or 

administering USDA programs are prohibited from discriminating based on race, color, national origin, 

religion, sex, gender identity (including gender expression), sexual orientation, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal 

or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not 

all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident.

Persons with disabilities who require alternative means of communication for program information (e.g., 

Braille, large print, audiotape, American Sign Language, etc.) should contact the responsible Agency or 

USDA’s TARGET Center at (202) 720-2600 (voice and TTY) or contact USDA through the Federal Relay 

Service at (800) 877-8339. Additionally, program information may be made available in languages other 

than English.

To file a program discrimination complaint, complete the USDA Program Discrimination Complaint 

Form, AD-3027, found online at http://www.ascr.usda.gov/complaint_filing_cust.html and at any USDA 

office or write a letter addressed to USDA and provide in the letter all of the information requested in 

the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or 

letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil 

Rights, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) 

email: program.intake@usda.gov.