Scientific Investigations Report 2012−5251

U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the Bureau of Land Management

Ecosystem Services Valuation to Support Decisionmaking

on Public Lands—A Case Study of the San Pedro River 

Watershed, Arizona
This document was produced through an interagency effort between the Bureau of Land Management and the 

U.S. Geological Survey. The following BLM number has been assigned for tracking and administrative purposes: 

BLM Technical Note 442.

COVER: Aerial view of the San Pedro River Valley, Arizona. Photograph used by permission: © Adriel Heisey.
Ecosystem Services Valuation to 

Support Decisionmaking on Public Lands—

A Case Study of the San Pedro River 

Watershed, Arizona

By Kenneth J. Bagstad, Darius Semmens, Rob Winthrop, Delilah Jaworksi, 

and Joel Larson

Prepared in cooperation with the Bureau of Land Management

Scientific Investigations Report 2012–5251

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Suggested citation:

Bagstad, K.J., Semmens, Darius, Winthrop, Rob, Jaworski, Delilah, and Larson, Joel, 2012, Ecosystem services valuation 

to support decisionmaking on public lands—A case study of the San Pedro River watershed, Arizona: U.S. Geological 

Survey Scientific Investigations Report 2012–5251, 93 p.
iii

Contents

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

Overview and Findings.........................................................................................................................1

Rationale.................................................................................................................................................2

Introduction.....................................................................................................................................................2

Description and Goals of This Project...............................................................................................2

Links to Strategic Goals of Arizona Bureau of Land Management ..............................................4

Literature Review..................................................................................................................................4

Ecosystem Services Analyzed............................................................................................................4

Water.............................................................................................................................................6

Carbon............................................................................................................................................8

Biodiversity ...................................................................................................................................8

Cultural Services..........................................................................................................................8

Ecosystem Services Assessment and Valuation—Methods and Tools....................................11

Primary Valuation................................................................................................................................11

Value Transfer......................................................................................................................................11

Point Transfer .............................................................................................................................13

Function Transfer.......................................................................................................................13

Function Transfer Using Defenders of Wildlife’s Wildlife Habitat 

Benefits Estimation Toolkit..........................................................................................14

Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST).......................................15

Artificial Intelligence for Ecosystem Services (ARIES)................................................................15

Multiscale Integrated Models of Ecosystem Services (MIMES) ...............................................16

EcoMetrix.............................................................................................................................................16

EcoAIM.................................................................................................................................................16

ESValue.................................................................................................................................................16

Natural Assets Information System (NAIS) and Simple, Effective Resource 

for Valuing Ecosystem Services (SERVES) .......................................................................16

Social Values for Ecosystem Services (SolVES) ...........................................................................17

Envision.................................................................................................................................................17

Ecosystem Portfolio Model (EPM) ...................................................................................................17

EcoServ.................................................................................................................................................18

Investing in Forests (InFOREST) .......................................................................................................18

Ecosystem Services Review (ESR) ..................................................................................................18

United Nations Environment Programme–World Conservation Monitoring Centre 

Ecosystem Services Toolkit.................................................................................................18

Results of This Study..........................................................................................................................18

Parallel Efforts.....................................................................................................................................19

Methods.........................................................................................................................................................19

Selection and Description of the Study Area.................................................................................19

Selection and Application of Ecosystem Services, Valuation Methods, and Tools ................20

Ecosystem Services ..................................................................................................................20

Evaluative Criteria......................................................................................................................20

Methods and Tools Considered in This Report.....................................................................21

Data Needs .................................................................................................................................21

Description of Assessment and Valuation Processes.........................................................21
iv

Scenarios .............................................................................................................................................22

Scenario Group 1: Urban Growth............................................................................................22

Scenario Group 2: Mesquite Management ..........................................................................23

Scenario Group 3: Water Augmentation................................................................................24

Scenario Group 4: Climate Change.........................................................................................25

Results ...........................................................................................................................................................25

Results by Ecosystem Service..........................................................................................................25

Carbon .........................................................................................................................................25

Water...........................................................................................................................................27

Biodiversity .................................................................................................................................34

Cultural Services: Viewsheds, Open-Space Proximity, Recreation ..................................34

Valuation Results for Scenarios .................................................................................... 39

Urban Growth Scenarios..........................................................................................................39

Mesquite Management Scenario ...........................................................................................41

Water-Augmentation Scenarios..............................................................................................41

Conclusions...................................................................................................................................................41

General Findings .................................................................................................................................41

Comparability of Spatially Explicit Results from ARIES and InVEST Models...................45

Advantages and Disadvantages of Alternative Approaches .............................................46

Application of Past Primary Valuation Studies ............................................................46

Value Transfer....................................................................................................................46

Ecosystem Services Review...........................................................................................47

Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST).....................47

Artificial Intelligence for Ecosystem Services (ARIES)..............................................52

Other Tools Not Tested in the Pilot Study .....................................................................52

Lessons Learned in the Valuation Process: Steps to Follow and Avoid...........................53

Integration of Past Work on the San Pedro River Watershed 

into Ecosystem Service Models ................................................................................54

Nonmonetary and Tribal Values ..............................................................................................54

San Pedro Riparian National Conservation Area Outcomes.......................................................54

Agency-Wide Outcomes ...................................................................................................................55

Implications for Future Work ............................................................................................................56

Incorporating Ecosystem Service and Value Information into the Decision Process........... 56

Data Sharing to Support Widespread Ecosystem Services Mapping and Valuation ........... 57

Follow-Up Studies......................................................................................................................57

Acknowledgments.......................................................................................................................................58

References Cited..........................................................................................................................................58

Appendix A. Spatial Data and Assumptions Used in InVEST Models.................................................69

Appendix B. Spatial Data and Assumptions Used in ARIES Models ..................................................74

Appendix C. Literature Review of Value Transfer and Best Practices ...............................................75

Appendix D. Additional Participants in Model and Document Review Process...............................81

Appendix E. Annotated Bibliography of Ecological and Socioeconomic References 

Related to the San Pedro River watershed and the U.S. Southwest.....................................82

Appendix F. Summary of Primary Economic Valuation Studies for the San Pedro 

River watershed and the U.S. Southwest ..................................................................................92
v

Figures

1. Strategic goals of Arizona Bureau of Land Management .....................................................6

2. San Pedro River watershed study area, States of Arizona (United States) 

and Sonora (Mexico)....................................................................................................................9

3. Land ownership within the San Pedro River watershed, States of Arizona 

(United States) and Sonora (Mexico), and Bureau of Land Management 

ownership in surrounding region.............................................................................................10

4. Urban growth scenarios, San Pedro River watershed, States of Arizona 

(United States) and Sonora (Mexico)......................................................................................23

5. Land cover before (A) and after (B) mesquite management, San Pedro 

River watershed, Arizona ..........................................................................................................24

6. Hydrologic scenarios (groundwater futures) and year 2006 baseline 

conditions in the San Pedro River watershed, Arizona........................................................25

7. Carbon sequestration, storage, and valuation estimates, San Pedro River 

watershed, States of Arizona (United States) and Sonora (Mexico)—InVEST 

model results ...............................................................................................................................26

8. Carbon sequestration, stored carbon release, and greenhouse gas emissions 

estimates, San Pedro River watershed, Arizona—ARIES model results..........................28

9. Water yield estimates for urban growth scenarios under representative wet 

year (2007) and dry year (2002) conditions, and year 2000 water consumption, 

San Pedro River watershed, Arizona—InVEST model results............................................30

10. Surface-water source, sink, and useage estimates, San Pedro River watershed, 

Arizona—ARIES model results.................................................................................................32

11. Biodiversity (habitat quality) and change estimates under urban growth and 

mesquite management scenarios, San Pedro River watershed, Arizona—

InVEST model results .................................................................................................................35

12. Estimates of relative value of open-space proximity and viewsheds (source, 

use, and flow results) under urban growth scenarios, San Pedro River 

watershed, Arizona—ARIES model results ...........................................................................37

13. Visual impacts to viewsheds, San Pedro River watershed, States of Arizona 

(United States) and Sonora (Mexico)—InVEST model results ...........................................38

14. Source values for birding and deer hunting (San Pedro River watershed, 

Arizona) and for birding (San Pedro Riparian National Conservation Area)—

ARIES model results...................................................................................................................40

15. Potential steps in assessing ecosystem services.................................................................52

Tables

1. Gains and losses in ecosystem services modeled under three scenario 

categories, San Pedro River watershed, States of Arizona (United States) 

and Sonora (Mexico).........................................................................................................................1

2. Management issues on the San Pedro River watershed, States of 

Arizona (United States) and Sonora (Mexico), as identified by managers 

and stakeholders ..........................................................................................................................5

3. Potential effects of mesquite management and native grassland restoration 

on ecosystem services, San Pedro River watershed, Arizona.............................................5

4. Ecosystem services endpoints of services to be valued, San Pedro River 

watershed, States of Arizona (United States) and Sonora (Mexico)...................................7

5. Ecosystem service tools described in this report, San Pedro River 

watershed, Arizona ....................................................................................................................12
vi

6. Methods used for ecosystems services valuation, San Pedro River watershed, 

States of Arizona (United States) and Sonora (Mexico)......................................................22

7. Carbon sequestration, storage, and valuation estimates, San Pedro River 

watershed, States of Arizona (United States) and Sonora (Mexico)—InVEST 

model results ...............................................................................................................................27

8. Carbon sequestration, stored carbon release, and greenhouse gas emissions, 

San Pedro River watershed, Arizona—ARIES model results. ............................................29

9. Water yield and demand estimates, San Pedro River watershed, Arizona—

InVEST model results .................................................................................................................31

10. Surface water sources, sinks, and usage estimates, San Pedro River 

watershed, Arizona—ARIES model results ...........................................................................33

11. Economic value of water in the San Pedro River watershed, Arizona..............................34

12. Habitat quality estimates, San Pedro River watershed, Arizona—

InVEST model results .................................................................................................................36

13. Relative value of open space proximity and viewsheds, San Pedro River 

watershed, States of Arizona (United States) and Sonora (Mexico)—

ARIES model results...................................................................................................................38

14. Visual impacts to viewsheds, San Pedro River watershed, Arizona—

InVEST model results .................................................................................................................39

15. Relative recreation source value estimates, San Pedro River watershed, 

Arizona—ARIES model results.................................................................................................41

16. Biophysical and relative values of ecosystem service changes under 

alternative urban growth scenarios, San Pedro River watershed, States 

of Arizona (United States) and Sonora (Mexico)—InVEST model results........................42

17. Biophysical and relative values of ecosystem service changes under 

alternative urban growth scenarios, San Pedro River watershed, Arizona—

ARIES model results...................................................................................................................43

18. Economic value of ecosystem service changes under alternative urban 

growth scenarios, San Pedro River watershed, States of Arizona (United 

States) and Sonora (Mexico)—InVEST mode results ..........................................................44

19. Economic value of ecosystem service changes under alternative urban growth 

scenarios, San Pedro River watershed, Arizona—ARIES model results.................................. 44

20. Total annual cost of ecosystem service loss under urban-growth scenarios, 

San Pedro River watershed, Arizona—ARIES and InVEST model results........................44

21. Biophysical and relative values of ecosystem service changes under 

mesquite management scenario, San Pedro River watershed, Arizona—

InVEST model results .................................................................................................................44

22. Biophysical and relative values of ecosystem service changes under 

mesquite management scenario, San Pedro River watershed, Arizona—

ARIES model results...................................................................................................................44

23. Economic value of ecosystem service changes under mesquite management 

scenario, San Pedro River watershed, Arizona—InVEST model results..........................45

24. Economic value of ecosystem service changes under mesquite management 

scenario, San Pedro River watershed, Arizona—ARIES model results............................45

25. Total annual cost or benefit of ecosystem service gain or loss under mesquite 

management scenario, San Pedro River watershed, Arizona—ARIES and 

InVEST model results .................................................................................................................45

26. Relative values of ecosystem service changes under water augmentation 

scenarios, San Pedro River watershed, Arizona—ARIES model results..........................45

27. Estimated time to complete ecosystem service assessment and valuation 

by use of alternative methods ..................................................................................................47

28. All ecosystem service tools relative to key evaluative criteria ..........................................48
vii

Abbreviations Used in This Report

AGAVES Assessment of Goods and Valuation of Ecosystem Services

AGWA Automated Geospatial Watershed Assessment tool

ARIES Artificial Intelligence for Ecosystem Services

BLM Bureau of Land Management

BOR Bureau of Reclamation

DOD Department of Defense

DOI Department of the Interior

EA Environmental Assessment

EIS Environmental Impact Statement

EPM Ecosystem Portfolio Model

ESR Ecosystem Services Review

EVRI Environmental Valuation Reference Inventory

FLPMA Federal Land Policy and Management Act

GIS Geographic Information System

InFOREST Investing in Forests

InVEST Integrated Valuation of Ecosystem Services and Tradeoffs

MIMES Multiscale Integrated Models of Ecosystem Services

NAIS Natural Assets Information System

NEPA National Environmental Policy Act

NOAA National Oceanic and Atmospheric Administration

NPS National Park Service

NRCS Natural Resources Conservation Service

OMB Office of Management and Budget

PRISM Parameter-elevation Regressions on Independent Slopes Model

RMP Resource Management Plan

SERVES Simple and Effective Resource for Valuing Ecosystem Services

SolVES Social Values for Ecosystem Services

SPRNCA San Pedro Riparian National Conservation Area

STATSGO State Soil Geographic database

UNEP–WCMC United Nations Environment Programme–World Conservation 

Monitoring Centre

US EPA United States Environmental Protection Agency

US EPA–ORD U.S. Environmental Protection Agency–Office of Research and Development

USDA United States Department of Agriculture

USGS United States Geological Survey

WIFA Water Infrastructure Finance Authority

WTP Willingness to Pay

Ecosystem Services Valuation to Support Decisionmaking 

on Public Lands—A Case Study of the San Pedro River 

Watershed, Arizona

By Kenneth J. Bagstad, Darius Semmens, Rob Winthrop, Delilah Jaworski, and Joel Larson

associated with model outputs, valuation methods, and discount 

rates applied. Further guidance on representing uncertainty and 

applying uncertain results in decisionmaking would benefit both 

tool developers and BLM offices in using ecosystem services 

to compare management tradeoffs. Decisionmakers and BLM 

managers at the State-, district-, and field- office level would 

also benefit from continuing model improvements, training, and 

guidance on tool use that can be provided by USGS, BLM, and 

the Department of the Interior.

Tradeoffs were identified in the level of effort needed 

to parameterize and run tools and the amount and quality of 

information they provide to the decision process. We found 

the Wildlife Habitat Benefits Estimation Toolkit, Ecosystem 

Services Review, and United Nations Environment Programme–

World Conservation Monitoring Centre Ecosystem Services 

Toolkit to be immediately feasible for application by BLM, 

given proper guidance on their use. It is also feasible for BLM 

to use the InVEST model, but in early 2012 the process of 

parameterizing the model required resources and expertise that 

are unlikely to be available in most BLM district or field offices. 

Application of past primary valuation is feasible, but developing 

new primary-valuation studies is too time consuming for regular 

application. Value transfer approaches (aside from the Wildlife 

Habitat Benefits Estimation Toolkit) are best applied carefully 

on the basis of guidelines described in this report, to reduce 

transfer error. The ARIES model can provide useful information 

in regions modeled in the past (Arizona, California, Colorado, 

and Washington), but it lacks some features that will improve 

its usability, such as a generalized model that could be applied 

anywhere in the United States. Eleven other tools described in 

this report could become useful as the tools more fully develop, 

in high-profile cases for which additional resources are available for tool application or in case-study regions where placespecific models have already been developed.

Executive Summary

Overview and Findings

This report details the findings of the Bureau of Land 

Management (BLM)–U.S. Geological Survey (USGS) 

Ecosystem Services Valuation Pilot Study. This project evaluated alternative methods and tools that quantify and value 

ecosystem services, and it assessed the tools’ readiness for use 

in BLM’s decisionmaking process. We tested these tools on 

the San Pedro River watershed in northern Sonora, Mexico 

and southeast Arizona. The study area includes BLM-managed 

San Pedro Riparian National Conservation Area, which has been 

a focal point for conservation activities and scientific research in 

recent decades. We applied past site-specific primary valuation 

studies, value transfer, the Wildlife Habitat Benefits Estimation 

Toolkit, and the Integrated Valuation of Ecosystem Services and 

Tradeoffs (InVEST) and Artificial Intelligence for Ecosystem 

Services (ARIES) models to value locally important ecosystem 

services for the San Pedro River watershed—water, carbon, 

biodiversity, and cultural values. We tested these approaches 

on a series of scenarios to evaluate ecosystem service changes 

and the ability of the tools to accommodate scenarios. A suite 

of additional tools were either at too early a stage of development to run, were proprietary, or were place-specific tools 

inappropriate for application to the San Pedro River watershed. 

We described the strengths and weaknesses of these additional 

ecosystem service tools against a series of evaluative criteria 

related to their usefulness for BLM decisionmaking.

Using these tools, we quantified gains or losses of 

ecosystem services under three categories of scenarios: urban 

growth, mesquite management, and water augmentation 

(table 1). These results quantify tradeoffs and could be useful 

for decisionmaking within BLM district or field offices. Results 

are accompanied by a relatively high level of uncertainty 

Table 1. Gains and losses in ecosystem services modeled under three scenario categories, San Pedro River watershed, States 

of Arizona and Sonora, Mexico.

Scenario Carbon sequestration

and storage Water supply Biodiversity (habitat quality)

Aesthetic

viewsheds

Proximity to

open space

Recreation

Urban growth Loss Loss Loss Gain Gain Loss.

Mesquite management Loss Gain Gain

Water augmentation Gain.
2 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

To improve the value of these tools in decisionmaking, 

we suggest scientific needs that agencies such as USGS can 

help meet—for instance, development and support of data 

archives. Such archives could greatly reduce resource needs 

and improve the reliability and consistency of results. Given 

the rapid state of evolution in the field, periodic follow-up 

studies on ecosystem services tools would help to ensure that 

BLM and other public land management agencies are kept up 

to date on new tools and features that bring ecosystem services 

closer to readiness for use in regular decisionmaking.

Rationale

Ecosystem service valuation has been a subject of 

academic interest for decades, but it has only recently 

matured to the point where it is beginning to inform policymaking (Salzman, 2005; Ruhl and others, 2007; Daily and 

others, 2009). Spurred by growing demand for more sophisticated analysis of the social and economic consequences of 

land-management decisions, BLM launched a pilot project with 

USGS to assess the usefulness and feasibility of ecosystem 

service valuation as an input to decisionmaking. The project 

analyzed ecosystem services in the entire San Pedro River 

watershed—not just BLM-managed portion—to more fully 

consider complex social and ecological relationships that 

transcend administrative divisions.

“Ecosystem services” are the benefits that nature provides 

to humans. Ecosystem services are essential for meeting basic 

human needs (for example, clean drinking water), including 

support for psychological well-being (for example, aesthetic 

enjoyment). Services are commonly subdivided according 

to the type of benefit provided. The Millennium Ecosystem 

Assessment, perhaps the best-known ecosystem services classification, divides services into four categories (Millennium 

Ecosystem Assessment, 2005):

1. Provisioning services (for example, water, timber, 

other raw materials)

2. Regulating services (for example, flood regulation or 

carbon sequestration, which supports climate regulation).

3. Cultural services (for example, recreational and 

spiritual uses)

4. Supporting services (for example, nutrient cycling, 

biodiversity, net primary productivity).

Ecosystems are commonly studied in biophysical terms; 

ecosystem service valuation is meant to add to, not substitute for, existing methods of analysis. Economic valuation 

of ecosystem services improves decisionmakers’ ability to 

compare commodity and noncommodity uses of the land. 

BLM commonly analyses the economic impact of alternative 

resource-management scenarios on income and employment. 

However, outside of well-studied services such as recreation, 

it is much less common for BLM’s economic analyses to 

consider the value of goods and services not traded in markets 

(nonmarket goods and services, including most ecosystem 

services). These nonmarket goods and services are typically 

the opportunity cost of resource extraction, and their quantification can more fully show the costs and benefits of such 

decisions. The public is increasingly demanding more inclusive economic analyses that encompass nonmarket values, 

and ecosystem service valuation is a substantial component 

of such analyses.

The perceived difficulty of conducting credible, replicable, and legally defensible ecosystem service valuation has 

limited its use among Federal agencies. One of the central 

aims of this pilot study was to evaluate the feasibility of conducting ecosystem service valuation given the limited capacity 

at BLM offices, particularly district and field offices. With 

this charge, we evaluated numerous valuation methods and 

tools to determine which are capable of providing defensible 

ecosystem service values without requiring resources beyond 

the reach of BLM.

Along with assessing the feasibility of conducting 

ecosystem service valuation, we were equally concerned with 

its relevance to BLM’s district and field office managers, the 

primary potential consumers of value information. Ecosystem 

service valuation is useful only if it improves the ability to 

identify the tradeoffs between alternative management actions 

facing decisionmakers. From the outset of the project, Gila 

District and San Pedro Riparian National Conservation Area 

managers and staff were involved with defining the direction 

of the analysis.

Introduction

Description and Goals of This Project

The Bureau of Land Management (BLM)–U.S. Geological 

Survey (USGS) Ecosystem Services Valuation Pilot study was 

conceived to assess the usefulness of ecosystem service valuation to BLM’s resource-management decisionmaking process. 

A relatively large body of research identifies ecosystem services 

(the benefits that ecosystems and their structure and processes 

provide to humans) (Millennium Ecosystem Assessment, 

2005), studies the ecological processes that underlie ecosystem 

services, and values certain services. However, the development 

of methods and tools that integrate ecology, economics, and 

geography to support decisionmaking is a much more recent 

phenomenon (Ruhl and others, 2007; Daily and others, 2009).

The purpose of the pilot study described in this report 

was twofold. First, we sought to determine which, if any, 

methods for valuing ecosystems are ripe for operational use 

by BLM. Second, we explored the usefulness of an ecosystem 

service valuation framework to BLM’s land and resource decisions. Although BLM commissioned this study and we discuss 

results in the context of BLM decisionmaking, we expect 
Introduction 3

that our results will be relevant to other resource managers 

interested in bringing ecosystem services into various 

decisionmaking processes.

BLM manages some 245 million acres, primarily in the 

Western United States, and 700 million acres of subsurface 

mineral estate. BLM’s multiple-use mission requires that 

it find an appropriate balance between nonextractive uses 

such as habitat conservation, recreation, and archaeological 

heritage protection, and the extractive use of resources such 

as timber, oil and gas, coal, uranium, and other minerals. 

Decisions that allocate lands and resources among these uses 

are made through the development of resource management 

plans (RMPs) and the analysis of proposed projects through 

environmental impact statements (EISs) and environmental 

assessments (EAs).

RMPs set overall land use allocations for a given management area, normally a field office, which usually covers 

several million acres. Within that framework, EISs and EAs 

identify the benefits and harms, both environmental and social, 

of proposed projects and alternative actions. Because BLM’s 

planning decisions generally have consequences beyond 

BLM-administered lands, the analyses provided in RMPs—

potentially including information on ecosystem services—

are also used by State and local governments and other 

stakeholders. Two Federal laws have a particularly important 

role in guiding BLM’s plan development and project assessment: the Federal Land Policy and Management Act (FLPMA) 

and the National Environmental Policy Act (Bureau of Land 

Management, 2005).

FLPMA declares that “the national interest will be best 

realized if the public lands and their resources are periodically 

and systematically inventoried and their present and future 

use is projected through a land use planning process coordinated with other Federal and State planning efforts” (FLPMA 

Sec. 102 [43 U.S.C 1701] (a)(2)). Several of its planning 

requirements are relevant for the consideration of ecosystem 

service values—specifically, that RMPs shall

(1) use and observe the principles of multiple use 

and sustained yield . . . ;

(2) use a systematic interdisciplinary approach to 

achieve integrated consideration of physical, 

biological, economic, and other sciences;

(3) give priority to the designation and protection 

of areas of critical environmental concern;

(4) rely, to the extent it is available, on the inventory of 

the public lands, their resources, and other values;

(5) consider present and potential uses of the public lands;

(6) consider the relative scarcity of the values 

involved . . . ; [and]

(7) weigh long-term benefits to the public against shortterm benefits (FLPMA Sec. 202 [43 U.S.C 1712] (c)).

The National Environmental Policy Act (NEPA) established a landmark national environmental policy that encourages environmental protection and informed decisionmaking. 

It provides the means to carry out these goals by 

• mandating that every Federal agency prepare a 

detailed statement of the effects of “major Federal 

actions significantly affecting the quality of the 

human environment,” 

• establishing the need for agencies to consider 

alternatives to those actions, and

• requiring the use of an interdisciplinary process in 

developing alternatives and analyzing environmental 

effects (Bureau of Land Management, 2008: 1).

Although the analysis of ecosystem services is appropriate for inclusion in NEPA documents, to date (early 2012) 

they have rarely contained such analyses, with the exception of historically well-quantified nonmarket values such as 

recreation (Ruhl and others, 2007). Indeed, the act’s focus on 

socioeconomic and environmental effects makes ecosystem 

services a potentially powerful integrating factor to consider in 

EAs and EISs. The limitation of requiring ecosystem services 

analysis within NEPA documents lies in the nascent state of 

the science of ecosystem services. Without tools and standards 

for measuring, quantifying, and valuing ecosystem services, 

Federal agencies, the private sector, and the general public 

are unlikely to support incorporation of ecosystem services 

into NEPA or other decisionmaking processes. The recent 

emergence of tools designed to support such decisionmaking 

offers initial insight into how services could be measured and 

compared for such decisionmaking processes.

Because BLM manages land for multiple uses, information 

to better inform managers about tradeoffs between commodities 

production and ecosystem services could be particularly 

useful. Commodities are derived from ecosystem structure 

and have economic values that can be relatively easily monetized. Ecosystem structure, in conjunction with the ecosystem 

processes it supports, generates ecosystem services that are nonexcludible or nonrival, making it difficult to clearly understand 

their economic value (Excludability is a legal characteristic that 

limits access to a good to those able to pay for it—for example, 

nonexcludible goods can be made excludible by introducing 

access fees to a park or emissions fees for the release of pollutants. Rivalness is a physical characteristic of a good that limits 

its use to one user or user group—for example, the consumptive use of water or other resources is rival, while water use 

for recreation is nonrival. (Samuelson, 1954)). As a result, 

commodities or ecosystem goods may be overconsumed at the 

expense of ecosystem services. This overconsumption has been 

termed the “tragedy of ecosystem services” (Lant and others, 

2008) or the “macroallocation problem” between ecosystem 

structure and function (Farley, 2008). By understanding, 

mapping, and valuing ecosystem services, BLM can better 

manage this tradeoff, a goal that directly addresses the multiple 

use–sustained yield mandate set forth in FLPMA.
4 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Incorporating ecosystem services into decisionmaking 

offers several potential advantages to BLM and other land 

managers charged with achieving a sustainable, equitable, and 

efficient allocation of goods and services to the public. Incorporating ecosystem services into BLM decisionmaking offers 

the potential for users to

• Better understand how ecosystem service benefits 

accrue to private land from adjacent public land, and 

the reverse.

• Evaluate tradeoffs in development and resourcemanagement decisions.

• Identify management discrepancies between jurisdictions, in cases in which a set of activities in one area 

affects neighboring jurisdictional units.

• Better differentiate categories of beneficiaries and 

stakeholders associated with management decisions 

and thus more effectively analyze their impacts.

• Reduce the incommensurability of costs and benefits 

when management tradeoffs are considered.

• Increase the scale of analysis by identifying cases 

where management activities have positive or negative 

effects beyond the boundaries of a BLM unit.

• Increase BLM’s responsibility and responsiveness in 

providing market and nonmarket goods to state and 

local communities and stakeholders.

This pilot study is intended to guide BLM at national, 

State, district, and field offices concerning the current readiness of ecosystem service valuation methods and tools. It 

does not support a particular set of management decisions 

for the San Pedro River watershed or elsewhere. Although this 

study assessed year 2000 conditions and a series of scenarios 

for that watershed, results are intended as proof-of-concept 

for the methods used and are not at this point intended to 

guide management.

San Pedro Riparian National Conservation Area 

(SPRNCA) and Gila District managers and stakeholders 

identified a number of resource-management issues relevant 

to the San Pedro River watershed whose connection to and 

effect on ecosystem services could be further explored (table 

2). Ecological and socioeconomic effects, appropriate valuation methods, and effects on specific stakeholders could then 

be identified (van Beukering and others, 2003). Potential 

ecosystem service effects of a specific management decision, mesquite management, with the intent of restoring 

native grassland, illustrate the linkages between management 

actions and potential changes in ecosystem services (table 3). 

Although this study did not precisely value and model changes 

to all of these services, it shows how a range of potential 

effects and affected parties can be identified by using an 

ecosystem services approach.

Links to Strategic Goals of Arizona Bureau 

of Land Management

The project estimated values for four ecosystem services. 

In determining which services to evaluate, the project team 

considered the link between the ecosystem services and 

Arizona BLM’s strategic goals . Ecosystem services evaluated 

were (1) water (valued for drinking, irrigation, and recreation), 

(2) biodiversity (supporting, for example, birding, hunting, 

and wildlife viewing, (3) carbon sequestration and storage, and 

(4) cultural values (such as spiritual and aesthetic uses). These 

services link to BLM Arizona State priorities such as healthy 

watersheds, intact habitats, habitat stabilization, climate, 

working landscapes, heritage resources, and recreation (fig. 1).

We intend for the results of this study to help BLM’s Arizona 

State, district, and field offices analyze their effectiveness 

at meeting their strategic goals and to identify opportunities 

for improvement.

In this project, we considered ecosystem services 

throughout the San Pedro River watershed, not just BLMmanaged San Pedro Riparian National Conservation Area. 

External stakeholders, therefore, were critical to the success 

of the project. We engaged more than 40 external participants 

from government, academic, and nonprofit organizations. 

The contributions of these participants ranged from help in 

collecting data and running models to project scoping and 

dissemination of results.

Literature Review

The SPRNCA differs from most BLM units in the large 

amount and diversity of background research available to 

support decisionmaking. The watershed has been extensively 

researched in the fields of ecology, hydrology, geomorphology, 

economics and, increasingly, as an area of focus for interdisciplinary and transdisciplinary research. Hydrologic 

research at the Walnut Gulch Experimental watershed began 

in the 1950s (Moran and others, 2008), and work to integrate 

ecology, hydrology, and other fields dates back nearly two 

decades. (See appendix E, an annotated bibliography of 

ecological and economic studies reviewed for this study.)

Ecosystem Services Analyzed

On the basis of stakeholder discussions at the project 

kickoff meeting held in Tucson, Ariz., in January 2010, broad 

categories of ecosystem services of interest were identified for 

the San Pedro River watershed. Participants sought to specify 

concrete economic benefits and beneficiaries relevant to these 

broad classes of ecosystem services, as described below for 

each broad group of services. Ecological “endpoints”—the 
Introduction 5

Table 2. Management issues on the San Pedro River watershed, States of Arizona and Sonora, Mexico, 

as identified by managers and stakeholders.

Management issues

Broad Specific

Water Water quality.

Water quantity.

Surface and groundwater flows from Mexico: quality and quantity.

Biodiversity Nonnative species.

Threatened and endangered species recovery.

Preserving biodiversity.

Cultural Cultural site protection (for example, Murray Springs Clovis site).

Rural character and lifestyle.

American Indian treaty and trust responsibilities.

International border Undocumented immigrants.

Border safety.

Recreation Managing recreational demand.

Hunting and game management.

Multiple-use demand.

Ecological process management Soil conservation.

Grassland preservation and erosion control.

Forage and range provision.

Wildfire management.

Habitat connectivity and corridors.

Growth and change Climate change.

Urban growth.

Preservation of baseline conditions Air quality maintenance or improvement.

Preserving natural soundscapes.

Preservation of night sky conditions.

Preservation of electromagnetic spectrum (Ft. Huachuca).

Aesthetics.

Migration.

Ft. Huachuca’s viability.

Table 3. Potential effects of mesquite management and native grassland restoration on ecosystem services, San Pedro River 

watershed, Arizona.

Ecosystem service Aspect potentially affected

Hydrology For domestic use, agriculture, mining, habitat and recreation.

Bird species Habitat and recreation, nonuse value, migration support.

Forage Beef, game.

Aesthetics Open-space proximity, viewsheds.

Carbon sequestration and storage Changes in carbon pools and uptake rates.

Firewood Quantities for various user groups.

Raw materials Wood for bowl and furniture making.

Recreation Wildlife viewing, maintenance of microclimates, surface water, cultural features, access (trails, roads, 

protected status, wilderness areas).

Trust responsibilities Nonuse value.

Migration support Maintenance of ecosystem services in distant areas based on species migration.
6 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

concrete biophysical attributes related to each ecosystem 

service that can be recognized by the general public as related 

to key ecosystem services (Boyd, 2007; Boyd and Banzhaf, 

2007)—can be further described (table 4). Although no new 

primary valuation research resulted from this study, providing 

the public with estimates of change in these easily understandable endpoints is important for supporting primary valuation 

studies. These endpoints are also usually relevant as data 

inputs to economic models and other social evaluations.

Water

Water within the San Pedro River watershed is used for 

domestic supply, irrigation, and mining, and it also supports 

upland and riparian ecosystems that themselves provide a 

variety of ecosystem services (Stromberg and Tellman, 2009). 

We do not explicitly consider the provision of ecosystem water 

needs as an ecosystem service, because it lacks direct human 

beneficiaries, even though it underpins recreational, cultural, 

and other values.

Drinking water in the San Pedro River watershed is 

entirely drawn from groundwater. While some wells near 

the river draw from the riparian aquifer, particularly in the 

more sparsely settled Lower San Pedro watershed, wells in 

the more extensively developed Upper San Pedro watershed 

largely draw from the regional aquifer (the Upper and Lower 

watersheds are divided by a geologic constriction located 

north of Benson and known as “The Narrows”). Drinking 

water can be valued by using market rates, replacement costs 

for water augmentation alternatives (Bureau of Reclamation, 

2007), or willingness to pay (WTP) for improved water quality 

and quantity (Piper and Martin, 1997). Agricultural water use 

has been declining in recent decades within the San Pedro 

River watershed, and irrigated agriculture was retired with 

establishment of the SPRNCA in 1988 and with The Nature 

Conservancy’s purchases of water rights beginning in 2000 

and continuing at the writing of this report (2012) to support 

in-stream flows on the Lower San Pedro River. The low profitability of alfalfa, the major irrigated crop in the region, also 

contributed to this decline. However, two small agricultural 

U.S. DEPARTMENT OF THE INTERIOR 

BUREAU OF LAND MANAGEMENT Bureau of Land Management

Arizona Strategic Goals

Landscape 

Sustainability 

BLM 

Arizona 

Strategies 

BLM National Priorities 

Land 

Improve Health 

& Productivity 

of the Land 

Conservation Cultivate Community 

Based Conservation 

Employees Respect, Value 

and Support 

Business 

Pursue Excellence in 

Business Practice 

Water Statewide Assessments 

Priority Living Rivers 

Community Engagement 

Evaluation & Adaptation 

Ecological 

Function Statewide Assessments 

Geographic Priority 

Community Engagement 

Evaluation & Adaptation 

Climate 

Working 

Landscapes 

Mitigation and Adaptation Authorizations Assessment 

Statewide Data Integration 

Community Engagement 

Evaluation & Adaptation 

Heritage 

Resources 

Stewardship 

NLCS Tribal 

Relations 

Heritage 

Properties Conservation Values 

Community Relations 

Youth Engagement 

Communication Protocols 

Leadership Relations 

Joint Projects 

Community 

Stewardship 

Community 

Support 

Community Use 

Sustainable 

Energy Community 

Infrastructure 

Recreation 

Partnerships/ 

Volunteers 

Budget 

Effectiveness 

Employee 

Development 

Process 

Improvement 

US– Mexico Border 

Public Safety 

Mission Integration 

Dispersed Recreation 

Geographic Focus 

Travel Management 

Signage 

Cost Effectiveness 

Capacity Improvement 

Out-Year Planning 

Community Support 

Agency Collaboration 

Joint Projects 

Stakeholder Participation 

Community Power Demand 

Four Tracks 

Uranium Analysis 

Employee Development 

Youth Emphasis 

Multi-Year planning 

GIS Capability 

NEPA Best Practices 

Cross-Border Collaboration 

Safe Visitation 

Border Patrol Relations 

Resource Protection 

N:\Jeff\den12_cmrh00_0171_bagstad\report_figures\figure_01.ai

Operational 

Excellence 

Figure 1. Strategic goals of Arizona Bureau of Land Management.
Introduction 

7

Table 4. Ecosystem services endpoints of services to be valued, San Pedro River watershed, States of Arizona (United States) and Sonora (Mexico).

Ecosystem service Beneficiary Endpoint

Groundwater for drinking Residents Depth to groundwater, trends in groundwater depth (stable, increasing, decreasing), concentration of recognized 

pollutants that affect taste and odor or present potential health risks.

Groundwater for irrigation Farmers Depth to groundwater, trends in groundwater depth (stable, increasing, decreasing), concentration of contaminants 

damaging to agriculture.

Groundwater for mining Mining companies Depth to groundwater, trends in groundwater depth (stable, increasing, decreasing).Carbon sequestration and storage Greenhouse-gas emitters Percent tree cover, vegetation type (for example, mesquite, grassland, oak woodland, cottonwood and willow), 

fire frequency.

Biodiversity for birding Birders Diversity and abundance of bird species in riparian corridor and uplands, diversity and abundance of rare species, 

riparian vegetation condition, stream flow permanence, flows from springs, accessibility of open space.

Biodiversity for hunting Hunters Abundance of harvestable species (for example, deer, quail, javelina, dove), riparian vegetation condition, stream 

flow permanence, flows from springs, accessible open space.

Biodiversity for wildlife viewing Wildlife viewers (hikers, equestrians, 

other recreationists)

Diversity and abundance of all taxonomic groups (such as birds, mammals, reptiles, amphibians, butterflies, and 

plants), riparian vegetation condition, stream flow permanence, accessibility of open space, view quality in recreational areas.

Biodiversity: nonuse values Residents of the Southwest and rest 

of the United States

Number of species in riparian corridor and elsewhere, population size and trends for threatened or endangered 

species (stable, increasing, decreasing).

Open-space proximity Homeowners Open space type (grassland, desert scrub, forest, riparian), fire threat, accessibility and area of open space.Scenic viewsheds Homeowners, hikers, motorists Desirable (mountains, riparian, forest, grassland) and undesirable (mines, transmission lines, development) 

features in viewsheds.

Collection of traditional food, 

fiber, medicinal resources

American Indians, Mexicans and 

Mexican-Americans

Accessibility of land for traditional uses, abundance of plants and animals collected for traditional uses (for 

example, mesquite, oaks, agave, medicinal plants).

Spiritual values American Indians, others Accessibility of land for traditional uses, avoidance of culturally damaging features (for example, intensive 

development, mining, dewatering of riparian areas).
8 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

communities downstream of the SPRNCA, St. David and 

Pomerene (fig. 2), maintain seasonal surface water diversions 

for agricultural irrigation. Although major copper mines in 

Bisbee and San Manuel have closed in recent decades, water is 

still used for copper mining in Cananea, Sonora, and upstream 

of the San Pedro’s confluence with the Gila River, where it is 

exported to nearby mining and smelting facilities in Hayden, 

Winkelman, and Ray, just outside the watershed (Katz and 

others, 2009).

Carbon

Carbon that is sequestered (added to soil and vegetation, typically measured on an annual basis as tons of carbon 

per acre per year) or stored (in vegetation and soils in tons of 

carbon per acre) can be valued by using carbon markets or 

estimates of the social cost of carbon (Tol, 2008; Ackerman 

and Stanton, 2012). Given the immature state of carbon 

markets and the fact that market caps are not tied to ecological 

thresholds for climate change, market prices are less appropriate than the social cost of carbon in estimating the value of 

carbon sequestration and storage. Data from past studies of 

carbon sequestration and storage in southwestern ecosystems 

(Schlessinger, 1982; Kaye and others, 2008; Schlessinger and 

others, 2009) were used to parameterize or calibrate carbon 

sequestration and storage models.

Biodiversity

Biodiversity is not in itself typically considered an ecosystem service, as direct anthropocentric value must be derived 

from it in order to qualify as such (for example, the Millennium 

Ecosystem Assessment (2005) considers biodiversity to be 

a supporting service). In the San Pedro River watershed, 

biodiversity supports key recreational activities such as bird 

watching, wildlife viewing, and hunting.

The economic value of birding is well established in 

the San Pedro River watershed (Orr and Colby, 2002; Colby 

and Orr, 2005; Brookshire and others, 2010), so valuation 

can rely on these primary economic studies. Hunting data 

are maintained by the Arizona Game and Fish Department 

(2009). Hunting is relatively limited in the SPRNCA, although 

it is important in other parts of the watershed. Deer, quail, 

javelina, and doves are some of the primary species of interest 

to hunters in the San Pedro area. Wildlife viewing is important for many visitors to the San Pedro area, although no 

specific studies have isolated the effect of wildlife viewing on 

visitor experiences.

Resource management agencies such as BLM also are 

responsible for protecting threatened and endangered species. 

Legal requirements related to the Endangered Species Act are 

commonly cited as evidence of public preferences for protecting threatened and endangered species (Engelmann and 

others, 2004). Spending on recovery is not a direct measure 

of consumer surplus (the price paid for a good subtracted 

from consumers’ WTP for that good, taken as the traditional 

economic measure of utility or benefit derived from consumption of that good), and much of the public views it as a 

cost, rather than as evidence of a social benefit. However, the 

goals and provisions of this act do, in a blunt way, signal the 

American public’s desire for species protection. Public interest, WTP, and actual funding levels for species recovery are 

typically greater for “charismatic” species—particularly large 

mammals and birds—and lower for fish, invertebrates, and 

plants (Richardson and Loomis, 2009).

Cultural Services

The economic value of several cultural services, defined 

by the Millennium Ecosystem Assessment (2005) as “nonmaterial benefits people obtain through spiritual enrichment, 

cognitive development, reflection, recreation, and aesthetic 

experiences,” has been estimated for the San Pedro River 

watershed and nearby regions, providing the basis for direct 

valuation, value transfer, and modeling. Recreational values 

derived from biodiversity—birding, hunting, and wildlife 

viewing—are described in the previous section. Additionally, 

Weber and Berrens (2006) described the value of wilderness recreation in Aravaipa Creek. These values could be 

applied to other wilderness areas in the San Pedro region, 

such as Redfield Canyon (located within the Muleshoe Ranch 

Cooperative Management Area (fig. 3)), using value transfer.

Aesthetic values are typically revealed by increased 

property values of homes and other real estate near open space 

or with access to high-quality views. Given the population 

projections for Cochise County, in which the San Pedro River 

watershed is located, viewsheds and open-space distribution could change markedly depending on whether future 

development patterns are clustered or dispersed (Steinitz and 

others, 2003). Additionally, a proposed SunZia Transmission 

Line Project has the potential to impact aesthetic and other 

ecosystem-service values on the Lower San Pedro River 

watershed. Hedonic studies to estimate aesthetic value have 

been completed for residents near riparian areas in Tucson

(Colby and Wishart, 2002; Bark and others, 2009) and 

central Arizona (Sengupta and Osgood, 2003) but not in the 

San Pedro area. (Hedonic pricing studies isolate the contribution of a particular attribute toward the total value of a good. 

In this case, structural attributes—such as square footage or 

number of bedrooms—and locational attributes—such as 

school district or proximity to green space—contribute to 

housing values, and the individual influence of each attribute 

can be identified).

The San Pedro River watershed holds important spiritual 

and cultural values, particularly for American Indian tribes 

with cultural or historic ties to the watershed. For instance,

Fort Huachuca, an Army base adjacent to the SPRNCA, 

consults with 11 tribes that have connections to the San Pedro 
Introduction 9

San Carlos 

Reservoir

Gila River

Gila River

San Pedro River

Santa Cruz River

Aravaipa Creek

Ashurst-Hayden 

Diversion Dam

"The

Narrows"

MEXICO

UNITED STATES

St. David

Benson

Sierra 

Vista

Bisbee

Ft.

Huachuca

Cananea

ARIZONA

SANTA CRUZ

COUNTY

PIMA

COUNTY

COCHISE

COUNTY

PINAL

COUNTY GRAHAM

COUNTY

GILA

COUNTY

SONORA

0

0 10 40 MILES

10 40 KILOMETERS

Mammoth

Winkelman Hayden

Tucson

San Manuel

Santa Cruz River Watershed

Base from U.S. Geological Survey digital data, 1999, 1:100,000

Universal Transverse Mecator projection, Zone 12N

North American Datum 1983, (NAD 83)

San Pedro River Watershed

19

10

10

8

Map area

EXPLANATION

San Pedro Riparian National Conservation Area

Fort Huachuca Military Reservation

Watershed boundary

111°

33°

32°

31°

110°

ARIZONA

San Pedro River

watershed

Figure 2. San Pedro River watershed study area, States of Arizona (United States) and Sonora (Mexico).
10 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Figure 3. Land ownership within the San Pedro River watershed, States of Arizona (United States) and Sonora 

(Mexico), and Bureau of Land Management ownership in surrounding region.

San Carlos 

Reservoir

Gila River

Gila River

San Pedro River

Aravaipa Creek

SANTA CRUZ

COUNTY

PIMA

COUNTY

COCHISE

COUNTY

PINAL

COUNTY GRAHAM

COUNTY

GILA

COUNTY

Las Cienegas National

Conservation Area

San Pedro National

Conservation Area

Muleshoe Ranch

Cooperative

Mamagement Area

Aravaipa Canyon

Wilderness

MEXICO

UNITED STATES

6

0

24 MILES

18

12

6

0

24 KILOMETERS

18

12

Base from U.S. Geological Survey digital data, 1999, 1:100,000

Universal Transverse Mecator projection, Zone 12N

North American Datum 1983, (NAD 83)

ARIZONA

SONORA

EXPLANATION

Bureau of Land Management

Coronado National Forest

Native American land

Local or state park

Fort Huachuca Military Reservation

National Park Service

Other

Private

State trust land

Ejido—In Mexico, parcels of land 

 communally owned, used primarily 

 for agriculture

San Pedro River Watershed boundary

Map area

111°

33°

32°

31°

110°

N:\Jeff\den12_cmrh00_0171_bagstad\report_figures\figure_03.ai

ARIZONA

San Pedro River

watershed
Introduction 11

River (Marty Tagg, Fort Huachuca, written commun., 2012). 

Ferguson and Colwell-Chanthaphonh (2006) describe cultural 

values for the San Pedro River held by the Tohono O’odham, 

Hopi, Zuni, and Apache. There are serious limitations in 

applying economic valuation and utilitarian assumptions 

(in other words, that value can be derived by knowing how 

well it maximizes utility, which in mainstream economic 

terms has typically been measured in dollar values) to indigenous values (Venn and Quiggin, 2007). These authors argue 

that rights are more central and important than price-based 

measures in understanding the preferences of indigenous 

groups for environmental management. Important cultural 

ecosystem features, whether expressed in monetary terms or 

not, are best considered essential to any planning or evaluation exercise. Although such studies are beyond the scope of 

this project, they are critical in addressing these concerns in 

decisionmaking for BLM.

WTP surveys do not always differentiate “use” values 

(for example, bird watching) and “nonuse” values such as 

existence, option, and bequest values. Nonuse value might 

be expected to be important in the San Pedro River watershed 

region, given its regional, national, and international significance and its designation as one of The Nature Conservancy’s 

“Last Great Places.” However, the San Pedro River is less well 

known outside Arizona than other iconic southwestern natural 

features and protected areas, which might reduce its expected 

nonuse value relative to better-known locations. Nonuse value 

commonly declines with distance (Pate and Loomis, 1997; 

Loomis, 2000; Bateman and others, 2006). Determining this 

rate of distance decay can help to better define the extent of 

groups holding nonuse value for the San Pedro. Ongoing work 

by Brookshire and others (2010) is exploring the spatial extent 

of nonuse values for the San Pedro.

Ecosystem Services Assessment and 

Valuation—Methods and Tools

Selection of appropriate methods for ecosystem services 

assessment and valuation followed identification of relevant 

ecosystem services for the San Pedro River watershed. All 

known ecosystem-service valuation methods and assessment 

tools were reviewed, including those that are qualitative and 

quantitative, spatially explicit and nonspatially explicit, open 

source and proprietary, biophysically and monetarily based, 

and those still in development or not yet adapted for use in 

the semiarid southwestern United States (table 5). Tools have 

been developed by all sectors involved in ecosystem services 

research and policy, including academic institutions, government agencies, nonprofits, and the private sector. Methods and 

tools used to assess and value ecosystem services are rapidly 

evolving. Newly developed methods and tools will also need 

to be evaluated for their usefulness in decisionmaking on 

public lands. As new features are added to existing tools, 

similar reassessment will be needed.

Primary Valuation

Primary valuation data for an area of interest can be 

obtained in one of two ways: by applying past ecosystem 

services valuation studies that have appeared in the peerreviewed or gray literature or, where such studies are lacking, 

by developing new valuation studies. Studies can use stated 

preference (for example, contingent valuation or conjoint 

analysis, using monetary or nonmonetary expressions of 

preferences), revealed preference (for example, travel cost, 

hedonic, or market-based approaches), or avoided or replacement cost approaches. Development of new primary studies 

that use stated or revealed preference is commonly too 

resource intensive for all but the most important resource 

management contexts, although avoided or replacement costs 

may be more feasibly estimated. Thus, application of past 

values at the study site of interest is the most feasible approach 

for widespread use by BLM. Values can be standardized by 

converting them into consistent units (for example, dollars 

per acre per year, or dollars per visitor per day) and adjusting 

for inflation. However, the resources being valued are typically poorly described in the primary study, which can make it 

difficult to standardize values. A strict interpretation of these 

values means that they are best used only in the context of the 

scenario described by the original valuation study.

Application of past primary valuation studies typically 

begins by collecting studies selected from a literature review 

of appropriate databases (for example, Web of Science or 

EconLit (both available only by subscription), Environmental 

Valuation Reference Inventory (Environmental Valuation 

Reference Inventory, 2011)) or discussion with academic 

personnel or agencies familiar with the area of interest. A 

close review of each study and some basic conversions may 

be needed to standardize units. Primary valuation provides 

rigorous, locally appropriate estimates of nonmarket economic 

value. Primary valuation results may be relevant for most 

BLM offices, and assembled valuation results may be used 

for value transfer, as described next.

Value Transfer

Given the time and expense associated with primary 

valuation, value transfer has grown in popularity as a means of 

obtaining values when development of new primary studies is 

not feasible or the policy site has not been studied (Brookshire 

and Neill, 1992; Wilson and Hoehn, 2006). Value transfer uses 

economic values previously estimated at another study site 

and applies them to the site of interest. Several authors have 

described the criteria needed for sound value transfer, such 

as basic equivalence of the population, institutional setting, 

environmental resource, and constructed market (for example, 

mail compared with phone surveys or proposed one-time or 

annual payments) (Boyle and Bergstrom, 1992; Desvousges 

and others, 1992; Brouwer, 2000; Spash and Vatn, 2006; 

Plummer, 2009).
12 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona Table 5. Ecosystem service tools described in this report, San Pedro River watershed, Arizona. [BLM, Bureau of Land Management; BSR, Business for Social Responsibility; GIS, geographic information system] Tool Brief description Tested in this study? Rationale for testing or not testing Application of past primary valuation studies Application of valuation data directly from past primary valuation studies, when available for the study site Yes Relevant studies have been already been conducted for the San Pedro. Point transfer Value transfer from policy site to study site without accounting for ecological and socioeconomic context No Primary valuation studies for the San Pedro could be used with greater accuracy. Function transfer (multiple regression) Value transfer from policy site to study site that accounts for ecological and socioeconomic context via classical multiple regression Yes Relevant for valuation of water supply; in all other accounts primary valuation studies for the San Pedro could be used. Function transfer (Bayesian) Value transfer from policy site to study site that accounts for ecological and socioeconomic context by use of Bayesian multiple regression No Relatively new method requiring sophisticated statistical expertise to develop transfer functions. Function transfer (Benefits Estimation Toolkit) Publicly available spreadsheets that use function transfer to value changes in ecosystem services Yes A well-documented valuation approach that can be independently applied, tested, and potentially used throughout BLM. InVEST Open-source ecosystem service mapping and valuation models run using ArcGIS Yes A well-documented modeling approach that can be independently applied, tested, and potentially used throughout BLM. ARIES Open-source modeling framework to map ecosystem service flows; web tool under development Yes Currently capable of running models in Arizona, California, Colorado, Washington; global model and web tool under development would enable application throughout BLM. MIMES Open-source dynamic modeling system for mapping and valuing ecosystem services No Requires commercial modeling software; model construction currently requires contracting with development group. EcoMetrix Proprietary tool for measuring ecosystem services at site scales using field surveys BSR, 2011 Proprietary method; demonstrated for the San Pedro as part of parallel BSR tool comparison workshop. EcoAIM Proprietary tool for mapping ecosystem services and stakeholder preferences BSR, 2011 Proprietary method; demonstrated for the San Pedro as part of parallel BSR tool comparison workshop. ESValue Proprietary tool for mapping stakeholder preferences for ecosystem services using focus groups BSR, 2011 Proprietary method; demonstrated for the San Pedro as part of parallel BSR tool comparison workshop. NAIS Proprietary valuation database paired with GIS mapping of land-cover types for point transfer No Proprietary method; limited primary valuation studies to support application to Southeast Arizona. SERVES Valuation database paired with GIS mapping of land-cover types for point transfer No Tool not fully developed by the time of the pilot study. SolVES ArcGIS toolbar for mapping social values for ecosystem services based on survey data or value transfer No No primary survey data available; conditions in southeast Arizona likely different from conditions in Colorado (source of existing data); unable to support value transfer. Envision Modeling approach for evaluating impacts of alternative futures on landscape metrics including ecosystem services No Has not yet been applied in the Southwest; not feasible to run without a substantial research effort. Ecosystem Portfolio Model Web-accessible tool to model economic, environmental, and quality of life impacts of alternative land-use choices No Being applied for adjacent Santa Cruz River but still under development. EcoServ Web-accessible tool to model ecosystem services No Still in development; initial case studies unavailable for Southwest. InFOREST Web-accessible tool to quantify ecosystem services in Virginia No Developed only for Virginia. ESR Publicly available, spreadsheet-based process to qualitatively assess effects of ecosystem services BSR, 2011 Directed at corporate effects but feasible for use by the Bureau of Land Management. UNEP–WCMC Ecosystem Services Toolkit Publicly available process to qualitatively assess ecosystem services, effects, and beneficiaries No Tool not yet publicly available in early 2012 at the time of the pilot study.
Introduction 13

There is clear danger in transferring values between 

study and policy sites without considering the similarity of 

ecological and socioeconomic contextual factors. Function 

transfer, as opposed to point transfer, is designed to reduce 

these potential misapplications by applying a mathematical 

function to account for differences between study and policy 

sites (Loomis, 1992). The function is developed through metaanalysis of multiple studies. Function transfer is preferable to 

point transfer, in which values from a study site are typically 

transferred to a policy site without adjustment. Yet to date, 

function transfer has been used relatively infrequently. The 

lack of primary valuation studies, which leads to a shortage 

of quality meta-analyses, has limited opportunities to use 

function transfer for most ecosystem types.

Point Transfer

In point transfer, land-use–land-cover data have typically 

been used to link values from study site to policy site (Troy 

and Wilson, 2006). In point transfer, studies are assembled by 

land-use–land-cover type and the area and value of ecosystem 

services in question are estimated. Although analyses 

typically multiply land-cover type times per-area value and 

sum to obtain a total value, this approach is inconsistent with 

marginal economic valuation, which underpins the valuation 

studies upon which point transfer is built (Bockstael and 

others, 2000). When valuation is conducted, it should be for 

a change in a particular resource as opposed to the summed 

value of all land-cover types. For example, the statement 

that “a 500-acre mesquite management–grassland restoration 

project will provide x thousand dollars per year in carbon 

sequestration, water supply, and recreational benefits,” is both 

more correct economically and more useful for management 

than the statement that “ecosystems in the San Pedro River 

watershed provide y billion dollars per year in benefits.”

Point transfer has typically begun with development of 

a land-use–land-cover typology for the area of interest and 

assembly of a database of primary valuation studies within an 

area defined as acceptably similar to the study site. The landuse–land-cover types for the study site are matched to primary 

valuation studies and summarized by type, and then values 

are totaled for the area of interest (Troy and Wilson, 2006). 

Results may, for example, summarize high, median, and low 

values for ecosystem services provided by land-cover types 

at a policy site. Potentially useful valuation databases include 

the Environmental Valuation Reference Inventory, a publicly 

accessible environmental valuation database (Environmental 

Valuation Reference Inventory, 2011), the Marine Ecosystem 

Services Partnership database (Marine Ecosystem Services 

Partnership, 2011), the Natural Assets Information System 

(Troy and Wilson, 2006), a proprietary database developed by 

the Spatial Informatics Group, and the Simple and Effective 

Resource for Valuing Ecosystem Services (SERVES) database 

(http://esvaluation.org/) developed by Earth Economics.

Point transfer is the simplest form of value transfer. Its 

application can be quite rapid given good land-use–land-cover 

data and a well-developed database of primary valuation 

studies. Although most advocates of point transfer recognize 

its shortcomings, they usually suggest that it can provide 

rough but useful “first cut” ecosystem service values given 

limited time and resources (Troy and Wilson, 2006). However, 

the weaknesses of point transfer are well documented: if differences between the study and policy site’s ecological and 

socioeconomic setting and constructed market characteristics 

are not accounted for, transfer error can be quite high (Boyle 

and Bergstrom, 1992; Desvousges and others, 1992; Brouwer, 

2000; Spash and Vatn, 2006; Plummer, 2009). Owing to 

these concerns, point transfers have been criticized by most 

economists, despite their popularity among practitioners. 

Thus, although point transfer is a low-cost option for valuation 

among BLM offices, it also has potentially low accuracy and 

utility for decisionmaking.

Function Transfer

In contrast to point transfer, an analyst using function 

transfer develops and applies a mathematical function to 

account for differences in resource characteristics, geographic setting, and the constructed market when comparing 

values between sites (Loomis, 1992). Multiple regression is 

typically used to estimate the relative contribution of several 

independent variables describing the site and study context 

in order to determine their individual influence on ecosystem 

service value, the dependent variable. For example, the annual 

per-acre value of wetlands might be empirically determined 

to depend on wetland acreage, wetland type, human population density, and per capita income in the area of interest. By 

supplying values for the independent variables, the analyst 

can determine the per-acre value of wetlands at the policy 

site in question. Bayesian approaches to developing transfer 

functions are a more sophisticated approach than classical 

multiple regression and offer the added benefits of accounting for the “n vs. k” problem in function transfer and handling 

the effects of methodological independent variables when 

using a transfer function (Moeltner and others, 2007; Moeltner 

and Woodward, 2009). The “n” vs. “k” problem refers to the 

dilemma where, owing to poor description of resource and 

study characteristics in primary valuation studies, the analyst uses either a small number of well-documented studies 

including all variables of interest (low n, high k) or a full set 

of studies but only evaluating the influences of a few common 

variables (high n, low k). The choice of a low k increases 

the risk of omitted variable bias in the regression model. 

Bayesian statistics differs from classical statistics principally in its use of past experiences to derive an expected 

probability distribution. Information from past studies 

and expert opinion are used to establish a prior distribution (for example, our belief of how observations such as 

WTP are distributed. The predictive distribution gives the 
14 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

probability of observing data that do not correspond with 

the prior distribution. As new information becomes available, the distribution may be updated to reflect changes. 

This updated set then becomes the posterior distribution 

(Atkinson and others, 1992; Morrison and Bergland, 2006; 

Moeltner and others, 2007).

Where transfer functions already exist, function transfer

requires the analyst to collect data on relevant independent 

variables for the study site and to insert these values into the 

transfer function to obtain values for the study site. If transfer 

functions do not exist, they need to be developed, if enough 

studies are available that use the same dependent variable, 

supporting a valid statistical analysis. Relevant primary studies 

on a common ecosystem or ecosystem service are collected, 

and values for all independent variables and the dependent 

variable are documented for each study. The analyst then runs 

regression models, drawing on economic theory to account for 

relevant determinants of supply and demand for the ecosystem services of interest. Lastly, appropriate diagnostics are 

run to ensure that classical regression assumptions are met, to 

suggest appropriate transformations or model respecifications 

to be used as needed, and to guide selection of one or more 

appropriate models.

In Bayesian analysis, by contrast, the goal is to estimate 

a probability distribution of values for the coefficient of each 

independent variable. This probability distribution is derived 

from a set of expert-defined prior probabilities. Prior probabilities are then run through an algorithm that collects random 

samples from a probability distribution, in order to estimate a 

set of final, or posterior, probability distributions. The means 

and standard deviations of these posterior distributions are 

then used as coefficients in a function transfer model.

Meta-analyses and regression equations are well developed for several ecosystem services (for example, recreation 

(Smith and Kaoru, 1990; Walsh and others, 1992; Rosenberger 

and Loomis, 2001) and water quality (van Houtven and others, 

2007)) and ecosystem types (for example, forests (Barrio and 

Loureiro, 2010), wetlands (Woodward and Wui, 2001; Brouwer 

and others, 2003; Brander and others, 2006; Ghermandi and 

others, 2008), and coastal and marine ecosystems (Brander and 

others, 2007)). When transfer functions are available, it is relatively easy to transfer values with greater confidence than that 

provided by point transfer, while theoretically reducing transfer error. This ease makes function transfer a relatively robust 

and efficient way for BLM district or field offices to obtain 

ecosystem service values.

However, transfer functions are not available for many 

ecosystem services and ecosystem types outside those already 

mentioned. The typical cause of a lack of transfer functions is 

that there are too few primary valuation studies upon which 

researchers can draw to develop transfer functions. New transfer functions are also relatively time consuming to develop. 

Bayesian analysis, while more powerful, requires additional 

time and expertise to run, which must be weighed against the 

benefit of potentially more rigorous transfer functions. Finally, 

sharing of databases built to construct new transfer functions 

is uncommon. These databases are typically built by academics who use them to conduct their own research, so there may 

be little incentive to share or collaborate, and the effort needed 

to build a new database is a barrier to scientists interested 

in entering the field. Although public databases are one way 

around this problem, such databases need to be designed 

to support meta-analysis, and few valuation databases (as 

is true for many other ecosystem-based management tools) 

have received funding for ongoing maintenance (McComb 

and others, 2006, Curtice and others, 2012). For example, the 

Ecosystem Services Database was last updated in 2002, and 

the Envalue database was last updated in 2004.

Function Transfer Using Defenders of Wildlife’s 

Wildlife Habitat Benefits Estimation Toolkit

The Wildlife Habitat Benefits Estimation Toolkit is a 

set of spreadsheets that incorporates transfer functions based 

on literature for recreation, hedonic property premiums, and 

WTP for threatened and endangered species recovery (Loomis 

and others, 2008). It is also available through Colorado 

State University’s Department of Agricultural and Resource 

Economics as the Benefit Transfer and Use Estimating 

Model Toolkit. These transfer functions were developed by 

using economic studies from throughout the United States, 

with the goal of supporting faster and more systematic 

function transfers.

The Toolkit requires users to choose and apply spreadsheets for each ecosystem service of interest. The user enters 

values for the required independent variables for a given 

transfer function (for example, species to value, type of water 

body, open-space characteristics, and wetland type, area, 

and region). The spreadsheet calculates economic value per 

household or recreation day, on the basis of transfer functions 

contained in the spreadsheet. The user can propose scenarios 

for management changes to quantify changes in economic 

value or sum economic values of different services.

The Toolkit is a well-documented package of metaregression analyses that can support more comprehensive and 

systematic function transfer than an analyst who assembles 

and uses transfer functions independently. However, its 

usefulness is limited to cases where transfer functions have 

already been developed—for the ecosystem services related to 

recreation, hedonic property premiums, and WTP for threatened and endangered species recovery. Periodic updates would 

be required to account for new transfer functions and primary 

valuation studies to inform them, although with a large number of existing studies in the database, transfer functions are 

likely to be relatively stable despite the addition of new data 

points, suggesting that updating could take place on a roughly 

5-year cycle (Timm Kroeger, The Nature Conservancy, oral 

commun., 2010).
Introduction 15

Integrated Valuation of Ecosystem Services 

and Tradeoffs (InVEST)

Developed by The Natural Capital Project, a collaboration of several academic and conservation nongovernmental 

organizations, InVEST is a set of ecosystem service models 

designed to map and value tradeoffs between multiple 

ecosystem services (Tallis and others, 2011). It has multiple 

releases or “tiers”: the tier 1 tools available in early 2012 

consisted of an ArcGIS toolbox interface for individual 

ecosystem service models; more detailed tier 2 tools that 

have been described (Kareiva and others, 2011) as of early 

2012 had not yet been released. Tier 2 tools use more dataintense external ecological process models. Integration of tier 

3 models—existing external ecological process models—is 

planned for future releases of InVEST. The marine InVEST 

models also include simple tier 0 models that map relative 

supply of or demand for ecosystem services in relative rather 

than biophysical units, with minimal underlying modeling and 

valuation. Economic valuation in InVEST can take place in 

biophysical units or by means of prices supplied as input by 

the user (for example, social cost of carbon, market price for 

timber, or avoided cost of sediment or nutrient removal).

InVEST is a well-documented method for mapping 

ecosystem service provision and comparing tradeoffs under 

land-use–land-cover change scenarios. In early 2012, InVEST 

included models for carbon sequestration, crop pollination, 

managed timber production, reservoir hydropower production, nutrient retention, sediment retention for reservoir 

maintenance, wave energy, coastal vulnerability, aquaculture, 

aesthetic quality, and fisheries and recreation overlap. It also 

includes biodiversity and habitat risk assessment models so 

that tradeoffs between biodiversity, habitat risk, and ecosystem 

services can be analyzed. Future releases of InVEST include 

planned models for flood mitigation, agriculture production, 

irrigation, open access harvest, commercial and recreational 

fisheries, recreation, and marine water quality, and carbon 

storage. Beta versions of InVEST have been in existence 

since summer 2008 and are reviewed through an open-source 

community, so they are likely at a more advanced and reliable 

stage than other modeling systems. Running on industry 

standard Geographic Information System (GIS) software 

(ArcGIS 9/10) and as a well-developed tool, InVEST in early 

2012 had potential for widespread use by BLM in decisionmaking, assuming adequate data are available to support 

modeling efforts.

Artificial Intelligence for Ecosystem 

Services (ARIES)

ARIES is a modeling framework developed by the 

University of Vermont’s Ecoinformatics Collaboratory at the 

Gund Institute for Ecological Economics, in partnership with 

several conservation NGO partners. ARIES seeks to address 

several scientific and modeling challenges in ecosystem 

services research and application, such as (1) the need to 

purchase and be proficient in commercial modeling or GIS 

software, (2) the difficulty of using existing ecological 

process models to generate outputs that are relevant for ecosystem services quantification, (3) the inability of past models 

to represent spatial dynamics of ecosystem services (that is, 

between points of provision and use, and spatial flows between 

them), and (4) lack of clear expression of uncertainty in past 

modeling efforts (Villa and others, 2011).

ARIES is accessible by means of an internet browser, 

and it provides users with access to a library of ecosystem 

service models and spatial datasets at global through local 

scales. Like InVEST tiers 2 and 3 models, ARIES plans 

integration of existing ecological process models in future 

releases of the system. In its early 2012 release, ARIES 

used a combination of Bayesian network models, deterministic models, and spatial data to quantify and map source 

locations that provide ecosystem services, use locations 

of human beneficiaries, and sink locations (the landscape 

features that deplete ecosystem service flows). A series 

of agent-based models, termed Service Path Attribution 

Networks, account for the full dynamics of ecosystem 

service flows (Johnson and others, 2012). ARIES encodes a 

set of artificial-intelligence-based decision rules that enable 

particular model components to be added or removed under 

appropriate circumstances (for example, to include different model influences under certain climatic regimes, above 

specified population or income thresholds, or for specific 

biomes). Once generalized global models supported by 

decision rules have been developed and deployed, ARIES 

will provide both low-cost first estimates of ecosystem service 

values in parts of the world where detailed local models have 

not yet been developed and also local models that are sensitive to regionally specific factors and make use of local data. 

ARIES handles uncertainty by means of Bayesian network 

modeling and Monte Carlo simulation (Villa and others, 2011; 

Bagstad and others, 2011; Johnson and others, 2012; Monte 

Carlo methods are a group of algorithms that use repeated 

random sampling to generate results that are expressed as a 

probability distribution).

ARIES models are open source. However, artificial 

intelligence, Bayesian modeling, and spatial flow modeling 

of ecosystem services are unfamiliar to many researchers 

and practitioners, and they require more sophisticated 

communication of results and modeling approaches. In early 

2012, use of ARIES by BLM was limited to four study regions 

in the Western United States (southern Arizona, southern 

California, Colorado, western Washington), though release 

of global models could make its use more widely feasible 

for BLM.
16 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Multiscale Integrated Models of Ecosystem 

Services (MIMES) 

MIMES is a system dynamics model initially developed 

by the University of Vermont’s Gund Institute for Ecological 

Economics and since 2010 managed by AFORDable Futures, 

LLC (http://www.afordablefutures.com/). It is used to map and 

value ecosystem services based on interrelationships between 

natural and socioeconomic systems (Multiscale Integrated 

Models of Ecosystem Services, 2011). It is designed to 

account for temporal dynamics and feedback loops, incorporate existing ecological process models into ecosystem service 

modeling, and economically value ecosystem services by 

means of input-output analysis. This model uses the Simile 

modeling platform, a commercial software modeling package.

Running MIMES requires the user to download the latest 

MIMES models, prepare needed spatial data for the region and 

ecosystem services of interest, and develop, run, and calibrate 

the models. Because the models are open source, they can be 

accessed and used by anyone. However, many segments of this 

model are still in development, and local applications require 

it to be adapted to local conditions. Supporting documentation 

for the models and modeling process is currently minimal. As a 

result, a highly capable systems modeler could use MIMES, but 

in nearly all other cases, consultants would need to be hired to 

run the models and train BLM staff in its use.

EcoMetrix

EcoMetrix assesses ecosystem services for individual 

parcels of land on the basis of field measurements (Parametrix, 

2010). It is a proprietary, spreadsheet-based approach 

developed by Parametrix, a consulting firm. EcoMetrix has 

been developed and parameterized for the Pacific Northwest 

and southwestern United States. It uses rapid field-based 

site evaluations to measure ecosystem services by linking 

observable field conditions to inputs in ecological production functions for ecosystem services of interest (ecological 

production functions specify relationships among the physical 

environment and ecosystem services along with relationships 

between key ecosystem services and human-induced environmental stressors—for example, human population growth 

affects available freshwater quantity, which affects other 

ecosystem services). Its primary use has been to estimate the 

generation of environmental credits for market-based trading 

by using field measurements for restoration or degradation 

scenarios. Because it is a proprietary method, Parametrix 

requires a contract for each application of EcoMetrix.

EcoAIM

EcoAIM is an ecosystem services mapping tool developed by Exponent, a consulting firm. EcoAIM is designed 

“to (1) inventory ecological services and help in making 

decisions regarding development, transactions, and ecological restoration; (2) develop specific estimates of ecosystem 

services in a geographically relevant context; and (3) offer the 

means for evaluating tradeoffs of ecosystem services resulting 

from different land or resource management decisions” (BSR, 

2011). EcoAIM uses a series of publicly available, projectspecific spatial datasets combined with a weighting or aggregation function to derive a score for a particular ecosystem 

service of interest. EcoAIM can also integrate stakeholder 

preferences in considering ecosystem service impacts, using 

a modified risk analysis approach. Because it is a proprietary 

method, Exponent requires a separate contract for each application of EcoAIM.

ESValue

ESValue is an ecosystem services mapping and valuation tool developed by Entrix, a consulting firm. ESValue 

combines expert, site-specific, and literature-derived data to 

develop ecosystem services production functions (BSR, 2011). 

On the basis of available science and stakeholder preferences, 

ESValue specifies the relative values that society, managers, 

and stakeholders place on ecosystem services, as developed 

during a stakeholder engagement process. The ESValue tool 

thus facilitates the comparison of what can be produced (that 

is, production function) with what participants want to be 

produced (that is, the valuation function) to help determine 

the most appropriate natural resource management strategy. 

Because it is a proprietary method, Entrix requires a separate 

contract for each application of ESValue.

Natural Assets Information System (NAIS) 

and Simple, Effective Resource for Valuing 

Ecosystem Services (SERVES)

NAIS and SERVES are valuation databases that grew 

out of the Ecosystem Services Database initially developed 

by the University of Vermont’s Gund Institute for Ecological 

Economics (Villa and others, 2002). Both combine an 

underlying valuation database with GIS analysis of land 

cover, which can be used for economic valuation using point 

transfer (Troy and Wilson, 2006). Analysis using these two 

approaches typically entails (1) identifying a study area, 

(2) developing a locally relevant land-use–land-cover typology 

and corresponding GIS layer to match with economic values, 

(3) searching the literature to select locally relevant studies 

for value transfer, linked to and-use–land-cover classes 

selected in step 2, (4) mapping, value calculation, and geographic summaries at the appropriate scales, and (5) scenario 

analysis (optional). NAIS was developed by the consulting 

firm Spatial Informatics Group, which requires a separate 

contract for each application of the database. SERVES, which 

is under development by the nongovernmental organization 
Introduction 17

Earth Economics, is being designed as a subscription-based 

service. Earth Economics can be contacted for information 

about database access.

Social Values for Ecosystem Services (SolVES)

SolVES was developed by the USGS in partnership with 

Colorado State University as a public domain GIS application accessible as an ArcGIS toolbar (Sherrouse and others, 

2011). It is available for download at http://solves.cr.usgs.gov. 

This tool is intended to quantify and map the perceived social 

values of ecosystem services for various stakeholder groups; it 

includes aesthetic and recreation values and resource extraction alternatives through the derivation of a quantitative 

“Value Index” metric. The 10-point Value Index is calculated from a combination of spatial and nonspatial responses 

to public attitude and preference surveys. This model also 

calculates landscape metrics from spatial data describing 

the underlying physical environment (for example, average 

elevation and distance to roads and water at locations along 

the Value Index). The tool uses MaxEnt maximum entropy 

modeling software (Elith and others, 2011) to produce Value 

Index maps and to report landscape metrics associated with 

each index value. Values modeling can be repeated for various 

survey subgroups that are distinguished by their attitudes 

and preferences regarding ecosystem uses such as motorized 

recreation or oil and gas drilling. The Value Index provides 

a basis of comparison within and among survey subgroups 

to illustrate the effect of social context on the valuation of 

ecosystem services. The Value Index also facilitates statistical 

analysis of relationships between index values and landscape 

metrics. By using a form of value-transfer methodology, 

SolVES can be used to apply regression coefficients produced 

by such analyses to their corresponding landscape data layers 

and to produce predicted social-value maps for similar areas 

where primary survey data are not available or as a complement to existing survey data. Multiple applications have been 

completed in national forests in the Rocky Mountain region. 

Additional applications for several coastal and marine environments in the United States and elsewhere are also underway. These applications will enable further testing of the tool’s 

value transfer approach.

Envision

Envision (http://envision.bioe.orst.edu/), previously 

known as “Evoland,” is a “spatially explicit, actor (agent)-

based approach to landscape change and alternative futures 

analysis” (Bolte and others, 2006). Envision is a GIS-based 

planning tool designed to assist in evaluating how alternative urban development scenarios affect a series of landscape 

metrics. It incorporates policy choices by a set of actors, 

quantifies the effects of those choices on land-use–landcover change, and couples ecological and economic process 

models to map effects on landscape metrics. These effects 

are summarized as scenarios; a series of submodels are then 

run that account for social preferences for economic development, landscape metrics, land value, and population growth, 

in order to link spatial data with sets of policies that achieve 

certain mixes of economic and environmental goals (Guzy 

and others, 2008).

In recent applications of Envision, landscape metrics 

have increasingly included ecosystem services such as nutrient 

regulation, water provisioning, carbon sequestration, food 

and fiber production, shoreline protection, and pollination, 

and valuation typically uses market prices, avoided-cost, or 

replacement-cost methods. Envision has been applied principally in the United States Pacific Northwest, athough in 2012 

international applications in Colombia and New Zealand were 

under development. Whereas the methods to develop new 

case studies for Envision are systematic and well documented, 

each application of Envision is place specific, meaning that 

transferability is low and new studies must be conducted for 

areas where Envision has not been previously applied. Recent 

applications of Envision have required approximately 1 year 

and $100,000–150,000 to complete (given the variability 

inherent in the size and complexity of each study, most other 

tool developers were reluctant to provide estimates of the 

effort needed to conduct new case studies).

Ecosystem Portfolio Model (EPM)

The EPM is designed as a place-specific land-use planning tool to model ecological, economic, and quality-of-life 

values; it offers insight into the effects of land-use change 

(such as development, conservation, and restoration choices) 

on these values (Labiosa and others, 2009). For some easily 

monetized criteria such as the hedonic value of open space, 

results are monetized; for criteria that are more difficult 

to value monetarily, such as biodiversity, alternative user 

preferences can be compared by using a multiattribute utility 

approach. Each application is tailored to locally important 

ecological, economic, and quality of life issues. The model 

was initially developed for Miami-Dade County, Florida, and 

it focused on land-use and water-management issues, particularly in the context of development and maintenance of 

the ecological integrity of Biscayne and Everglades National 

Parks. Development of the EPM was underway in 2012 for 

two additional sites—Puget Sound, Washington and the Santa 

Cruz River, Arizona (Norman and others, 2010).

The EPM is a stand-alone, web-based tool. To run it, 

the user accesses the website (http://lcat.usgs.gov/sflorida/

sflorida.html for South Florida), chooses the area of interest, 

selects weights for valuation of each criteria, and views and 

compares results in the online viewer. The tool is well tailored 

to model environmental, economic, and quality-of-life issues 

for areas where it has been fully developed. Although its application on the Santa Cruz River, Arizona, could have informed 

the ecosystem service valuation and modeling work for this 
18 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

project on the San Pedro River watershed, it was at too early a 

stage of development to inform this study. Recent applications 

of the model to new regions has entailed a multiyear research 

and modeling effort. Thus, it is unlikely that its development 

and use by BLM will be feasible for regions where it has not 

previously been used. However, in regions where the model 

has already been developed, its application by BLM would be 

quite feasible.

EcoServ

EcoServ is an open-source, web-based ecosystem-service 

modeling and mapping tool under development by the USGS 

and the Chinese Academy of Sciences (Feng and others, 

2011). The initial model was developed in the Prairie Pothole 

region of the north-central United States and the Canadian 

prairies, with additional planned case study sites in Mississippi 

and California’s Central Valley. EcoServ’s developers intend 

to eventually develop generalized national or global models 

that complement the EcoServ case studies. EcoServ links 

external ecosystem process models and data and will make 

them accessible to the public by means of a web tool. It is 

temporally dynamic and supports evaluation of scenarios for 

climate and land-use change. EcoServ does not explicitly 

use production functions in modeling ecosystem services; 

instead, it relies on a series of external models to proxy a 

service of interest. EcoServ does not put an monetary value on 

ecosystem services, although model outputs could be used in 

external valuation efforts. As of early 2012, EcoServ was in 

the development phase. However, once regional and national 

models are available on the web, it could be feasible for BLM 

offices to use EcoServ if it had been developed for their area 

of interest.

Investing in Forests (InFOREST)

The InFOREST model (http://inforest.frec.vt.edu/) is 

designed as a spatially explicit, web-based assessment tool for 

carbon, watershed nutrient and sediment loading, and biodiversity. The user enters the online interface, chooses the area 

of interest, and (if desired) enters land-cover and agricultural 

practices information. InFOREST is designed as an ecosystem 

service credit calculator; thus, it does not include economic 

valuation as a goal. It incorporates a series of existing carbon 

and hydrologic models and habitat metrics. InFOREST is 

under development at Virginia Tech University. Its spatial 

extent in early 2012 was only the state of Virginia, limiting 

its applicability for BLM, though its developers may eventually adapt it for other parts of the United States. InFOREST’s hydrologic models are unlikely to be easily generalized to other parts of the United States, although its carbon 

and biodiversity models may be more easily transferred to 

other regions. If InFOREST is eventually expanded to a tool 

capable of covering Western United States ecosystems and 

ecological and hydrologic processes, it could be a useful tool 

for BLM.

Ecosystem Services Review (ESR)

The Ecosystem Services Review (World Resources 

Institute, 2012) is designed to identify ecosystem-servicesrelated resource and waste-sink dependencies and potential 

economic opportunities and liabilities for corporations. The 

ESR uses a spreadsheet (http://www.wri.org/publication/

corporate-ecosystem-services-review) that systematically 

identifies business dependencies, risks, and opportunities 

related to ecosystem services. However, it does not quantify, 

map, model, or value ecosystem services.

ESR is a free and well-documented approach to qualitatively evaluating ecosystem services from a business perspective. Although it was designed for corporate users, it could 

easily be used by BLM to identify ecosystem-service impacts 

associated with resource extraction tradeoffs. It is a relatively 

low-cost option for qualitative ecosystem services analysis 

that could be easily used by BLM.

United Nations Environment Programme–World 

Conservation Monitoring Centre Ecosystem 

Services Toolkit

This ecosystem services toolkit developed by the United 

Nations Environmental Programme’s World Conservation 

Monitoring Centre (UNEP-WCMC) is a structured process for 

identifying stakeholders and the effects of ecosystem services 

at the site scale (United Nations Environmental Programme’s 

World Conservation Monitoring Centre, 2011). It prompts 

users to identify ecosystem services, their beneficiaries, and 

impacts caused by alternative resource-management scenarios. 

Like the Ecosystem Services Review, this toolkit does not 

involve quantification, spatially explicit mapping, modeling, 

or valuation of ecosystem services, and its primary use 

would be as a low-cost framework for identifying ecosystem 

services of interest and impacts associated with resourcemanagement tradeoffs.

Results of This Study

This report quantifies and values four groups of ecosystem services identified by stakeholders and scientists as 

important to the San Pedro: those derived from water, carbon, 

biodiversity, and cultural values (such as recreational and 

aesthetic values). We used the valuation tools and methods 

described below.

Application of past primary valuation studies: Market 

and replacement cost values for water and social costs for carbon are summarized. Past nonmarket valuation studies could 

be used to account for some recreational and nonuse values on 

the San Pedro River watershed.

Value transfer: Where possible, value transfer was 

used to assess aesthetic value, water supply, and nonuse and 

recreational values not covered by primary valuation. Value 
Methods 19

transfer approaches include point transfer and function transfer 

where appropriate, and Defenders of Wildlife’s Wildlife 

Habitat Benefits Estimation Toolkit.

InVEST model: InVEST models were used for carbon, 

water yield and demand, biodiversity, and aesthetic viewsheds.

ARIES model: ARIES models were used for carbon, 

water, aesthetic viewsheds and open-space proximity, and 

recreational values.

The testing of additional tools—MIMES, EPM, SolVES, 

SERVES, Ecosystem Services Toolkit, and EcoServ—was 

beyond the scope of this project, because the tools were either 

still in development or were not able to be run within the pilot 

study’s time frame. This report briefly describes these tools to 

offer guidance on when and where they could be most useful 

for BLM.

During the course of this pilot study, a parallel comparative ecosystem services project was undertaken by BSR 

(formerly Business for Social Responsibility) in the context of 

corporate ecosystem service decisionmaking (see subsequent 

section Parallel Efforts). That effort assessed several additional 

tools, including the ESR, EcoMetrix (applied by Parametrix), 

EcoAim (applied by Exponent), ESValue (applied by Entrix), 

and (NAIS, applied by Spatial Informatics Group).

Parallel Efforts

A range of recent efforts is attempting to synthesize work 

on ecosystem services tools and approaches. Open communication is thus important so that researchers can learn from 

each other and minimize replication of such syntheses. Given 

the lag between research and publication in the peer-reviewed 

literature, and the fact that some of these reviews are internal 

documents, not all can be cited; Nelson and Daily (2010) and 

Vigerstol and Aukema (2011) are two published examples.

There is growing research interest in ecosystem 

services in the Southwest for the San Pedro and beyond. The 

U.S. Environmental Protection Agency’s Office of Research 

and Development (US EPA–ORD) has undertaken placebased ecosystem services research in the Southwest United 

States and has applied the Ecosystem Portfolio Model to 

the Santa Cruz watershed, Ariz. The work in the Santa Cruz 

watershed is relevant to the San Pedro project owing to their 

similar ecological and social contexts. The Santa Cruz project 

also includes efforts to better understand how ecosystems and 

their services are valued by American Indian populations, 

through surveys of the San Xavier Tohono O’odham. In addition, the project team has collaborated with the Assessment of 

Goods and Ecosystem Services (AGAVES) group. This group 

is an interagency research partnership that builds on past ecosystem research in the Southwest. Its members include agency 

and academic scientists with expertise in hydrology, ecology, 

economics, and decision-support tools.

In a parallel effort, BSR undertook an evaluation of 

ecosystem services tools for corporate decisionmaking, and 

it also used the San Pedro for its comparison. BSR invited 

tool developers to a roundtable meeting in Phoenix, Ariz. 

in October 2010, where participants compared results for 

these tools from analysis of a set of hypothetical housing 

development scenarios for the San Pedro River watershed 

(BSR, 2011). Developers of the ARIES, InVEST, EcoMetrix, 

EcoAIM, and ESValue tools participated, along with others 

from the public and private sector.

Methods

Selection and Description of the Study Area

BLM and USGS set several criteria for the pilot study 

site. Because the goal of the pilot study entailed both monetary and nonmonetary valuation of ecosystem services, the 

study site needed extensive research that characterized its 

ecology in a landscape framework, including the effects of 

human uses, so that such information could be used to support 

quantification and valuation of ecosystem services. The ideal 

site also needed to have urban growth as a stressor, multiple 

management issues related to multiple ecosystem services, 

well-organized stakeholders (in this case, the Upper San Pedro 

Partnership), and native or cultural-heritage management 

issues, which would require consideration of how to value 

benefits that are not easily analyzed by monetary techniques.

The San Pedro River watershed in southeast Arizona met 

these criteria. This watershed contains the SPRNCA and has 

been studied extensively by members of the Upper San Pedro 

Partnership. BLM and USGS are members of the Partnership, 

as are many other government agencies, academic institutions, nonprofit organizations, and private interests. Although 

BLM has limited jurisdiction over the resources outside the 

SPRNCA, the San Pedro River watershed presented a robust 

example of the types of real-world resource-allocation dilemmas faced by BLM around the country, and it provided a good 

opportunity to test numerous valuation methods on a variety of 

resource issues.

After consulting with project partners, we selected the 

entire San Pedro River watershed as the study area, from its 

headwaters in Sonora, Mexico, to its confluence with the Gila 

River. Project participants generally favored studying the 

entire landscape within the watershed, from mountaintops 

to the riparian zone, to better account for the interspersion 

of different management practices and landowners (such as 

BLM, Department of Defense (DOD), U.S. Department of 

Agriculture (USDA) Forest Service, National Park Service 

(NPS), Arizona State Trust Land, and private land). In addition, the benefits of ecosystem services generally accrue 

to human beneficiaries at various spatial scales. Analysis 

of a broader geographic region allows a more complete 

analysis of the spatial flow of services from ecosystems to 

human beneficiaries.

The Upper San Pedro River watershed extends from the 

river’s headwaters near Cananea, Sonora, to a geological constriction known as “The Narrows,” north of Benson, Arizona 
20 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

(fig. 2). The Upper San Pedro has been the subject of considerable scientific study that has described the relationships 

between the watershed’s geomorphology, hydrology, 

riparian vegetation, and avifauna (Moran and others, 2008; 

Stromberg and Tellman, 2009). Additional studies have 

focused on other aspects of the Upper San Pedro’s ecology 

and have begun to document the economic value generated by riparian habitat, particularly for recreational value 

(Orr and Colby, 2002; Colby and Orr, 2005). Spatial data 

for the Upper San Pedro, including the watershed’s Mexican 

headwaters, have also been compiled in a centralized archive 

(Kepner and others, 2003). The combination of intense 

conservation interest in the biologically diverse Upper 

San Pedro and the threat of groundwater decline due to pumping resulting from urban growth, particularly near Sierra Vista 

and Benson, have led to the integration of past research into 

a decision support system designed for use by local watershed groups such as the Upper San Pedro Partnership and the 

Benson Community Watershed Alliance (Serrat-Capdevila and 

others, 2009).

The San Pedro River’s headwaters are located in Sonora, 

Mexico, an area of relatively few scientific investigations. 

Economic values for the river are likely to be different in 

the Mexican part of the watershed than in the United States 

part, owing to the presence of markedly different stakeholder 

groups that likely hold different preferences for riparian and 

upland habitat, and thus likely have a different demand for 

ecosystem goods and services (Ready and Navrud, 2006). 

This probable difference poses challenges to the valuation of 

ecosystem services in the Mexican portion of the watershed, 

particularly if value transfer is applied.

The Lower San Pedro River watershed is less populated 

and has received less scientific study than the Upper San 

Pedro. However, the Lower San Pedro also has high conservation significance, particularly given the threat of groundwater 

pumping prevalent in the Upper San Pedro. In recent years, 

groups such as The Nature Conservancy have been working to 

secure water rights for in-stream flow on the Lower San Pedro. 

USGS and the University of Arizona have conducted initial 

hydrologic modeling of the Lower San Pedro; additionally, 

ecological studies have been completed for parts of the Lower 

San Pedro, and an economic study has examined the value of 

recreation in Aravaipa Canyon, an important tributary to the 

Lower San Pedro (Weber and Berrens, 2006).

The Lower San Pedro River watershed also contains 

two important BLM management units: Aravaipa Canyon 

Wilderness and Muleshoe Ranch Cooperative Management 

Area; most of the remaining land is in private, state land, 

or USDA Forest Service ownership (fig. 3). A central data 

archive does not yet exist for the Lower San Pedro. Thus, 

spatial, ecological, and economic data are somewhat more 

limited for the Lower San Pedro than the Upper San Pedro.

Although we explicitly value ecosystem services generated within the San Pedro River watershed for this study, 

the beneficiaries of these services may be located outside 

the watershed, as provision and use of ecosystem services 

typically occur at various spatial scales (Fisher and others, 

2008; Tallis and Polasky, 2009, Johnson and others, 2012). A 

complete analysis thus necessarily considers beneficiaries at 

global scales (for example, beneficiaries of carbon sequestration and storage), continental scales (bird migration support), 

national scales (national values about endangered species), 

and regional scales (recreation). Some values may extend an 

uncertain distance from the San Pedro (for example, nonuse 

values, Pate and Loomis, 1997; Loomis, 2000).

The SPRNCA is one of only two Riparian National 

Conservation Areas managed by BLM. Its management thus 

focusses predominantly on preservation of natural and cultural 

resources and provision of recreational opportunities (as 

opposed to resource extraction, which is more prominent in 

many other BLM units). On other BLM lands, ecosystem service values could inform tradeoffs between ecosystem services 

and extractive commodity uses such as energy development.

Selection and Application of Ecosystem 

Services, Valuation Methods, and Tools

Ecosystem Services

During a workshop held in January 2010 in Tucson, Ariz., 

with stakeholders and scientists involved with the San Pedro 

River watershed, ecosystem services was selected for further 

analysis. Tools and valuation methods for analysis were used 

as described in the Introduction and Methods sections.

Evaluative Criteria

Through discussions with stakeholders and scientists 

involved this pilot study and the parallel BSR project, including academic and agency scientists and practitioners applying 

ecosystem services concepts within the private sector, a set 

of eight evaluative criteria emerged that were deemed to be 

desirable by these potential users of ecosystem services tools. 

These criteria were applied to judge each tool’s strengths 

and weaknesses in quantifying ecosystem services and its 

applicability within decisionmaking processes through seven 

questions. These questions gage the tool’s ability to support 

ecosystem service assessments that are quantifiable, replicable, 

credible, flexible, and not unreasonably resource intense

1. Does this method or tool quantify ecosystem services, 

and can it explicitly account for uncertainty? Quantified 

outputs are necessary for measuring ecosystem-service 

tradeoffs, though qualitative tools may be useful in 

screening or scoping processes. Reporting a single value 

can inspire false confidence in the certainty of results, so 

uncertainty estimates are a valuable addition to the set of 

model outputs.
Methods 21

2. What are the time requirements to use the method or 

tool? As the time required to apply a tool decreases, it 

becomes increasingly practical for widespread use in 

decisionmaking processes.

3. Is the tool or method open source or proprietary (would 

it require hiring of consultants, or could BLM staff use 

it if properly trained)? Ideally, BLM staff would be able 

to apply the tool without excessive time and training 

requirements or purchase of additional spatial analysis or 

modeling software.

4. What is the current level of development of the tool?

Ideally the tool should be sufficiently developed enough 

to run reliably, use validated models, produce replicable 

results, and have its methods, assumptions, strengths and 

limitations well documented as part of a user manual 

and peer-reviewed journal articles, which may include 

validation exercises. Tools that are well developed and 

documented have greater transparency and credibility, 

which generates trust with decision makers and the public.

5. What is the scalability (applicability at various spatial 

scales) associated with this tool or method? Ideally the 

tool should be applicable at sites ranging from small BLM 

parcels to large parcels, watersheds, or district or field 

office boundaries.

6. What is the generalizability (ease of application at sites in 

different ecosystems, socioeconomic settings, or regions of 

the country) associated with this tool or method? Ideally 

the tool should be applicable throughout the Western 

United States, within all ecoregions and socioeconomic 

settings that contain BLM-managed lands.

7. How is the tool or method able to incorporate multiple 

valuation systems (monetary and nonmonetary) and 

cultural perspectives (such as tribal values)? Ideally 

the tool would account for nonmonetary preferences, 

including American Indian perspectives on how to value 

the environment in decisionmaking.

Methods and Tools Considered in This Report

We were unable to apply proprietary tools or those that 

required hiring a consultant or academic research group. These 

tools were EcoAIM, EcoMetrix, ESValue, NAIS, MIMES, and 

Envision (the last two are open source but require contracting 

with outside developers). We also eliminated tools that were 

at too early a stage of development to apply in new regions 

without substantial participation by an outside research group 

(for example, EcoServ, SERVES, UNEP-WCMC Ecosystem 

Services Toolkit). Tools at an early stage of development 

also tended to lack adequate documentation of assumptions and operating instructions. Other place-specific tools 

were eliminated because they were not easily applied to new 

locations. The Ecosystem Portfolio Model, which was initially 

developed for south Florida and is now under development 

for Puget Sound and the Santa Cruz River in Arizona falls 

into this category, as does InFOREST, which in early 2012 

ran only for the State of Virginia. Finally, without the primary 

survey data needed to populate the SolVES tool, we could not 

apply it to the San Pedro pilot project.

We did not apply the Ecosystem Services Review, which 

is intended for a corporate audience, for this pilot. However, 

this model was applied as part of the BSR tool comparison 

project, and its application may be appropriate in evaluating 

resource-extraction scenarios. (For more information about 

this tool, see the Conclusions section.)

Although we did not explicitly test all of the above-listed 

tools as part of this project, we further discuss their application in the Introduction and Conclusions sections, on the basis 

of discussions with their developers and the parallel BSR 

comparison of tools.

After the tools mentioned previously in this section were 

eliminated, four tools remained that were applicable to the 

San Pedro River watershed within the scope of this pilot project: 

past primary valuation studies, value transfer (using the Wildlife 

Habitat Benefits Estimation Toolkit), InVEST, and ARIES.

Data Needs

Ideally an ecosystem-services tool would use nationally 

available datasets such as those for climate, soils, vegetation, 

and socioeconomic data, supplemented as needed by local 

data. For the San Pedro River watershed, much data were 

obtained from the US EPA San Pedro Data Browser (Kepner 

and others, 2003). See appendixes A and B for the full data 

needs of the InVEST and ARIES tools, as derived from their 

respective modeling references (Bagstad and others, 2011; Tallis 

and others, 2011). Loomis and others (2008) provide details on 

running the Wildlife Habitat Benefits Estimation Toolkit.

Description of Assessment 

and Valuation Processes

The valuation process began with a review of primary 

studies focused on the San Pedro River watershed. Primary 

studies were identified through database searches, specifically 

using Web of Science, EconLit, and EVRI and in consultation 

with researchers who have worked in the San Pedro region. 

See appendixes E and F for primary economic studies of the 

Southwest. Certain individual studies and the Wildlife Habitat 

Benefits Estimation Toolkit provide potentially relevant WTP 

estimates for water, biodiversity, and recreation, and hedonic 

studies provide value estimates for aesthetics. ARIES models 

were used to map carbon, water, aesthetic, and recreational 

values, and InVEST models were used to map carbon, 

water, viewsheds, and biodiversity (table 6). Ecosystem 

services models were reviewed at a workshop in Tucson, 

Ariz., September 21–23, 2010, to incorporate expert input in 

parameterizing the ARIES and InVEST models. Four economists also participated in a review panel that discussed the 
22 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

approaches and results of this study and to review study 

documents, including this report. Participants in the review 

workshop and the economics review panel are listed in 

appendix D.

Scenarios

One of the goals of this study was to compare a set of 

scenarios to baseline conditions to evaluate the effectiveness of tools in handling scenarios. Scenarios provide a way 

to explore realistic, marginal changes in ecosystem services 

and their values. The results of this study were not, however, 

intended to assess or prescribe policy mechanisms, such as 

growth management, or to provide definitive results for making 

a particular decision (for example, as part of an existing costbenefit analysis). They were intended as a proof of concept to 

describe the strengths and shortcomings of current ecosystemservices methods that could guide future decisionmaking.

Several ecosystem-service valuation methods were 

amenable to scenario analysis. InVEST and ARIES rely 

on spatial data. Provided that a data layer is relevant model 

input, inserting an alternative data layer (for example, 

land use, population density, or precipitation) produces a 

second set of results that can be compared to baseline conditions. The Wildlife Habitat Benefits Estimation Toolkit 

can also accommodate scenarios by including proposed 

changes to ecological and socioeconomic conditions in its 

valuation spreadsheet.

Baseline conditions for our analysis are for the 

year 2000, for which many spatial datasets are available, 

such as the Southwest Regional Gap Analysis Project 

land-cover dataset and urban growth scenarios comparing the year 2000 to alternative 2020 scenarios (Steinitz 

and others, 2003). Three scenario categories were used to 

evaluate the responsiveness of ecosystem services tools: 

urban growth, mesquite management, and water augmentation. Although a substantially greater number of scenarios 

can be developed by combining scenarios (for example, 

urban growth–mesquite management–water augmentation), 

in order to present more straightforward results, we evaluated 

each scenario category independently.

Scenario Group 1: Urban Growth

Urban growth scenarios for the Upper San Pedro River 

watershed were compared for the year 2020 by using year 

2000 baseline plus “open” and “constrained” scenarios for 

2020 (Steinitz and others, 2003; fig. 4). The open-development 

scenario assumes 50 percent greater-than-expected population 

growth by the year 2020 with relaxed restrictions on the 

location of new development, whereas the constraineddevelopment scenario combines 50 percent less-than-expected 

population growth with concentration of new development 

near already developed areas. These two scenarios provide 

upper- and lower-bound estimates for urban growth. In addition to urban development, the 2020 scenarios also assumed 

that nearly all grassland and mesquite in the Upper San Pedro 

will have converted to desert scrub. Although Steinitz and 

others (2003) did not justify this assumption, it means that 

not all ecosystem-service changes in these scenarios were 

brought on by development; some result from this assumed 

widespread vegetation change. Kepner and others (2004) also 

used these scenarios to model the relative hydrologic impacts 

of alternative development futures. Data in that study were 

not used in this pilot, as they were derived from an uncalibrated hydrologic model for demonstration purposes, but this 

model calibration approach could be used in the future as 

an input for ecosystem service modeling. Urban-growth and 

climate-scenario datasets compiled by the US EPA (Bierwagen 

and others, 2010) and USGS were not available within this 

project’s timeline.

To run the urban growth scenarios using the InVEST 

model, the model’s baseline land-cover layer was replaced 

by a layer appropriate for each scenario (for example, year 

2020 constrained growth or open growth). For all ARIES 

models, we coded a series of changes in land cover and 

Table 6. Methods used for ecosystems services valuation, San Pedro River watershed, States of Arizona and Sonora, Mexico.

[--, not applicable]

Method Ecosystem service

Carbon Water Biodiversity Aesthetics Recreation

ARIES --

InVEST --

Market price -- -- --

Social cost -- -- -- --

Replacement cost -- -- -- --

Travel cost -- -- -- --

Willingness to pay -- -- -- --

Willingness to pay (transferred) -- -- -- --

Hedonic (transferred) -- -- -- --
Methods 23

other service-relevant model attributes that typically follow 

development on a previously undeveloped landscape pixel. 

For instance, in the ARIES carbon model, percentage of tree 

canopy cover was reduced to “very low” and fire frequency to 

“no fire frequency,” because fire is suppressed in developed 

areas. For the water-supply model, percentage of tree canopy 

cover was reduced to “very low” and potential mountain-front 

recharge was eliminated in developed pixels, because development replaces natural vegetation and soils with impervious surfaces. In the scenic-viewshed model, the preexisting 

vegetation type was eliminated and a new user (housing) 

was created simultaneously on the landscape. For the openspace-proximity model, past land cover was eliminated, fire 

frequency was reduced to “no fire frequency,” and a new user 

(housing) was created on the landscape. For the recreation 

models, wildlife habitat and diversity measures were reduced 

to their lowest value and public access to those developed 

areas was restricted to quantify potential effects on birding, 

wildlife viewing, and hunting.

Scenario Group 2: Mesquite Management 

In recent decades, mesquite has encroached widely onto 

desert scrub and grassland ecosystems in the United States 

Southwest (Kepner and others, 2000), causing an interest 

in using fire or mechanical means to remove mesquite and 

restore grasslands. In 2012, SPRNCA BLM staff completed 

a mesquite management EA identifying specific areas for 

management. Since these areas were not identified at the time 

of the modeling exercise, hypothetical mesquite management 

areas were identified within the SPRNCA (between Highway 

Figure 4. Urban growth scenarios, San Pedro River watershed, States of Arizona (United States) and Sonora (Mexico).

32°30'

31°30'

B. 2020 Constrained development

110°30' 110°30' 110°30'

32°30'

32°00'

31°00'

32°00'

31°00'

32°00'

31°00'

31°30'

32°30'

31°30'

0

0 12 24 MILES

12 24 KILOMETERS

A. 2000 Baseline C. 2020 Open development

EXPLANATION

Forest

Landuse

Oak

Mesquite

Grassland

Desert scrub Barren

Riparian

Agriculture

Urban

Water

San Pedro River 

 Watershed boundary

Base from U.S. Geological Survey digital data, 1999, 1:100,000

Universal Transverse Mecator projection, Zone 12N

North American Datum 1983, (NAD 83)

ARIZONA

San Pedro River

watershed
24 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

90 and Hereford Road). This entailed modeling the conversion of 2,278 acres of mesquite into grassland to evaluate 

ecosystem service effects of this management strategy (fig. 5). 

The Automated Geospatial Watershed Assessment (AGWA) 

Land-cover Modification Tool (Miller and others, 2007) was 

used to convert mesquite to grassland in these areas.

In both the InVEST and ARIES models, the mesquiteremoval layer replaced the baseline land-cover layer. Additionally, because ARIES uses more attributes than land cover 

to model ecosystem service change, changes to other model 

attributes that accompany conversion of mesquite to grassland were coded. For instance, in the carbon and water supply 

models, the percentage of tree canopy cover was reduced to 

“very low.”

Scenario Group 3: Water Augmentation

The Bureau of Reclamation’s (BOR’s) proposed extension of the Central Arizona Project from Tucson to Sierra Vista 

provided the basis for a third category of scenarios (Bureau of 

Reclamation, 2007). This proposed action would bring water 

from the Colorado River to the San Pedro in order to increase 

groundwater levels and surface flow within the SPRNCA, 

with the goal of maintaining or improving aquatic and riparian 

ecosystem quality. Scenarios for riparian condition initially 

developed by Stromberg and others (2006) and more recently 

used by Brookshire and others (2010) for ecosystem service 

analysis (fig. 6) were used to evaluate baseline conditions as 

changed by groundwater futures 3 (a uniform 0.5-meter rise 

Figure 5. Land cover before (A) and after (B) mesquite management, San Pedro River watershed, Arizona.

EXPLANATION

Grassland

Landuse

Desert scrub

Mesquite

Riparian

Agriculture

Urban

SONORA

ARIZONA

Map area

0 1

0 1 2 MILES

2 KILOMETERS

Base from U.S. Geological Survey digital data, 1999, 1:100,000

Geographic projection

World Geodetic System 1984, (WGS 84) San Pedro River

watershed

N:\Jeff\den12_cmrh00_0171_bagstad\report_figures\figure_05.ai

110°10' 110°08' 110°06' 110°04'

31°34'

31°32'

31°30'

31°28'

31°26'

Hereford Road

Hypothetical mesquite 

 management area boundary

90

110°10' 110°08' 110°06' 110°04'

31°34'

31°32'

31°30'

31°28'

31°26'

Hereford Road

90

A B

San Pedro Riparian National 

 Conservation Area boundary
Results 25

in groundwater throughout the SPRNCA) and 9 (achieving 

year-round surface flow within the SPRNCA). In 2012 the 

Arizona BLM and The Nature Conservancy were determining 

desirable groundwater and flow conditions for the Upper San 

Pedro River, which would provide a more objective basis for 

choosing desirable water-augmentation levels.

Water augmentation scenarios were used only in the 

ARIES recreation models, which predicted how changes 

in riparian-ecosystem quality could influence recreation in 

the SPRNCA, because the effect of these scenarios on other 

ecosystem services was unclear. Because riparian condition 

class was a direct input to the ARIES recreation models, the 

two groundwater-rise-scenario datasets were used instead of 

baseline data to obtain scenario results.

Scenario Group 4: Climate Change

Although some climate change modeling work has taken 

place on the San Pedro (Serrat-Capdevila and others, 2007; 

Dixon and others, 2009), this work is either not spatially explicit 

or does not contain results from the Intergovernmental Panel on 

Climate Change (2007) Fourth Assessment Report. We considered but did not apply climate-change scenarios for this project.

Results

Results by Ecosystem Service

Carbon

The InVEST tier 1 carbon storage and sequestration 

model uses a table linking land-cover type to carbon storage; 

sequestration is modeled as a function of land-cover change 

over time. Using this model, carbon sequestration and its value 

for the urban growth and mesquite management scenarios 

were mapped (fig. 7, table 7) (see appendix A for assumptions 

and data sources used with the InVEST models). Carbon was 

valued by using a set of conservative assumptions from the 

climate change economics literature ($22 social cost of carbon 

per ton (2011 United States dollars), 0 percent annual change 

in social cost, 7 percent discount rate) and a set of nonconservative climate economics assumptions ($89/ton, 6 percent 

annual change in social cost, 1 percent discount rate) (for 

more information see appendix A). The InVEST carbon model 

showed a substantial loss in carbon storage under the urban 

growth scenarios, and more carbon storage was lost under 

the open-development scenario (3.4 million tons lost during a 

20-year period, valued at $35.8–144.4 million) than under the 

constrained-development scenario (2.2 million tons lost, valued 

at $26.2–105.5 million). It also showed a smaller loss of carbon 

storage (34 thousand tons, valued at $0.8–3.1 million) under the 

mesquite management scenario (table 7).

The ARIES carbon sequestration and storage models 

quantify and map the following:

1. Carbon sequestration and its uncertainty on the landscape, 

using a probabilistic model and sequestration values from 

the literature and spatial datasets.

2. Potential stored carbon release due to fire or deforestation, 

and its uncertainty, using stored carbon values from the 

literature and spatial datasets. Overlaying a polygon of fire 

locations in a particular year (for example, with average or 

high-intensity wildfire) can provide a more precise estimate 

of carbon release from wildfire.

3. Anthropogenic greenhouse gas emissions, using 

spatial data.

These three maps let a user quantify the regional carbon 

balance by subtracting stored carbon release and greenhouse 

gas emissions from sequestration to estimate whether a region 

is a net carbon sink or source. As in the InVEST model, a value 

can be placed on carbon sequestration by applying a social 

cost of carbon to each ton of carbon sequestration; results are 

presented as average annual values or summed for the policy 

time period as net present values. By using the ARIES carbon model, carbon sequestration and the potential release of 

stored carbon and their uncertainty were mapped for urban 

growth and mesquite removal scenarios (fig. 8, table 8) (see 

appendix B for assumptions and data sources used with ARIES 

Figure 6. Hydrologic scenarios (groundwater futures) and 

year 2006 baseline conditions in the San Pedro River watershed, 

Arizona (Brookshire and others, 2010).

14

13

12

11

10

9

8

7

6

5

4

3

2

1

14

13

12

11

10

9

8

7

6

5

4

3

2

1

Groundwater

future 3

Groundwater

future 9

14

13

12

11

10

9

8

7

6

Groundwater

current

5

4

3

2

1

d\report_figures\figure_06.ai

EXPLANATION

Class 1—Dry

Class 2—Intermediate

Class 3—Wet
26 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Figure 7. Carbon sequestration, storage, and valuation estimates, San Pedro River watershed, States of Arizona (United States) and Sonora (Mexico)—InVEST model results.

A. Carbon storage, 2000

110°30'

32°30'

32°00'

31°30'

31°00'

B. Carbon storage,

 2020 constrained development

110°30'

32°30'

31°30'

32°00'

31°00'

C. Carbon storage,

2020 open development

110°30'

32°30'

31°30'

32°00'

31°00'

D. Value of carbon storage

 change, 2020 open development

110°30'

32°30'

31°30'

32°00'

31°00'

E. Carbon storage,

before mesquite removal

110°15'

31°45'

31°35'

31°25'

Flow

San Pedro

River

F. Carbon storage,

post-mesquite removal

110°15'

31°45'

31°35'

31°25'

Flow

San Pedro

River

EXPLANATION

15.570

Carbon, in tons

Carbon, in tons

15.570

Carbon, in tons

15.570

San Pedro watershed boundary

$695.22$–695.22

Value

—Dollar 

 per pixel nonconservative climate economics 

EXPLANATION

Mesquite management area boundarySan Pedro Riparian National Conservation  Area boundary

15.571.08

Carbon, in tons

15.571.08

Carbon, in tons

0

6 MILES 0

6 KILOMETERS

0

24 MILES 0

24 KILOMETERS

Base from U.S.Geological Survey digital data, 1999, 1:100,000Geographic projection World Geodetic System 1984 (WGS 84)Base from U.S.Geological Survey digital data, 1999, 1:100,000Geographic projection World Geodetic System 1984 (WGS 84)
Results 27

models). As was true for InVEST results, ARIES results also 

suggest loss in carbon sequestration under the urban growth 

scenarios (loss of 110–115 thousand tons of sequestration 

per year, valued at $2.4–10.3 million) and mesquite management scenario (loss of 148 tons of sequestration per year, 

valued at $0.003–0.013 million). All scenarios also resulted in 

reduced potential for the release of stored carbon—with greater 

reduced potential in the open than in the constrained development scenario—because fire will likely be suppressed on and 

around newly developed land, thus lowering the risk for loss of 

stored carbon from fire. Although Bayesian network training 

(Villa and others, 2011) is available within the ARIES system, 

the quality of the training data was such that trained networks 

produced more spatially homogeneous results and had poor 

sensitivity to changing conditions in the scenarios, with only 

a minimal improvement in fit to the training data relative to 

untrained results. Improved training data may improve the 

quality of ARIES results that use trained Bayesian networks.

InVEST and ARIES carbon estimates are not directly 

comparable. InVEST estimates carbon storage on the basis 

of a table of carbon pools linking land use and land cover 

to carbon storage. Carbon sequestration is then estimated 

based on change with time between baseline and future 

scenarios. Although simple, this model provides relatively 

straightforward results based on the carbon pools data. ARIES 

estimates carbon sequestration based on factors influencing 

sequestration itself, the potential release of stored carbon due 

to fire, and greenhouse-gas emissions in the geographic area of 

interest in order to estimate a regional carbon budget. ARIES 

also produces uncertainty estimates for carbon sequestration 

and potential stored carbon release, as derived from results 

of Baysian network models. Both models would benefit from 

further calibration, testing, and refinement of carbon storage 

values by regional experts. Monetary values for ARIES carbon 

results are in dollars per year, whereas InVEST carbon results 

are in dollars during a 20-year period, so InVEST results 

need to be converted to annual values or ARIES results to net 

present values in order for the results to be directly comparable. Annualized results appear for both models in the subsequent section, Valuation Results of Scenarios.

Changes in carbon sequestration were estimated for the 

mesquite management and urban growth scenarios, but not for 

the water augmentation scenario. The InVEST model relies 

on changes in land use or land cover, and this study lacked a 

land-use land-cover typology and carbon pools data with fineenough detail to identify differences in carbon sequestration 

in riparian communities with perennial and with ephemeral 

streamflow. The ARIES carbon model uses factors such as percentage of tree canopy cover, mean annual precipitation, and 

fire frequency in estimating carbon sequestration and potential 

stored carbon release. A lack of information on how (and to 

what extent) water augmentation would change these factors 

meant that these changes were too poorly understood to model 

them accurately (we tested the relationship between riparian 

tree canopy cover and riparian condition class but found it to 

be extremely weak).

Water

InVEST models annual water yield by using average 

annual precipitation and the Budyko curve (Budyko, 1974), 

subtracting evapotranspiration from combined infiltration 

and runoff without differentiating between surface water, 

subsurface water, and base flow. Additionally, InVEST can 

estimate water demand by assigning a water-use coefficient 

to each land use or land-cover type to estimate total anthropogenic water demand. InVEST’s tier 1 water model quantifies 

these values on an annual basis. However, spatially explicit 

water demand could not be quantified by using InVEST, 

owing to limitations in the scenario input data (table 9). 

InVEST’s water yield output is designed to be interpreted at 

the subwatershed to watershed scale, because “pixel-scale 

representations of some outputs (are) for calibration and 

model-checking purposes only” (Tallis and others, 2011). The 

water yield submodel of the InVEST reservoir hydropowerproduction model was used to map water yield based on 

2002 (representative dry year) and 2007 (representative wet 

year) precipitation, in addition to water demand for the urban 

growth and mesquite management scenarios (fig. 9, table 9) 

(see appendix A for assumptions and data sources used with 

Table 7. Carbon sequestration, storage, and valuation estimates, San Pedro River watershed, States of Arizona 

(United States) and Sonora (Mexico)—InVEST model results.

Scenario Carbon storage

(tons) [change]

Net present value of sequestered carbon and stock 

change during a 20-year interval under two sets 

of economic assumptions

(United States dollars 2011) [change]

Conservative Nonconservative

Urban growth

2000 baseline 53,019,000

2020 open 49,660,000 [–3,359,000] [–$35,811,000] [–$144,357,000]

2020 constrained 50,817,000 [–2,202,000] [–$26,174,000] [–$105,508,000]

Mesquite management1

Before 1,557,000

After 1,523,000 [–34,000] [–$756,000] [–$3,057,000] 1

For all services, the results presented for before and after mesquite-management conditions show ecosystem services results for the 

San Pedro Riparian National Conservation Area.
28 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Figure 8. Carbon sequestration, stored carbon release, and greenhouse gas emissions estimates, San Pedro River watershed, Arizona—ARIES model results.

110°30'

32°30'

31°30'

110°30'

32°30'

31°30'

A. Carbon sequestration, 

2000

110°30'

32°30'

31°30'

2.90.03

2.90.03

2.90.03

Carbon, in tons  per hectare  per year

Carbon, in tons  per hectare  per year

Carbon, in tons  per hectare  per year

Carbon, in tons  per hectare  per year

Carbon, in tons  per hectare  per year

110°30'

32°30'

31°30'

D. Potential stored 

carbon release, 2000

B. Carbon sequestration, 

2020 constrained development

C. Carbon sequestration, 

2020 open development

8.930.02

Base from U.S. Geological Survey digital data, 1999, 1:100,000Geographic projection World Geodetic System 1984, (WGS 84)

110°30'

32°30'

32°00'

31°30'

31°00'

32°00'

31°00'

32°00'

31°00'

32°00'

31°00'

32°00'

31°00'

32°00'

31°00'

32°00'

31°00'

E. Carbon sequestration 

uncertainty, 2000

3.630.28

Coefficient of variation

110°30'

32°30'

31°30'

F. Potential stored carbon 

release uncertainty, 2000

110°30'

32°30'

31°30'

G. Greenhouse gas 

emissions, 2000

San Pedro watershed boundary

12.101.84

0

24 MILES 0

24 KILOMETERS

Coefficient  of variation

EXPLANATION

9.950
Results 29

Table 8. Carbon sequestration, stored carbon release, and greenhouse gas emissions, San Pedro River watershed, Arizona—ARIES model results.

Scenario Carbon sequestration (tons/year) [change] Potential stored- carbon release, (tons/year) [change] Greenhouse gas emissions, (tons/year) [change] Annual value of sequestered carbon under two economic assumptions (United States dollars 2011) [change] Conservative Nonconservative 

Urban growth

2000 baseline 526,200 3,291,000 136,600 $11,576,400 $46,831,8002020 open 410,900 [–115,300] 2,789,000 [–502,000] 214,100 [+77,500] $9,039,800 [–$2,536,600] $36,570,100 [–$10,261,700]2020 constrained 416,600 [–109,600] 2,847,000 [–444,000] 150,800 [+14,200] $9,165,200 [–$2,411,200] $37,077,400 [–$9,754,400]

Mesquite management

Before 14,152 96,900 $311,340 $1,259,550After 14,004 [–148] 94,200 [–2,700] $308,090 [–$3,250] $1,246,340 [–$13,210]
30 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, ArizonaFigure 9. Water yield estimates for urban growth scenarios under representative wet year (2007) and dry year (2002) conditions, and year 2000 water consumption, San Pedro 

River watershed, Arizona—InVEST model results.

A. Water yield, 2000 development

(2002 precipitation)

B. Water yield, 2000 development

(2007 precipitation)

C. Water yield, 2020 constrained 

development (2002 precipitation)

D. Water yield, 2020 constrained 

development (2007 precipitation)

401.532.47

Water, in  millimeters

Water, in  millimeters

Water, in  millimeters

Water, in  millimeters

609.536.78

401.534.11

609.5311.02

E. Water yield, 2020 open 

development (2002 precipitation)

F. Water yield, 2020 open 

development (2007 precipitation)

G. Water consumption, 2000

410.542.47

Water, in  millimeters

Water, in  millimeters

614.546.78

110

0

EXPLANATION

Water, in  cubic meters

San Pedro watershed boundary

110°30'

32°30'

32°00'

31°30'

31°00'

110°30'

32°30'

32°00'

31°30'

31°00'

110°30'

32°30'

32°00'

31°30'

31°00'

110°30'

32°30'

32°00'

31°30'

31°00'

110°30'

32°30'

32°00'

31°30'

31°00'

110°30'

32°30'

32°00'

31°30'

31°00'

110°30'

32°30'

32°00'

31°30'

31°00'

0

24 MILES 0

24 KILOMETERS

Base from U.S.Geological Survey digital data, 1999, 1:100,000Geographic projection World Geodetic System 1984, (WGS 84)
Results 31

the InVEST models). Total water-yield values were obtained 

by multiplying the sum of all pixel values in millimeters by the 

area of a cell (3030 meters) and divided by 1,000 to convert 

water yield in millimeters into water yield in cubic meters. In 

InVEST model estimates, water yield substantially increased 

under both urban growth scenarios but showed greater yield 

under the open development (as opposed to the constrained) 

scenario (table 9). The increase in water yield reflects greater 

area of impervious surfaces that results in faster runoff. Faster 

runoff generally is undesirable because it can increase erosion, 

degrade water quality and aquatic habitat, and reduce groundwater recharge (this study did not quantify those effects). A 

small increase in water yield for the mesquite management 

scenario was expected given the lower evapotranspiration 

typical of grasslands relative to mesquite (Nie and others, 

2012). In contrast to the urbanization scenario, increase in 

water yield is beneficial for the ecosystem, because grasslands near the riparian aquifer promote greater infiltration 

and base flow.

ARIES water models quantify (1) annual precipitation 

as the “source” of surface water, (2) evapotranspiration and 

infiltration and their uncertainty as “sinks” that deplete the 

quantity of surface water for downstream flows, (3) use areas 

where surface water is extracted for human use, and (4) flow 

models that map the movement of water across the landscape. 

In early 2012, ARIES did not account for groundwater flows, 

and thus results presented here refer only to surface-water 

flows and users (two small agricultural surface diversions 

near St. David, Ariz. (fig. 2)). In addition, groundwater-use 

data are not available in Arizona, severely limiting spatially 

explicit modeling of groundwater use and flows. The ARIES 

surface-water flow algorithm used in this study takes water 

only immediately upstream of a user to satisfy its water needs, 

so far-upstream impacts do not affect model results.

As in InVEST, these processes in early 2012 are modeled 

at the annual time step, and output was converted from millimeters to cubic meters. The ARIES surface water model, 

mapped theoretical and actual sources, sinks, and surfacewater users for the urban-growth and mesquite-management 

scenarios (fig. 10, table 10) (see appendix B for assumptions 

and data sources used with the ARIES models). Theoretical values show potential evapotranspiration and infiltration, 

whereas actual values show results when connected with a 

flow model (that is, accounting for what happens to water as 

it flows across the landscape). The most interesting ARIES 

results are the theoretical sink values—quantified potential 

landscape-scale infiltration and evapotranspiration. Watershedwide theoretical sink strength decreased by 2.7 percent under 

the open-development scenario and by 2.3 percent under the 

constrained-development scenario. Mesquite management 

reduced the sink strength within the SPRNCA by 0.3 percent. 

These results mean that under these scenarios less infiltration and evapotranspiration are likely to reduce surface water 

flows, largely owing to reduced infiltration in the urban growth 

scenarios, and reduced evapotranspiration for the mesquite 

management scenario. Similar to the increase in surface-water 

yield found in the InVEST water yield model, the reduced 

sink strength in the urban-growth scenarios is a detrimental 

effect, whereas in the mesquite management scenario it is a 

positive effect.

Both InVEST and ARIES estimate water yield and 

demand, but the modeling approaches and output are quite 

different for these two modeling systems. ARIES spatially 

links surface-water users to surface-water provision, whereas 

InVEST models basin-wide water yield. InVEST simplifies 

water movement by considering the combined movement of 

groundwater and surface water, assuming that groundwater 

follows the same flow path as surface water and reaches a 

stream where it is eventually discharged as base flow. In the 

San Pedro River watershed, this process is very slow, and 

individual water molecules may take as little as 50 years or as 

many as 10,000 years (1,100 years on average) to move from 

recharge zones to the river (Mac Nish and others, 2009). However, InVEST water-yield models have been tested in other 

groundwater-dominated systems, where they were deemed to 

perform acceptably, provided that the results could be calibrated to time-series streamflow data (Mendoza and others, 

2011, their box 4.1).

The ARIES water models in early 2012 considered only 

surface flow and account for locally important mountainfront and ephemeral-channel infiltration processes. However, 

the flow model values are lower than expected (table 10), 

indicating that evapotranspiration and infiltration sinks may 

be acting too strongly. Because layers containing spatially 

explicit data for infiltration and evapotranspiration were either 

at inadequate spatial resolution (evapotranspiration) or were 

Table 9. Water yield and demand estimates, San Pedro River watershed, Arizona—InVEST model results.

Housing

development

 scenario

Water yield

(cubic meters) Water demand1

2002—dry year (cubic meters) [change]

[change]

2007—wet year

[change]

2000 baseline 507,627,000 987,713,000 7,743,000

2020 open 568,469,000 [+60,842,000] 1,063,809,000 [+76,096,000] 12,141,000 [+4,398,000]

2020 constrained 533,537,000 [+25,910,000] 1,024,104,000 [+36,391,000] 8,548,000 [+805,000] 1

The InVEST water demand model yielded unrealistic results because it quantified per-pixel water demand, and developed pixels increased by 467 percent 

in the open scenario and 163 percent in the constrained scenario. However, population increased by only 57 percent in the open scenario and 10 percent in 

the constrained scenario. These unrealistic results are strictly a result of scenario input data that considers new development to have a far less compact pattern 

than existing developed areas. As such, to quantify water demand changes in table 9, we multiplied baseline water demand by 10 percent for the constrained 

development scenario and 57 percent for the open development scenario.
32 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Figure 10. Surface-water source, sink, and useage estimates, San Pedro River watershed, Arizona—ARIES model results.

110°30'

32°30'

31°30'

D. Actual Surface water source, 2000 

(2007 preciptation)

334.10

110°30'

32°30'

31°30'

E. Actual surface water sink, 2000 

(2007 precipitation)

110°30'

32°30'

31°30'

F. Actual surface water use, 2000

(2007 precipitation)

87.300

0

24 MILES 0

24 KILOMETERS San Pedro watershed boundary

11,46420.37

Base from U.S.Geological Survey digital data, 1999, 1:100,000Geographic projection World Geodetic System 1984, (WGS 84)

110°30'

32°30'

31°30'

32°00'

31°00'

32°00'

32°00' 32°00'

32°00'32°00'

31°00'

31°00'

31°00'

31°00' 31°00'

716.99228.27

Water, in millimeters

Water, in millimeters

Water, in millimeters

Water, in millimeters

Water, in millimeters

Water, in millimeters

110°30'

32°30'

31°30'

110°30'

32°30'

31°30'

A. Theoretical surface water source

(2007 precipitation)

B. Theoretical surface water sink, 2000

C. Theoretical surface water use

(2007 precipitation)

14521.30

511,5210

EXPLANATION
Results 33

Table 10. Surface water sources, sinks, and usage estimates, San Pedro River watershed, Arizona—ARIES model results.

Scenario

Actual surface water

(cubic meters) Theoretical sink

(cubic meters) [change] 2002–dry year [change] 2007–wet year [change]

Source Sink Water use1 Source Sink Water use

Urban growth

2000 baseline 2,081,000 202,700 2,077,000 3,409,000 202,700 3,397,000 235,407,0002020 open 2,073,000 [–8,000] 208,800 [+6,100] 2,063,000 [–14,000] 3,430,000 [+21,000] 208,800 [+6,100] 3,398,000 [+1,000] 229,176,000 [–6,231,000]2020 constrained 2,097,000 [+16,000] 185,500 [–17,200] 2,094,000 [+17,000] 3,428,000 [+19,000] 185,500 [–17,200] 3,422,000 [+25,000] 230,067,000 [–5,340,000]

Mesquite management

Before 468,000 27,300 709,000 720,000 27,300 1,118,000 5,561,000After 468,000 [0] 27,300 [0] 709,000 [0] 720,000 [0] 27,300 [0] 1,118,000 [0] 5,545,000 [–16,000]

1For comparison, the St. David diversion had an average discharge of 5,671,800 cubic meters per year from 1968 to 1972, while the Pomerene diversion had an average discharge of 1,726,200 cubic meters 

per year for the same period, for a total surface water use of 7,398,000 cubic meters per year (Lacher, 1994). This 5-year period was relatively dry in terms of total annual precipitation. These discrepancies in values suggest the need for further model calibration and testing.
34 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

deemed inappropriate by experts for use in basin-and-range 

topography (infiltration), Bayesian training was not applied to 

the surface-water-sink model. The model would benefit from 

further testing and calibration.

As with the carbon models, changes in water yield were 

estimated for the mesquite-management and urban-growth 

scenarios, but not for the water-augmentation scenario. Both 

models rely on changes in land use or land cover (the ARIES 

model also uses percentage of tree canopy cover as an input), 

but the data did not support using a land-use–land-cover 

typology fine enough to consider differences in water yield 

in ephemeral compared with perennial riparian communities. 

Hydrologic dynamics of the riparian community, particularly 

evapotranspiration, are likely to change with water augmentation, but these changes were too poorly understood to model 

them by using InVEST or ARIES.

An economic value can be placed on water by using 

market price, replacement cost, or WTP (table 11), although 

none of these methods gives complete estimates of the full 

social, ecological, or economic value of water. Market prices 

for water in the San Pedro range from $1.31 to $3.39 per 

thousand gallons (2011 United States dollars), on the basis 

of water rates for the Arizona American Water Company’s 

service in Sierra Vista, Bisbee, San Manuel, and Winkelman, Ariz. (fig. 2) (Water Infrastructure Finance Authority 

of Arizona, 2009). Replacement costs for water range from 

$4.03 to $9.17 per thousand gallons (Bureau of Reclamation, 2007). One study found values for household WTP for 

improved water quality and water system reliability to range 

from $4.63–18.06 per month in the rural Western United 

States (Piper and Martin, 1997). On the basis of regression 

coefficients from Piper and Martin (1997, their table 4) and 

values for the independent variables for Cochise County, 

household WTP ranged from $4.51 to 14.99 per month in 

Cochise County, Ariz. This dollar amount is in addition to an 

average water bill of $53.70 per month (Water Infrastructure 

Finance Authority of Arizona, 2009); in 2011 water expenditures plus WTP would have been $1.49–4.00 per thousand 

gallons, somewhat closer to the lower bound replacement 

cost. These values were applied to scenario results from the 

InVEST and ARIES models.

Biodiversity

The InVEST terrestrial and aquatic biodiversity model 

links land-use and land-cover data with experts’ estimates of 

habitat quality for each land-use or land-cover type maps of 

drivers of landscape change, such as grazing, groundwater 

pumping, roads, climate change, or the United States–Mexico 

border fence; and the degree of change caused by each of 

these drivers. By using the InVEST biodiversity model, 

habitat quality was mapped for the urban growth and mesquite 

management scenarios (fig. 11, table 12) (see appendix A for 

assumptions and data sources used with the InVEST models). 

Modeling results showed a decrease in habitat quality under 

the open-development scenario, a very slight increase in habitat quality under the constrained-development scenario, and 

an increase in habitat quality under the mesquite-management 

scenario (table 12). Both urban-growth scenarios assumed 

large-scale urbanization, which reduced habitat quality, but 

in the constrained-development scenario, conversion of other 

vegetation types from lower- to higher-quality habitat offset 

habitat loss from urbanization and produced a small gain in 

habitat quality for that scenario. ARIES does not have a biodiversity model, but instead it incorporates biodiversity as an 

input into other ecosystem service models, such as recreation, 

discussed in the next section.

Like the ARIES open-space proximity, viewshed, and 

recreation models described below, the InVEST biodiversity 

model estimates values in relative units (for example, 0–100). 

Lacking underlying biophysical units, the numeric outputs 

from these models are less amenable to monetization than 

biophysical outputs of other models such as tons of carbon or 

cubic meters of water. However, the percentage change in the 

service can be a useful metric for comparison of scenarios. 

Although model outputs scored using expert-supplied relative 

values may invite less criticism than outputs that measure up 

poorly against empirically measured ecosystem processes, 

the underlying model inputs must be carefully scrutinized to 

reduce subjectivity.

Cultural Services: Viewsheds, Open-Space 

Proximity, Recreation

The ARIES viewshed and open-space-proximity models 

quantify the contribution of nature toward amenity values, 

typically measured by using hedonic pricing of real estate. 

These models produce maps of (1) sources of high-quality 

open space that are desirable to live near or views that are 

desirable to see; (2) sinks that degrade these features, such as 

transmission lines or mines (visual blight of viewsheds) or 

highways that reduce privacy, increase noise, and block openspace access at the neighborhood scale; and (3) users of open 

space or viewsheds, in this case homeowners. The models 

account for recognizable endpoints related to viewsheds and 

open-space proximity—desirable and undesirable landscape 

Table 11. Economic value of water in the San Pedro River 

watershed, Arizona.

Valuation method

Value

(United States dollars 2011)

Per

1,000 gallons

Per

cubic meter

Market price – lower bound 1.31 0.34

– upper bound 3.39 0.90

Replacement cost – lower bound 4.03 1.07

– upper bound 9.17 2.42

Willingness to pay – lower bound 1.49 0.40

– upper bound 4.00 0.93
Results 35

Figure 11. Biodiversity (habitat quality) and change estimates under urban growth and mesquite management scenarios, San Pedro 

River watershed, Arizona—InVEST model results.

A. Habitat quality,

2000

B. Habitat quality, 

2020 constrained development

C. Habitat quality,

2020 open development

110°30'

31°30'

32°30'

32°00'

31°00'

110°30'

31°30'

32°30'

110°30'

31°30'

32°30'

32°00'

31°00'

32°00'

31°00'

2.4

0

0

0 24 MILES

24 KILOMETERS

Base from U.S.Geological Survey digital data, 1999, 1:100,000 EXPLANATION

Geographic projection World Geodetic System 1984, (WGS 84)

Base from U.S.Geological Survey digital data, 1999, 1:100,000

Geographic projection World Geodetic System 1984, (WGS 84)

1

0

EXPLANATION

Relative

 value

Mesquite management area boundary

San Pedro Riparian National Conservation

 Area boundary

1

0

Relative

 value

1

0

Relative

 value

Relative

 value

0

0 6 MILES

6 KILOMETERS

2.4

0

Relative

 value

E. Habitat quality,

before mesquite removal

110°15'

31°25'

Flow

31°45'

31°35'

San Pedro

San Pedro River River

F. Habitat quality,

after mesquite removal

110°15'

31°45'

31°35'

31°25'

Flow

San Pedro watershed boundary
36 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

features that provide or degrade views and access to highquality open space. Sources, sinks, and users are linked by 

using the appropriate flow model (lines of sight for viewsheds 

and a Gaussian decay function for open-space proximity). 

Uncertainty estimates, produced as the outputs of Bayesian 

network models and variance propagation through the flow 

models, are provided for all outputs. These model outputs are 

quantified by using relative values, which could theoretically 

be linked to hedonic values for real estate.

Using the ARIES viewshed and proximity models, 

we mapped the theoretical source (viewshed or open-space 

quality independent of the location of homeowners and sinks) 

and actual-use values (dependent on user presence and flow 

characteristics) for open-space proximity and viewsheds 

(fig. 12, table 13) (see Appendix B for assumptions and data 

sources used with the ARIES models). We then modeled 

changes in these values under urban growth scenarios. 

Lacking data on how changes in mesquite cover or surface 

flow would be translated into higher or lower aesthetic value, 

we did not run the models for these scenarios, although we 

did calculate baseline theoretical proximity and viewshed 

values for the SPRNCA.

The urban-growth scenarios showed a slight decline in 

the provision of high-quality open space and view quality as 

natural ecosystems were replaced by development. They also 

showed a large increase in actual open space and view use as 

many new low-density developments were spread throughout the landscape where they were close to open space or 

had views of visually appealing objects such as mountains or 

riparian vegetation.

Regrettably, the underlying equations from the most 

locally applicable open-space-proximity hedonic studies 

(Sengupta and Osgood, 2003; Bark and others, 2009) do not 

readily lend themselves to value transfer to the San Pedro 

River watershed. Because this region lacked studies of the 

value of views, it was not possible to obtain economic values 

to apply toward ARIES viewshed results. Bourassa and others 

(2006) found that view-value premiums were greater when 

high-quality views were scarcer. In other words, when many 

new housing units have high-quality views, as might be the 

case in the San Pedro River watershed development scenarios, we might expect the value premium for each unit to 

be relatively small.

The InVEST aesthetic viewsheds model, suitable for use 

in marine and terrestrial settings, quantifies visual impact in 

terms of the number of “visual blight” points visible on the 

landscape, on the basis of data for visual blight points (in this 

study, highways, transmission lines, development, and mines), 

population counts, and a digital elevation model (fig. 13) (see 

appendix A for assumptions and data sources used with the 

InVEST models). Unfortunately, the model automatically 

combines results based on quartile breaks. Without the flexibility for the user to analyze raw results, precise quantification 

of changes in visual impacts between the baseline and scenario 

conditions is impossible (table 14).

The ARIES recreation models quantify the potential 

contribution of nature to different recreational activities. 

Other influences on recreational value include site accessibility, 

infrastructure, and visitor preferences, but the ecosystem 

service component of recreation is the relative contribution of 

underlying natural features toward the quality of the recreational 

experience. The ARIES recreation models produce maps of 

the relative value and uncertainty of potential birding, wildlife 

viewing, and hunting sites for important game species (fig. 14, 

table 15) (see appendix B for assumptions and data sources used 

with the ARIES models). Recreational-use models would show 

the locations of potential users of recreation sites, whereas flow 

models would link users to potential sites through a transportation-network model. Fully parameterized recreational use and 

flow models require higher-quality data on visitor use, preferences, and points of origin than are available for many recreation sites, including those on the San Pedro River watershed, 

however. Changes in potential recreation value for birding, 

wildlife viewing, and hunting under urban growth and water 

augmentation scenarios were modeled, but not for mesquite 

management, owing to a lack of information on how this scenario would influence recreational value.

The estimates produced by the ARIES recreation models showed increases in recreation potential in the SPRNCA 

associated with groundwater augmentation, corresponding to 

improvements in wildlife-habitat quality. Potential recreation 

values declined in both urban-growth scenarios, and a greater 

decline was associated with the open-development scenario. 

Both scenarios produce new development spread throughout 

the landscape, which diminished habitat quality and connectivity. Urban growth, however, also increased new residents, 

who may be recreational users, which could actually increase 

the value of recreation despite declines in habitat quality. 

More intensive modeling of recreational use and flows could 

produce a better understanding of recreational use.

The Wildlife Habitat Benefits Estimation Toolkit provided estimates for several relevant biodiversity and cultural 

values. For hunting on public land a use value of $70.24 per 

Table 12. Habitat quality estimates, San Pedro River watershed, 

Arizona—InVEST model results.

Scenario Habitat quality

Relative value Percent change

Urban growth

2000 baseline 170,700

2020 open 158,400 –7.2

2020 constrained 170,800 +0.03

Mesquite management

Mesquite baseline 128,000

After mesquite removal 132,000 +3.2

Figure 12 (facing page). Estimates of relative value of openspace proximity and viewsheds (source, use, and flow results) 

under urban growth scenarios, San Pedro River watershed, 

Arizona—ARIES model results.
Results 37

110°30'

32°30' 32°30'

31°30' 31°30'

32°00' 32°00'

31°00' 31°00'

32°30'

31°30'

32°00'

31°00'

67.55.25

Relative value

Relative value

Relative value

Relative valueRelative value

Relative value

Relative value

Relative value

4,998.90

110°30'

10,089.30

110°30'

32°30'

31°30'

32°00'

31°00'

10,089.30

110°30'

110°30'

32°30'

31°30'

32°00'

31°00'

10,089.30

110°30'

32°30'

31°30'

32°00'

31°00'

10,089.30

31°30'

32°00'

31°00'

32°30'

110°30'

55.62.5

55.62.5

31°30'

31°00'

32°30'

110°30'

32°00'

H. Actual proximity use, 

2020 open development

G. Actual proximity use, 

2000

F. Theoretical proximity source, 

2020 open development

E. Theoretical proximity source, 

2020 constrained development

D. Theoretical proximity source,

2000

C. Actual viewshed flow,

2000

B. Actual viewshed use,

2000

A. Theoretical viewshed source,

2000

Base from U.S.Geological Survey digital data, 1999, 1:100,000Geographic projection World Geodetic System 1984, (WGS 84)

EXPLANATION

San Pedro watershed boundary

0

24 MILES 0

24 KILOMETERS
38 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Figure 13. Visual impacts to viewsheds, San Pedro River watershed, States of 

Arizona (United States) and Sonora (Mexico)—InVEST model results.

Table 13. Relative value of open space proximity and viewsheds, San Pedro River watershed, Arizona—ARIES model results.

[--, not applicable]

Scenario

Relative value

Viewsheds Proximity of open space

Theoretical source

[percent change]

Actual use

[change]

Theoretical source

[percent change]

Actual use

[change]

2000 baseline 1,026,000 142,200 1,698,000 1,146,000

2020 open 1,025,000 [–0.1] 931,000 [+554.7] 1,649,000 [–2.9] 1,607,000 [+40.2]

2020 constrained 1,026,000 [–0.04] 483,900 [+240.3] 1,679,000 [–1.1] 1,327,000 [+15.7]

Baseline, San Pedro Riparian 

National Conservation Area only

14,100 -- 43,000 --

110°30'

32°30'

32°00'

31°30'

31°00'

No impact (0)

0

0 6 12 18 MILES

6 12 18 KILOMETERS

EXPLANATION

Impact—Number of features visible 

 in parentheses 

San Pedro watershed boundary

Base from U.S.Geological Survey digital data, 1999, 1:100,000

Geographic projection World Geodetic System 1984, (WGS 84)

Low impact (1–2,608)

Low-medium impact (2,609–10,732)

Medium-high impact (10,733–30,214)

High impact (30,215–193,150)
Results 39

hunter-day was estimated for both big- and small-game hunting. The toolkit’s visitor-use model estimated annual visitation to the SPRNCA at 118,147, as compared with the BLM 

estimate of 150,000. The toolkit’s aquatic habitat nonuse 

value worksheet was inappropriate for the San Pedro River. 

Although it is intended to estimate public nonuse values for 

aquatic habitat, this worksheet values changes in water quality 

rather than water quantity or flow permanence, the changes 

that are most relevant for the San Pedro River. The Wildlife 

Habitat Benefits Estimation Toolkit also includes an openspace property premium model. However, this model accounts 

for values from forests, parks, and wetlands in urban or suburban settings rather than desert vegetation in rural settings, 

so its transfer functions were unable to estimate open-space 

values for the San Pedro.

Valuation Results for Scenarios

Urban Growth Scenarios

Traditionally, quantified economic benefits such as 

increased employment and municipal tax base are often used 

to justify the negative environmental and social impacts that 

can accompany urban growth. Consideration of ecosystem 

services as an opportunity cost of urban growth can more fully 

show the costs and benefits of urban expansion. Because the 

aesthetic, recreation, and biodiversity models did not produce 

results amenable to monetization, the use of multicriteria 

analysis, which allows comparison of results using nonmonetary units—where decisionmakers compare the total “basket” 

of ecosystem services produced under alternative scenarios—

is the most feasible way to use these results in decisionmaking. InVEST model results show a loss of carbon storage, an 

increase in water yield, increased water demand, and a small 

gain (under constrained development) or larger loss (under 

open development) in habitat quality. These changes were 

more extreme in the open-development scenario (table 16). 

ARIES model results show a reduction in carbon sequestration, potential stored carbon release, theoretical surface water 

sinks (evapotranspiration and infiltration), viewshed and 

open-space quality, and recreational-value quality; again, 

changes were more extreme in the open-development scenario. 

ARIES models also show large gains in the amount of viewshed and open-space-proximity use, based on the increase 

in new housing units in the watershed (table 17). By using 

Table 14. Visual impacts to viewsheds, San Pedro River watershed, Arizona—InVEST model results.

Urban growth

scenario

Number of blight points visible for different quartiles of impact

(cell count)

None Low Low to medium Medium to high High

2000 baseline 0 (3,871) 0–2,608 (11,203) 2,608–10,732 (11,204) 10,732–30,214 (11,200) 30,214–193,150 (11,202)

2020 open 0 (3,307) 0–7,308 (11,344) 7,308–49,016 (11,343) 49,016–145,360 (11,343) 145,360–738,436 (11,343)

2020 constrained 0 (3,754) 0–3,832 (11,233) 3,832–21,123 (11,230) 21,123–63,341 (11,231) 63,341–385,979 (11,232)

economic values, changes in carbon and water can be monetized, and annual social costs in the constrained scenario 

were $1.6–7.2 million (InVEST results) and $2.7–12.1 million 

(ARIES results). Annual social costs in the open-development 

scenario were $3.3–17.8 million (InVEST results) and 

$3.3–16.0 million (ARIES results) (tables 18, 19).

Raw water volumes are difficult to interpret socially and 

ecologically. For the urban growth scenarios, increased runoff 

from impervious surfaces leads to a variety of water quality 

and quantity problems—most important for the San Pedro 

River watershed is reduced infiltration that can recharge 

groundwater. To more rigorously value water yield changes 

more precise biophysical models need to link changes in 

water yield to other groundwater and aquatic system impacts 

while valuing changes to water supply for people in terms 

of increased groundwater pumping or municipal water costs. 

Because the models lacked such precision on both ecological 

impacts and economic costs, our monetization of water-yield 

change is a relatively nonconservative estimate, and it should 

be interpreted with caution. Although monetary values could 

be applied to the change in recreational-site quality, proper 

quantification of recreational value change would require 

development of a more extensive visitation model than was 

possible for this study or would be possible in the context of 

a typical BLM decision (Loomis, 1995). Although aesthetic 

viewshed and open-space-proximity results could theoretically 

be valued by using hedonic studies, the available literature for 

the San Pedro River watershed did not support monetization of 

these values.

The results reported here for all scenarios cover a wide 

range of dollar values. This range is entirely due to the differences in economic values applied to the biophysical outputs of 

the ARIES and InVEST models. In other words, the value difference for carbon sequestration is attributable to differential 

social costs of carbon, and the value differences for water to 

the three different methods used to value water. These results 

do not include additional sources of uncertainty, such as different discount rates used to convert between annual and net 

present values, or uncertainty outputs of the ARIES models. 

Given the lack of clarity on preferred discount rates or model 

uncertainty measures to use, it was easier at this point to 

leave these sources of uncertainty open for future discussion. 

Eventually, if such approaches were adopted by BLM or other 

agencies, explicit guidance on valuation methods, discount 

rates, and model uncertainty measures should be developed. 
40 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona 110°30' 32°30' 32°00' 31°30'31°00' 32°00' 31°00' 110°30' 32°30'31°30' 32°00' 31°00' 110°30' 32°30'31°30' 32°00' 31°00' 110°30' 32°30'31°30' 32°00' 31°00' 110°30' 32°30'31°30'

0

24 MILES 0

24 KILOMETERS

110°15'

31°45'

31°35'

31°25'

110°15'

31°45'

31°35'

31°25'

110°15'

31°45'

31°35'

31°25'

0

6 MILES 0

6 KILOMETERS

80.155.72

Relative value

Relative value

Relative values

Relative values

Relative values

Relative value

Relative value

1.820.13

Coefficient of variation

65.62.5

80.155.72

80.155.80

80.155.80

80.155.80

80.155.72

G. Birding source value, 

0.5-m groundwater rise

H. Birding source value,

all perennial

F. Birding source 

value, baseline

E. Deer hunting 

source value, 2000

D. Birding source value, 

2020 open development

B. Birding source 

value uncertainty, 2000

C. Birding source value, 

2020 constrained development

A. Birding source value, 

2000

EXPLANATION

Mesquite management area boundarySan Pedro Riparian National Conservation Area boundary

Base from U.S. Geological Survey digital data, 1999, 1:100,000Geographic projection North American Datum 1983, (NAD 83)Base from U.S. Geological Survey digital data, 1999, 1:100,000Geographic projection North American Datum 1983, (NAD 83) San Pedro watershed boundary

EXPLANATION
Conclusions 41

For the constrained-development scenario, changes in 

ecosystem service values were greater when modeled by 

ARIES rather than InVEST. The reverse was true, however, 

for the open-development scenario. The InVEST and ARIES 

models produced more divergent results for the constraineddevelopment scenario (table 20).

Mesquite Management Scenario

Restoration projects such as mesquite management 

fall under the SPRNCA’s mandate “to protect, enhance, 

and maintain the riparian area and the aquatic, wildlife, 

archaeological, paleontological, scientific, cultural, educational, and recreational resources of the public lands surrounding the San Pedro River in Cochise County, Arizona” 

(16 U.S.C. 460xx). In real-world terms, however, restoration 

decisions hinge on prioritization of scarce resources and the 

cost of and public response to alternative restoration projects. 

Ecosystem services provide a quantifiable metric for comparison with other costs and benefits. For example, InVEST modeling results quantified the loss of carbon storage and increase 

in water yield and habitat quality associated with mesquite 

management (table 21). Similarly, ARIES quantified a reduction in summed evapotranspiration and infiltration, effectively 

increasing water yield (table 22).

By applying monetary values to the changes in carbon 

sequestration and water yield, the summed annual social 

cost of lost carbon storage and benefit of increased water 

yield ranged from –$127,400 to +$142,000 (InVEST results; 

table 23) and –$7,750 to +$35,135 (ARIES results; table 24). 

Unlike the urbanization scenario, for which increased water 

yield equated with reduced groundwater recharge and a 

flashier hydrograph, water yield in the mesquite management 

scenario increased largely through reduced evapotranspiration 

as mesquite is replaced by grassland, and we thus considered it a positive value (Nie and others, 2011). As mentioned 

previously, both models would benefit from further parameterization and testing. In the mesquite management scenario, 

changes in ecosystem service values were greater for InVEST 

model estimates than in ARIES model estimates. The ARIES 

and InVEST models produced more divergent results for the 

mesquite management scenario than either of the urban growth 

scenarios (table 25).

Water-Augmentation Scenarios

As previously discussed, the underlying data, models, 

and scenario definition of water augmentation lent themselves poorly to analysis by the InVEST and ARIES models. 

Improved scenarios that better described the biophysical 

change brought on by water augmentation, combined with 

better underlying datasets and models, would enable better 

ecosystem services quantification for the water augmentation scenarios. Relative change in recreation-site quality was 

obtained for the SPRNCA by using the ARIES recreation 

models (table 26). Valuing these effects on recreational use 

was beyond the scope of this study, so it was not attempted.

Conclusions

General Findings

Ecosystem services for the San Pedro River watershed 

were successfully quantified by using multiple ecosystem 

services tools, modeling changes under managerially relevant 

scenarios, and monetizing some services for comparison in 

common units. Despite problems with time requirements, 

uncomparable outputs, and inability to monetize all services, 

this process could successfully be replicated by BLM or other 

management agencies in high-profile cases with adequate 

Figure 14 (facing page). Source values for birding and deer 

hunting (San Pedro River watershed, Arizona) and for birding 

(San Pedro Riparian National Conservation Area)—ARIES 

model results.

Table 15. Relative recreation source value estimates, San Pedro River watershed, Arizona—ARIES model results.

Scenario

Recreation source value and relative value

(relative value) [percent change]

Birding Wildlife

viewing

Hunting

Javelina Dove Deer Quail

Urban growth

2000 baseline 2,034,000 2,770,000 2,489,000 2,583,000 2,483,000 2,601,000

2020 open 2,012,000 [1.1] 2,729,000 [1.5] 2,459,000 [1.2] 2,513,000 [2.7] 2,441,000 [1.7] 2,461,000 [5.4]

2020 constrained 2,020,000 [0.7] 2,748,000 [0.8] 2,475,000 [0.6] 2,529,000 [2.1] 2,457,000 [1.1] 2,478,000 [4.7]

Water augmentation, San Pedro Riparian National Conservation Area

Baseline 65,500 86,100 78,800 83,100 75,300 84,000

Uniform 0.5-m groundwater rise 68,000 [+3.9] 89,200 [+3.7] 82,000 [+4.1] 85,900 [+3.4] 78,300 [+4] 86,800 [+3.3]

Entire area perennial 68,700 [+4.9] 90,300 [+4.9] 82,400 [+4.6] 86,300 [+3.9] 78,700 [+4.6] 87,200 [+3.8]
42 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona Table 16. Biophysical and relative values of ecosystem service changes under alternative urban growth scenarios, San Pedro River watershed, States of Arizona (United States) and Sonora (Mexico)—InVEST model results. Urban growth scenario Ecosystem services (relative value) [change] Carbon storage (ton) Water yield (cubic meters) Water demand (cubic meters) Biodiversity or habitat quality (relative ranking) 2002—dry year 2007—wet year 2000 baseline 53,019,000 507,627,000 987,713,000 7,743,000 170,700 2020 open 49,660,000 [–3,359,000] 568,469,000 [+60,842,000] 1,063,809,000 [+76,096,000] 12,141,000 [+4,398,000] 158,400 [–7.2 percent] 2020 constrained 50,817,000 [–2,202,000] 533,537,000 [+25,910,000] 1,024,104,000 [+36,391,000] 8,548,000 [+805,000] 170,800 [+0.03 percent]
Conclusions 43

Table 17. Biophysical and relative values of ecosystem service changes under alternative urban growth scenarios, San Pedro River watershed, Arizona—ARIES model results.

Urban growth

scenario

Carbon

(tons/year) [change] Theoretical surface-water sink

(cubic meters) [change]

Viewshed

(relative value)

[percent change]

Proximity to open space

(relative value)

[percent change]

Sequestration Potential release of stored carbon Theoretical source Actual use Theoretical source Actual use

2000 baseline 526,000 3,291,000 235,407,000 1,026,000 142,200 1,698,000 1,146,0002020 open 410,900 [–115,300] 2,789,000 [–502,000] 229,176,000 [–6,231,000] 1,025,000 [0.1] 931,000 [+554.7] 1,649,000 [2.9] 1,607,000 [+40.2]2020 constrained 416,600 [–109,600] 2,847,000 [–444,000] 230,067,000 [–5,340,000] 1,026,000 [0.04] 483,900 [+240.3] 1,679,000 [1.1] 1,327,000 [+15.7]

Urban growth

scenario

Recreation source value and relative value

[percent change]

Birding Wildlife viewing Hunting Javelina Dove Deer Quail

2000 baseline 2,034,000 2,770,000 2,489,000 2,583,000 2,483,000 2,601,0002020 open 2,012,000 [1.1] 2,729,000 [1.5] 2,459,000 [1.2] 2,513,000 [2.7] 2,441,000 [1.7] 2,461,000 [5.4]2020 constrained 2,020,000 [0.7] 2,748,000 [0.8] 2,475,000 [0.6] 2,529,000 [2.1] 2,457,000 [1.1] 2,478,000 [4.7]
44 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Table 18. Economic value of ecosystem service changes under alternative urban growth scenarios, San Pedro River watershed, 

States of Arizona (United States) and Sonora (Mexico)—InVEST mode results.

Urban growth

scenarios

Annual value

(United States dollars 2011)

Sequestered carbon Added water supply Total 

2020 open –$1,791,000 to –7,218,000 –$1,516,000 to –10,660,000 –$3,307,000 to –17,878,000

2020 constrained –$1,308,000 to –5,275,000 –$278,000 to –1,952,000 –$1,586,000 to –7,227,000

Table 19. Economic value of ecosystem service changes under alternative urban growth scenarios, San Pedro River watershed, 

Arizona—ARIES model results.

Urban growth

scenario

Annual value

(United States dollars 2011)

Sequestered carbon Reduced infiltration1 Total

2020 open –$2,436,000 to –10,258,000 –$822,000 to –5,777,000 –$3,258,000 to –$16,035,000

2020 constrained –$2,412,000 to –9,756,000 –$327,000 to –2,301,000 –$2,739,000 to –$12,057,000 1

Calculated as the reduced infiltration component of the ARIES surface water sink models, in other words, the reduced groundwater recharge caused 

by development.

Table 20. Total annual cost of ecosystem service loss under urban-growth scenarios, San Pedro River watershed, Arizona—ARIES 

and InVEST model results.

Urban growth

scenario

Estimate of change in value

(United States dollars 2011)

Value

differential

ARIES InVEST (percent)

2020 open –3,258,000 to –16,035,000 –3,307,000 to –17,878,000 1.5 to 11.5

2020 constrained –2,739,000 to –12,057,000 –1,586,000 to –7,227,000 40.1 to 42.1

Table 21. Biophysical and relative values of ecosystem service changes under mesquite management scenario, San Pedro River 

watershed, Arizona—InVEST model results.

Mesquite

management

scenario

Ecosystem services

[change]

Carbon storage

(ton) [change]

Water yield

(cubic meters) Biodiversity or habitat quality

(relative value) [change] 2002—dry year 2007—wet year

Before 1,557,000 9,859,000 25,264,000 128,000

After 1,523,000 [–34,000] 9,935,000 [+76,000] 25,338,000 [+74,000] 132,100 [+3.2 percent]

Table 22. Biophysical and relative values of ecosystem service changes under mesquite management scenario, San Pedro River 

watershed, Arizona—ARIES model results.

Mesquite

management

scenario

Carbon

(tons/year) [change] Theoretical surface-water sink

and reduced evapotranspiration

Sequestration (cubic meters/year) [change] Potential storedcarbon release

Before 14,152 96,900 5,561,000

After 14,004 [–148] 94,200 [–2,700] 5,545,000 [–16,000]
Conclusions 45

resources. This section further discusses these problems and 

potential solutions to lower the barriers to widespread adoption of ecosystem services in decisionmaking.

Comparability of Spatially Explicit Results 

from ARIES and InVEST Models

Although the magnitude of change in ecosystem services 

was not always comparable when InVEST and ARIES were 

used, the sign of the change was typically equivalent, and for 

this case study a decisionmaker would reach similar conclusions about ecosystem services by using either tool.

Monetized results generated by the ARIES and InVEST 

models were most similar under conditions of the greatest 

landscape change, the open development scenario, and most 

divergent under the mesquite management scenario, which 

took place over a relatively limited spatial extent. There are 

two possible reasons for the divergent values obtained by 

using ARIES and InVEST: use of different ecosystem service 

metrics, and different modeling approaches or philosophies 

behind the two tools.

Most of the ARIES and InVEST outputs use different 

ecosystem service metrics. The fact that these modeling tools 

use different ecosystem service metrics likely stems in part 

from differences in their underlying modeling approaches. 

InVEST relies on published production-function information 

wherever possible, whereas ARIES focuses on the beneficiaries of ecosystem services and ecosystem service flow 

quantification by modeling and mapping ecosystem-service 

sources, sinks, use, and flows. InVEST’s tier 1 models are 

largely populated by linking land cover to service provision by means of coefficient tables for each land-cover type 

(for example, for carbon storage, evapotranspiration coefficient, or nutrient-filtering capacity); data for the coefficient 

tables are typically derived from field experiments. The 

Table 24. Economic value of ecosystem service changes under mesquite management scenario, San Pedro River watershed, 

Arizona—ARIES model results.

Mesquite

management

scenario

Estimate of change in value

(United States dollars 2011)

Sequestered carbon Reduced transpiration Total

After –$3,250 to 13,210 +$5,460 to 38,400 –$7,750 to +$35,150

Table 23. Economic value of ecosystem service changes under mesquite management scenario, San Pedro River watershed, 

Arizona—InVEST model results.

Mesquite

management

scenario

Estimate of change in value

(United States dollars 2011)

Sequestered carbon Added water supply Total

After –$37,800 to –152,900 +$25,600 to 179,700 –$127,400 to +$142,000

Table 25. Total annual cost or benefit of ecosystem service gain or loss under mesquite management scenario, San Pedro River 

watershed, Arizona—ARIES and InVEST model results.

Mesquite

management

scenario

Estimate of change in value

(United States dollars, 2011) Value differential

(percent) ARIES InVEST

After –$7,750 to +$35,150 –$127,400 to +$142,000 304 to 1,544

Table 26. Relative values of ecosystem service changes under water augmentation scenarios, San Pedro River watershed, Arizona—

ARIES model results.

San Pedro Riparian National

Conservation Area scenario

Recreation source

(relative value) [percent change]

Birding Wildlife

viewing

Hunting

Javelina Dove Deer Quail

Baseline 65,500 86,100 78,800 83,100 75,300 84,000

Uniform 0.5-meter groundwater rise 68,000 [+3.9] 89,200 [+3.7] 82,000 [+4.1] 85,900 [+3.4] 78,300 [+4] 86,800 [+3.3]

Entire area perennial 68,700 [+4.9] 90,300 [+4.9] 82,400 [+4.6] 86,300 [+3.9] 78,700 [+4.6] 87,200 [+3.8]
46 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

ARIES models, by contrast, commonly use remotely sensed 

or modeled spatial datasets for Bayesian network training 

or calibration. It is thus not surprising that these approaches 

yield divergent results. Both approaches have scientific validity, and further comparative applications, perhaps in concert 

with more robust biophysical models, are needed to identify 

the conditions under which each more accurately quantifies 

ecosystem services.

Advantages and Disadvantages 

of Alternative Approaches

A key tradeoff in the application of any ecosystemservice tool is the time required to apply it relative to the depth 

and quality of information that it adds to the decisionmaking 

process (table 27). If the quality and quantity of information 

added by these tools is high relative to the time requirements, 

such tools are more likely to add value to the decision process. 

The time needs of many tools could be substantially reduced 

if they were better developed and supported by high-quality 

archives of spatial, ecological, and economic data. Such data 

would permit more rapid model parameterization, reduce 

the likelihood that practitioners will overlook important data 

sources, and potentially facilitate adoption of these tools by 

resource managers. We further discuss such data archives in 

the “Implications for Future Work” section.

The approaches evaluated in this study differed greatly 

in their performance against the seven evaluative criteria 

described in the Methods section (table 28). No tool performs 

well in all categories, suggesting that different tools will 

be most appropriate in different situations and highlighting 

the value of rigorous comparisons of available tools. Some 

complementarity exists between tools, which suggests that 

some tools could be used together to fill different ecosystem 

service assessment needs (fig. 15). For example, the ESR 

or UNEP–WCMC Ecosystem Services Toolkit can serve as 

screening tools to evaluate ecosystem services of importance, 

either in the absence of local stakeholders who can provide 

informed input or in collaboration with local stakeholders as a 

way to structure their input. This preliminary assessment could 

then be used as a broader analytical frame within which to 

conduct more detailed analyses from mapping and modeling 

tools that quantify landscape-scale ecosystem services 

tradeoffs (for example, ARIES, EcoAIM, Envision, EPM, 

InVEST, InFOREST, MIMES, SolVES). If needed, EcoMetrix 

can be used for site-scale modeling to compare tradeoffs at 

fine spatial scales. Finally, model outputs can be valued by 

multicriteria analysis tools (for example, EcoAIM, ESValue) 

or monetary valuation by NAIS, SERVES, or the Wildlife 

Habitat Benefits Estimation Toolkit. General strengths and 

limitations of each tool are discussed in the following sections. 

Specific strengths and weaknesses of individual ecosystem 

service models within each toolkit are discussed in the 

Results section.

Application of Past Primary Valuation Studies

Although it is not feasible to conduct new primaryvaluation studies to support all but the most high-profile 

decisions, past primary-valuation studies can often aid in 

decisionmaking. Past studies are used in some BLM district 

and field offices, but there are no standard practices for applying past-valuation studies, and their use typically depends on 

the capability of individual analysts. BLM offices should be 

aware that past local primary studies typically capture only a 

limited number of ecosystem services or decision contexts. 

Searching for, reviewing, and distilling data from these studies 

into useful information can also be relatively time consuming, and it requires a thorough understanding of various 

econometric methods. Such searches also require access to 

academic journal articles, which may further limit feasibility 

for BLM district and field offices. Compilation of such studies 

into an existing or new database, however, could reduce the 

time needed to compile and synthesize local studies for use in 

decisionmaking.

Value Transfer

The Wildlife Habitat Benefits Estimation Toolkit provides 

a well-documented, spreadsheet-based set of transfer functions, 

reducing the need to develop new transfer functions or use 

unreliable point transfers (see appendix C for further discussion of value transfer). Point transfers or other value transfers 

known to be inadequate are sometimes justified by noting that 

any value is better than the implicit value of zero that natural 

resources too commonly are given. However, a consequence 

of such transfers is the risk of discrediting all environmental 

valuation techniques (Smith, 1992), a risk that may be greater 

in higher-profile decision contexts. Most of the literature 

supports the notion that if a resource value cannot be defensibly 

monetized, it is preferable to provide a qualitative description 

or utilize a framework that allows for various types of values 

(for example, multicriteria analysis (Spash and Vatn, 2006)). 

The benefits of a single unit (money) to compare disparate 

resources, however, can be substantial. For instance, Office of 

Management and Budget (OMB) economists have been skeptical that qualitative descriptions of ecosystem service benefits 

would be able to offset monetary estimates of the opportunity 

cost of their preservation (Loomis and Rosenberger, 2006).

BLM is encouraged to be cautious when using value 

transfer. Monetization may give a false sense of precision, particularly to nonexpert audiences. The value transfer literature 

has found value estimate errors that are as high as 7,000 percent (Loomis, 1992). Errors ranging from 20–40 percent are 

probably in the acceptable range (Navrud and Ready, 2007). 

Deciding on acceptable errors depends on the context in which 

the values will be used (Rosenberger and Stanley, 2006). A 

cost-benefit analysis on a small-scale project will require less 

precision than a Natural Resource Damage Assessment where 

legal compensation is decided. Value transfer may be appropriate for the former case but is probably inappropriate for 

the latter (Navrud and Ready, 2007). Although one needs to 

appreciate the potential pitfalls of value transfer, this method 
Conclusions 47

can be a useful aid for decisionmaking in BLM, particularly if 

functions are incorporated into well-documented, user-friendly 

spreadsheets such as the Wildlife Habitat Benefits Estimation 

Toolkit. Support for keeping this toolkit up to date and 

incorporating additional transfer functions from the literature 

into a similar framework would be worthwhile for BLM and 

other agencies to consider. Regardless of the applications of 

value transfer and the transfer errors that BLM decides are 

acceptable, the best-practice principles outlined in appendix C 

provide guidelines for more defensible value transfer.

Ecosystem Services Review

Ecosystem Services Review is a well-documented, 

spreadsheet-based tool that can be used to rapidly but qualitatively describe the effects of ecosystem services. Although 

it was originally developed for private sector use it could 

be adapted for ecosystem service impact analysis by public 

agencies involved in extractive resource use, such as the BLM 

or USDA Forest Service. Although the ESR requires relatively 

little time to complete, it does not provide any quantitative or 

spatial information about the distribution of ecosystem service 

values or impacts. Its greatest value may thus be as a screening 

tool to pair with another method that will provide a quantitative or spatial ecosystem services assessment.

Integrated Valuation of Ecosystem Services 

and Tradeoffs (InVEST)

InVEST enables mapping and quantification of ecosystem services and of tradeoffs between services by using 

well-documented models that have appeared extensively in 

the recent peer-reviewed literature. InVEST’s tier 1 models 

are relatively simple but are transparent and could be used 

by BLM district and field offices given adequate supporting 

data, ArcGIS software, and a moderate level of GIS expertise. Underlying data are the largest obstacle to regular use of 

InVEST: assembling the needed spatial data and parameterizing InVEST’s underlying data tables can be time consuming 

and risks subjectivity if done poorly. A central data archive 

that supports InVEST modeling would greatly reduce the time 

required for analysis, and it could make widespread application of InVEST much more feasible. InVEST requires the 

user to conduct all up-front spatial-data preparation, which 

for cross-boundary cases such as we encountered on the 

San Pedro River watershed can include the sometimes-difficult 

task of merging multiple datasets by using common concepts, 

units, spatial resolution, and projection. This task limited our 

ability to map ecosystem services for the entire San Pedro 

River watershed, in particular for the Mexican side of the 

watershed, as had initially been hoped. These limitations could 

cause problems when InVEST is applied throughout contiguous regions having different underlying datasets. Improperly 

formatted input data and files are apt to produce errors, making it critical for the user to closely follow the file formats 

provided with the initial download of the InVEST tool.

Uncertainty was not addressed in early 2012 InVEST 

models, although future releases of InVEST may support 

Monte Carlo simulation. Kareiva and others (2011) recommend including a range of values for relevant ecological 

coefficients needed to parameterize the InVEST models, 

which would produce a range of values in the model results. 

Use of high and low ecological coefficients in future applications of InVEST (not done in the San Pedro River watershed 

study) could better capture the range of likely values to be 

found within different land use and land-cover types.

InVEST tier 2 models have been described (Kareiva and 

others, 2011), but they are not yet linked to a modeling or 

mapping platform as the tier 1 models are. The tier 2 models 

are substantially more data intensive, which may make them 

even more time intensive for BLM use in the near term. Future 

InVEST releases may improve the system’s usability, quality 

of outputs, and ability to link to external ecological process 

models, potentially enhancing the tool’s functionality for BLM.

Table 27. Estimated time to complete ecosystem service assessment and valuation by use of alternative methods.

Method or tool

Estimated hours

to complete Relative

quantity of

information

provided

Comments Pilot

study

With highquality data

archive

Application of past primary 

valuation studies

60 20 Moderate Time needed for review and synthesis of the literature; could be 

greater in areas where more studies have been completed (for 

example, Pacific Northwest).

Value transfer 10 10 Moderate

to low

Estimate for the Wildlife Habitat Benefits Estimation Toolkit. Building 

new transfer functions would take much longer, particularly if a 

Bayesian approach is used.

Ecosystem Services Review 10 10 Low Can be completed quite quickly but does not provide quantitative 

results; time to completion could be several times as great if a large 

number of stakeholders are involved.

InVEST (four ecosystem services) 275 40 High Time to complete could be sharply reduced by a system for sharing 

data and underlying model assumptions.

ARIES (four ecosystem services) 800 40 Highest Included time to customize and extensively debug models, which will 

be unnecessary for future applications. Spatial data management 

system reduces data input needs in future applications.
48 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Table 28. All ecosystem service tools relative to key evaluative criteria.—Continued

[BLM, Bureau of Land Management; GIS. geographic information system; n vs. k, sample size versus number of independent variables]

Tool

Quantifiable,

approach to 

uncertainty

Time required Open source

or proprietary

Level of

development Scalability

Primary valuation Quantitative, uncertainty 

estimates provided in 

statistical models

High (1 year or more), unless 

local studies have been 

previously completed

Results typically accessible 

via publication in the 

peer-reviewed or gray 

literature

Theoretically well 

developed. Requires 

survey development and 

administration, analysis

Results are properly 

interpreted as 

dependent on the 

scenario posed to 

respondents

Point transfer Quantitative, uncertainty 

through reporting a 

range of values

Low (less than 1 month; less 

if there is access to a good 

valuation database)

Varies; databases range from 

open source to proprietary

Theoretically well developed Site to landscape scale

Function transfer 

(multiple 

regression)

Quantitative, uncertainty 

estimates provided in 

regression models

Low for previously 

developed functions, 

high if developing 

original functions

Results typically accessible 

by means of publication 

in peer-reviewed or gray 

literature

Well-developed for some 

services and ecosystems, 

poorly developed for 

others

Site to landscape scale

Function transfer 

(Bayesian)

Quantitative, uncertainty 

through Bayesian 

methods

High, requiring additional 

statistical expertise beyond 

development of multiple 

regression-based function 

transfer

Results typically accessible 

by publication in the peerreviewed or gray literature

Theoretically well developed 

but has only been used 

several times

Site to landscape scale

Function transfer 

(Defenders of 

Wildlife)

Quantitative, uncertainty 

through varying inputs

Low Publicly available Fully developed and 

documented

Site to landscape scale

InVEST Quantitative, uncertainty 

through varying inputs

Moderate to high, depending 

on availability of GIS and 

ecological data to support 

modeling

Publicly available, 

requires ArcGIS

“Tier 1” models 

fully developed and 

documented; “Tier 2” 

models documented but 

not yet released

Watershed or 

landscape scale

ARIES Quantitative, uncertainty 

through Bayesian 

networks and Monte 

Carlo simulation

High to develop new case 

studies, low for preexisting 

case studies

Publicly available Fully documented; case 

studies complete but 

global models and web 

tool under development

Watershed or 

landscape scale

MIMES Quantitative, uncertainty 

through varying inputs 

(automated)

High to develop and apply 

new case studies.

Publicly available, requires 

SIMILE modeling software

Some models complete 

but not documented

Multiple scales

Ecometrix Quantitative Approximately 150 hours of 

consultant’s time for field 

visits, data analysis in 

regions where tool has not 

yet been applied

Proprietary High for Pacific Northwest 

and Southwestern 

U.S., requires further 

development elsewhere. 

Some documentation 

available

Site scale

EcoAIM Quantitative Variable: biodiversity model 

requred 25 hours, inclusion 

of preferences, mathmatical models, tradeoff 

modeling, decision support system framework, 

monetization more time 

consuming.

Proprietary, requires 

contracting with Exponent

Public documentation 

unavailable

Watershed or 

landscape scale

ESValue Quantitative, uncertainty 

through Monte Carlo 

simulation

Approximately 400 hours of 

consultant’s time per case 

study.

Proprietary, requires 

contracting with Entrix

Well developed but formal 

documentation not 

publicly available.

Landscape to 

watershed scale
Conclusions 49

Table 28. All ecosystem service tools relative to key evaluative criteria.—Continued

[BLM, Bureau of Land Management; GIS. geographic information system; n vs. k, sample size versus number of independent variables]

Generalizability

Nonmonetary 

and cultural 

perspectives

Other notes Feasibility 

for BLM

Information 

content for BLM

Moderate; results can be 

used in value transfer

Accommodates other perspectives; 

does not have to use dollars to 

report preferences

Although completion of new 

studies is typically infeasible, 

completed studies for area of 

interest can be applied with 

low resource needs

Low, time and expense to 

develop is too high for all 

but the most high-profile 

decisions

High relevance to the 

decision at hand.

High Typically uses dollars to 

aggregate value

Substantial assumptions when not 

using a transfer function

High, low time and resource 

requirements

Low, often used to get 

“ballpark numbers” 

or build awareness of 

the value of ecosystem 

services but less relevant 

for decisionmaking.

High, within the constraints 

of the transfer function

Typically uses dollars to 

aggregate value

Not all services have functions 

developed. “n vs. k” problem, 

the tradeoff between number of 

usable studies and information 

content in transfer functions

High were previously 

developed; low otherwise

Intermediate between 

primary valuation and 

point transfer.

High, within the constraints of 

the transfer function

Typically uses dollars to 

aggregate value

Addresses “n vs. k” problem at 

a cost of greater development 

time and statistical expertise

High items were previously 

developed, low otherwise

Likely greater than multiple 

regression-based function 

transfer due to inclusion 

of more studies or 

independent variables, 

or both.

High Dollar values only Not all services have functions 

developed. Few primary studies 

drawn from desert ecosystems

High, transfer functions are 

contained within Excel 

spreadsheets

Intermediate between 

primary valuation and 

point transfer.

High, assuming availability 

of underlying data

Biophysical values, can 

be monetized

Well-vetted coarse-scale models; 

local offices would need to 

determine usefulness of the 

outputs

Moderate; time to assemble 

data to parameterize 

models limits broad 

applicability

High; quantifies tradeoffs 

across multiple ecosystem 

services.

High, once global models are 

completed

Biophysical values, can 

be monetized

Models are still in development; 

beta release planned for 

late 2012

Moderate; highly feasible 

to use online models but 

use elsewhere depends on 

release of global models

Very high; quantifies 

tradeoffs across multiple 

ecosystem services while 

providing uncertainty 

estimates and spatial flow 

information.

High, once global or national 

models are completed

Monetary valuation via inputoutput analysis. Combinations 

of biophysical outputs could be 

used in nonmonetary valuation 

as an added step

Dynamic model incorporating 

change over time and feedback 

processes. Model complexity 

makes it difficult to transfer to 

high resolution or spatial extent

Low, unless further 

model development 

and specialized training 

occurred

Potentially high, including 

results of dynamic models 

and valuation using inputoutput modeling.

Requires field visits and 

data analysis for the sites 

to be evaluated, which 

must be completed for 

application of Ecometrix 

in new regions

Designed as a credit calculator, 

no economic valuation

The only tool analyzed that uses 

site-level analysis; could be 

paired with a landscape-scale 

tool to provide fine-grained 

site analysis

Moderate, could be applied if 

funding were available to 

hire consultants

High, if applied in a 

region where it has been 

developed and site-level 

analysis was desirable.

High, though results are 

specific to the issues and 

stakeholder group

Incorporates stakeholder 

preferences via modified 

risk analysis approach

Moderate, could be applied if 

funding were available to 

hire consultants

High, if combined weighted 

mapping of ecosystem 

services and risk-analysisbased stakeholder 

preferences evaluation 

is desired.

Results would be specific to 

the issues and stakeholder 

group involved in 

the process.

Explicitly incorporates preferences 

nonmonetarily via ranked 

analysis of tradeoffs by 

stakeholders

Moderate, could be applied 

if funding were available 

to hire consultants

High, if nonmonetary 

preferences are important.
50 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Table 28. All ecosystem service tools relative to key evaluative criteria.—Continued

[BLM, Bureau of Land Management; GIS. geographic information system; n vs. k, sample size versus number of independent variables]

Tool

Quantifiable,

approach to 

uncertainty

Time required Open source

or proprietary

Level of

development Scalability

NAIS Quantitative, reports 

range of values

Variable depending on 

stakeholder involvement 

in developing the study

Proprietary Developed but public documentation 

unavailable

Watershed or 

landscape scale

SERVES Quantitative, reports 

range of values

Uncertain; tool under 

development, but 

presumed to be 

relatively low

Subscription-based service run 

by Earth Economics

Under development; 

expected beta release 

by late 2012

Watershed or 

landscape scale

SolVES Quantitative, no explicit 

handling of uncertainty

High if primary surveys 

needed to be developed, 

administered, and 

analyzed; low if 

compatible survey data 

were available or function 

transfer approach is used

Publicly available, requires 

ArcGIS

Fully developed and 

documented

Watershed or 

landscape scale

Envision Quantitative California, 1 year, 

$100,000–150,000 to 

develop a new case study

Publicly available Developed and documented 

for Pacific Northwest 

case study sites

Landscape scale

Ecosystem 

Portfolio 

Model

Quantitative High to develop new case 

studies, low for existing 

case studies

Publicly available High for South Florida, in 

development for Puget 

Sound and for Santa Cruz 

watershed, Arizona

Watershed or landscape scale

EcoServ Quantitative, uncertainty 

through varying inputs

High to develop new case 

studies, low for existing 

case studies

Publicly available Under development, not yet 

documented

Site to landscape scale

InFOREST Quantitative Low; accessed through 

online interface

Publicly available Developed and documented 

only for Virginia

Site to landscape scale

ESR Qualitative Low (1 week to 1 month, 

depending on number of 

stakeholders involved)

Publicly available Fully developed and 

documented

Multiple scales

UNEP–WCMC 

Ecosystem 

Services Toolkit

Qualitative Low, depending on 

involvement of 

stakeholders in the 

ES survey process

Publicly available Documented but tool has not 

yet been publicly released

Multiple scales
Conclusions 51

Table 28. All ecosystem service tools relative to key evaluative criteria.—Continued

[BLM, Bureau of Land Management; GIS. geographic information system; n vs. k, sample size versus number of independent variables]

Generalizability

Nonmonetary 

and cultural 

perspectives

Other notes Feasibility 

for BLM

Information 

content for BLM

High, within limits of 

point transfer

Dollar values only Point transfer often used to get 

“ballpark numbers” or build 

awareness of ecosystem 

service values

Moderate, could be applied 

if funding were available 

to hire consultants

Low, often used to get 

approximate numbers 

or build awareness of 

the value of ecosystem 

services but less relevant 

for decisionmaking.

High, within limits of 

point transfer

Dollar values only Point transfer often used to get 

“ballpark numbers” or build 

awareness of ecosystem 

service values

Moderate to high, depending 

on the structure of the 

subscription service

Low, often used to get 

approximate numbers 

or build awareness of 

the value of ecosystem 

services but less relevant 

for decisionmaking.

High, if value transfer can 

be shown to successfully 

estimate values at 

new sites

Nonmonetary preferences 

(rankings) of relative 

values for stakeholders

Unique in providing maps of 

social values for ecosystem 

services; time consuming 

to run new studies but 

lower cost for as-yet 

untested value transfer

High if primary survey 

data are available 

(currently only for 

forests in Colorado) or 

value transfer results 

are acceptable. New 

applications could 

expand versitility 

of SolVES’ value 

transfer function

High, particularly if primary 

survey data are available 

and nonmonetary 

values are desired. Initial 

transferrability of results 

using value transfer 

appears promising (that 

is, to still offer high 

information content in 

the absence of primary 

survey data), though this 

should be tested further 

before extensively 

relying on SolVES’ value 

transfer function.

Place specific Designed to allow stakeholders 

to compare tradeoffs using 

nonmonetary perspective, 

also to support monetary vluation

Links ecological and economic 

process models to 

measure and map impact 

to “landscape metrics,” 

including ecosystem services

High in regions where it 

has been developed, 

low elsewhere

High in regions where it 

has been developed.

Place specific Ecological, economic, and quality 

of life attributes could support 

nonmonetary valuation

Cost effective in regions where 

it has been developed; 

otherwise time consuming 

for new applications

High in regions where it 

has been developed, 

low elsewhere

High in regions where it 

has been developed.

High, once global or national 

models are completed

Biophysical values, 

can be monetized

Dynamic model incorporating 

change over time and 

integration of existing 

ecological and hydrologic 

process models

High once online interface 

and models are released, 

assuming the model 

is available globally, 

nationally, or in states 

with BLM land

Potentially high, including 

maps and measurements 

of ecosystem service 

change over time, 

user-friendly interface that 

enables easy creation of 

scenarios.

Currently place specific Designed as a credit calculator, 

no economic valuation

Cost effective in regions where 

it has been developed; 

otherwise time consuming 

for new applications

High, if developed for 

western United States

Moderate to high if 

developed for the 

western United States. 

Minimal value for 

BLM otherwise.

High No valuation component Most useful as a low-cost 

screening tool

Feasible now Low, though can be used 

as a screening tool for 

quantitative ecosystem 

service assessment 

using other tools.

High No valuation component Most useful as a low-cost 

screening tool

Feasible once tool 

is released

Low, though can be used 

as a screening tool for 

quantitative ecosystem 

service assessment 

using other tools.
52 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Artificial Intelligence for Ecosystem Services (ARIES)

Like InVEST, ARIES quantifies and maps ecosystem 

services and supports scenario analysis for evaluating ecosystem service tradeoffs. ARIES is a more flexible modeling 

system than InVEST, because it is easier to customize scenarios 

and models to account for locally important ecological and 

socioeconomic conditions. It provides uncertainty estimates 

associated with results and ecosystem service flows by explicitly evaluating provision, use, and flow characteristics for each 

service. This type of analysis provides maps of actual ecosystem 

service provision and use rather than just potential provision in 

the absence of human beneficiaries (Johnson and others, 2012). 

Finally, ARIES minimizes local spatial data handling requirements by linking to a GeoServer—an open-source database 

that can serve spatial data for modeling (http://www.geoserver.

org)—that in early 2012 stored over 400 datasets relevant to 

ecosystem service modeling. These datasets can be called on for 

any ecosystem service model, and results can be delivered to the 

user by means of a web interface.

However, in early 2012 ARIES had several key limitations. Constructing new ARIES models is time consuming, 

although substantially less time consuming upon completion 

of this project than at its start, and currently it requires use 

of a particular open-source but poorly documented modeling 

language. Although this currently makes it impractical for 

BLM district and field offices to construct new models, future 

improvements such as a fully documented, graphical user 

interface (GUI)-based modeling language may make localmodel editing much more feasible. Although ARIES models 

account for local influences on service provision and use, a 

generalized model for each ecosystem service that is globally or nationally applicable has not yet been developed. 

Until global models are ready, ARIES can be run only in its 

case-study regions (southern Arizona, Southern California, 

Colorado, and western Washington as case-studies regions 

containing BLM land). Even for these regions, further model 

testing and refinement would be desirable in order to improve 

the models’ quality and relevance for decisionmaking. ARIES 

currently runs in a command line interface that is not useful for external users; however, access through an internet 

browser was planned by late 2012. As is true for InVEST, 

ARIES currently integrates cross-boundary datasets poorly. 

It was not possible to run these models for the Mexican side 

of the watershed, despite the intent of the ARIES model to 

be able to run under conditions of data scarcity. Improved 

handling of cross-boundary datasets, which will improve 

model results, is planned for future ARIES releases. Future 

releases of ARIES (similar to those of InVEST) intend to link 

to external models, further improving the accuracy and credibility of results.

Other Tools Not Tested in the Pilot Study

We were unable to test a number of other ecosystem services tools within the scope of this pilot study (table 5). Some 

of these tools are proprietary methods that could be used for 

high-profile decisions where contracting with consultants is 

feasible, but for which repeated bureau-wide application is not 

likely (for example, EcoAIM, EcoMetrix, ESValue, NAIS). 

For details about these tools’ strengths, weaknesses, and application to the San Pedro River watershed, see BSR (2011).

Other approaches require intensive, place-specific 

modeling that would be too expensive and time consuming to 

apply to bureau-wide decisionmaking (for example, Envision, 

Ecosystem Portfolio Model). However, these tools could be 

Figure 15. Potential steps in assessing ecosystem services.

Landscape-scale

modeling and

mapping

ARIES

EcoAIM

EcoServ

Envision

EPM

InFOREST

InVEST

MIMES

SolVes

Ecosystem

services impact

screening

ESR

UNEP−WCMC ES

toolkit

Nonmonetary

valuation

EcoAIM

ESValue

SolVES

Monetary

valuation

NAIS

SERVES

Wildlife Habitat

Benefits

Estimation Toolkit

Site-scale

modeling

EcoMetrix

Level of detail in ecosystem services assessment
Conclusions 53

quite informative if they had previously been developed in 

areas where BLM wanted to bring ecosystem services assessment into the decision process.

The remaining tools were at too early a stage of development to allow independent application to the San Pedro River 

watershed. Although its models are open source, as of early 

2012 MIMES had limited documentation, requires proprietary modeling software, and is too complex to run without 

contracting with its developer. SolVES lacked local primary 

survey data and has not yet been used in a wide enough 

variety of case studies to test the capacity of its value-transfer 

functions. As SolVES is used more, and if the accuracy of 

its value transfer approach can be demonstrated, it could be 

widely used by BLM. EcoServ’s online interface and promised seamless integration of data and models could make it a 

useful tool for BLM as well. However, in early 2012 EcoServ 

was at too early a stage of development for testing or use. The 

SERVES valuation tool, planned for release as a subscriptionbased service, could be a useful economic valuation database 

for BLM, but it was under development as of early 2012. A 

booklet describing the UNEP-WCMC Ecosystem Services 

Toolkit was released in late 2011, but full guidance on the 

toolkit remained in development at the conclusion of this 

pilot study. Finally, InFOREST is another web-accessible 

ecosystem service modeling approach that could be used by 

agencies such as BLM but as of early 2012 is planned only for 

use in the Eastern United States. Although it may be useful for 

eastern land managers, InFOREST is unlikely to be a relevant 

tool for BLM unless its models are adapted for western states.

Although none of these tools were used during this pilot 

study, follow-up studies could reconsider their relevance for 

BLM as their capability, documentation, stability, and generalizability improve with time.

Lessons Learned in the Valuation Process: 

Steps to Follow and Avoid

All model developers face tradeoffs. Is it better to 

accurately capture locally important factors that influence 

ecosystem service values but sacrifice generalizability to 

other locations? Or is it better to be generalizable but produce 

results that neglect important local factors and potentially sacrifice accuracy? Although ARIES uses a more modular modeling approach to better account for local processes, its data 

and models are not yet available for the entire Western United 

States. InVEST models, on the other hand, may be used anywhere but may not account for locally important ecological 

factors. The planned approach of ARIES and EcoServ—generalized, intentionally simple global models for coarse-scale 

analysis, combined with locally specific case-study models—

is one way around this problem. However, neither of these tool 

features were available for this pilot study. Similarly, InVEST 

tier 2 models may better account for local processes but were 

not available for the pilot study. Place-specific tools such 

as Envision and the Ecosystem Portfolio Model model only 

local conditions rather than provide a generalizable model, 

which limits their bureau-wide value for BLM but improves 

their scientific and policy relevance in places where they have 

been developed.

It is important to run spatial ecosystem service models 

such as InVEST and ARIES at the highest feasible spatial 

resolution within the confines of time and computer processing requirements. Land cover and carbon storage compared 

manually (by using data at 30 x 30-meter resolution) showed 

storage to be greatest in year 2000, lowest in the year 2020 

open scenario, and intermediate in the year 2020 constraineddevelopment scenario. However, the InVEST carbon model 

run at 100 × 100-meter resolution showed the greatest level 

of carbon storage in the 2020 open-development scenario, 

a nonintuitive result. The resampling process tends to lose 

individual high-value pixels and, through this averaging 

process, the value of ecosystem services analyzed at coarse 

spatial resolution is likely to be less than the value produced 

by a fine-scale analysis (Konarska and others, 2002). Running 

InVEST at 30-meter resolution produced the expected results, 

as reported in the Results section. BLM and other users need 

to be aware that overly coarse scale analysis could lead to 

incorrect conclusions, such as the (incorrect) finding that 

carbon storage is maximized in an open-development scenario 

as described above.

As previously noted, some scenarios are much easier 

to run in existing ecosystem service models than others. 

For tier 1 InVEST models, land-use and land-cover change 

drives ecosystem services, and for the water-augmentation 

scenario, a good understanding of how water augmentation 

affects land cover was lacking. It is reasonable to expect a 

shift from mesquite- and tamarisk-dominated to cottonwoodwillow-dominated riparian communities, given restoration of 

shallow groundwater, surface flows, and vegetation management. Eventual senescence of mature cottonwood trees on 

the San Pedro River is also reasonable to expect, because 

the stream channel has not migrated across the floodplain in 

recent decades, and thus opportunities for establishment of 

new cottonwood stands are limited (Dixon and others, 2009). 

However, the lack of spatial data on the extent of riparian 

community types and how they would be expected to differ 

in terms of ecosystem service delivery, made it impractical 

to run InVEST models for this type of scenario. The greater 

flexibility of the ARIES model allowed it to accommodate 

water-augmentation scenarios in the recreation models. This 

accommodation shows the importance of understand how 

potential scenario changes will interact with ecosystem service 

models given the primacy of land-use and land-cover change 

as a driver of ecosystem services in the tier 1 InVEST models. 

BLM, The Nature Conservancy, and BOR continue work on 

optimal hydrologic restoration scenarios, and their guidance 

can facilitate better ecosystem service modeling for water 

augmentation scenarios.
54 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Integration of Past Work on the San Pedro River 

Watershed into Ecosystem Service Models

Although the San Pedro River watershed was selected 

for its large body of past research, much of this scientific 

knowledge was not useful for parameterizing the ecosystem 

service models that were tested. This finding has been 

documented by others; even for areas whose ecology is 

well understood and richly documented, that knowledge is 

not always the type needed to support ecosystem service 

modeling, mapping, valuation, and decisionmaking (Norgaard, 2010). Ecosystem services researchers can improve 

this situation by collaborating with disciplinary researchers to 

fully understand the state of disciplinary research, incorporate 

existing models where they are appropriate, and build new 

models that reflect the state of the ecological science. Where 

research gaps prevent disciplinary science from supporting 

ecosystem services assessment and valuation, these research 

needs should be articulated to the scientific community. Such 

collaboration can help to integrate existing work and develop 

new research to better understand the ecological cause and 

effect that underlie ecosystem service production functions 

(Daily and others, 2009). The US EPA–ORD is beginning to 

catalog ecological production functions for use in ecosystem 

services modeling, which could inform future modeling efforts 

and tradeoff quantification for a number of the ecosystem 

services tools.

Ecosystem service tools are designed to be relatively 

simple in their application, to facilitate quantification of tradeoffs between services, and to link to human beneficiaries. Yet 

in some contexts discipline-specific biophysical models may 

be more appropriate: specific hydrologic models such as Soil 

and Water Assessment (SWAT), Variable Infiltration Capacity 

(VIC), Water Evaluation and Planning (WEAP), or Hydrologic 

Engineering Center–River Analysis System (HEC–RAS) 

and large-scale carbon modeling efforts such as the USGS 

LandCarbon program (Zhu and others, 2010). Where such 

resources and modeling expertise are available, disciplinespecific models may better quantify ecosystem services. When 

one chooses an ecosystem service tool, deterministic models 

may be more appropriate in regions with more data, whereas 

probabilistic approaches (such as those in ARIES) may be 

more appropriate in areas with sparse data and high uncertainty (Vigerstol and Aukema, 2011).

Nonmonetary and Tribal Values

The San Pedro River watershed holds immeasurable 

significance to numerous American Indian tribes (Ferguson 

and Colwell-Chanthaphonh, 2006). As of early 2012, Fort 

Huachuca consulted with 11 Federally recognized tribes that 

have a cultural affiliation with the San Pedro Valley (Marty 

Tagg, Fort Huachuca, written commun., 2012). Viewed within 

the westernized construct of ecosystem services, American 

Indians could be said to value the San Pedro River watershed 

for cultural, symbolic, spiritual, and historic reasons, along 

with its many provisioning services.

A direct assessment of how tribes value the environment 

and ecosystem services was beyond the scope of this project, 

but even so the ecosystem services tools and methods evaluated for this project were not amenable to estimating tribal 

values. Tribes may not be willing to share with outsiders 

the information needed to map key points on the landscape, 

preventing the use of spatial mapping tools for ecosystem 

services. However, tribes might be amenable to indicating and 

possibly ranking their preferences for landscape-change alternatives and tradeoffs between ecosystem services as measured 

in biophysical units. Tools that are designed to incorporate 

nonmonetary stakeholder preferences (for example, EcoAIM, 

ESValue, SolVES, and Envision) could aid in understanding 

tribal values for ecosystem services, although these tools were 

not tested as part of this study.

San Pedro Riparian National Conservation 

Area Outcomes

Our results demonstrate how ecosystem services 

could be used by a BLM district or field office to aid in 

decisionmaking. Before undertaking this modeling exercise, 

local stakeholders held that urban growth, particularly at low 

densities, would entail some loss of ecosystem services, that 

mesquite management would involve tradeoffs between carbon, 

water, and habitat quality, and that water augmentation of 

the San Pedro River would provide some ecosystem services 

benefits. However, SPRNCA managers lack jurisdiction on 

basin-wide urban growth choices, and even county planners 

may desire information on ecosystem services changes at 

smaller spatial scales (for example, to measure the impacts of 

development of a particular parcel of land). InVEST and ARIES 

are landscape-scale models that can be run at smaller scales 

such as subwatersheds, and their results can be clipped to measure smaller-scale impacts. To truly measure site-scale changes 

in ecosystem services (for example, on alternative development approaches to the same parcel), a site-scale tool such as 

EcoMetrix is most appropriate.

Rising demand for ecosystem services can increase their 

value, even as ecosystems are being degraded. ARIES viewshed 

and proximity results are good examples: in both urbanization 

scenarios, landscape quality declined while ecosystem service 

use went up, as more users were present on the landscape. It is 

thus important that rising ecosystem service values not be automatically equated with improvements in ecosystem quality.

Our results map and quantify ecosystem services tradeoffs and provide monetized values for some services. All 

scenarios illustrate some degree of uncertainty in the results, 

on the basis of the models themselves and the economic 

values and discount rate applied. For scenarios such as 

mesquite management, the total value ranges from a loss to 
Conclusions 55

a gain in annual value, depending on how water and carbon 

are valued. Because of this uncertainty and the inability to 

monetize habitat quality, incorporation of economic values 

into a strict cost-benefit framework for decisionmaking is 

likely inappropriate for some management decisions. Using 

water augmentation scenarios with the InVEST and ARIES 

ecosystem service models was difficult. Well-defined scenarios 

and spatial and ecological data to support them are important 

in order to successfully model ecosystem-service change in 

scenarios. BLM and others are working to define optimal 

flows to better understand the costs and benefits of water augmentation under alternative scenarios, which could produce 

better input data for ecosystem service models in the future.

Given the large effort needed to develop and apply these 

tools to southeast Arizona, USGS will supply BLM national, 

State, district, and field offices with the data and models developed for this project. However, local users will likely need 

some training and support to use these models independently. 

Models continue to be developed and tested by USGS, which 

can provide BLM district and field offices the opportunity to 

learn about and apply locally appropriate models to management questions. Additionally, State, Gila District, and BLM 

field offices would benefit from further guidance on how and 

when to apply ecosystem services in decisionmaking, and 

particularly how to deal with valuation and uncertainty. Guidance on technical, economic, and policy matters related to 

ecosystem services can likely be provided to BLM district and 

field offices by National office, DOI Office of Policy Analysis, 

and USGS, among others. At the conclusion of this pilot, the 

Arizona, Gila District, and SPRNCA offices have the opportunity to lead BLM in applying ecosystem services toward 

decisionmaking, but they would benefit from further technical 

support, training, and policy guidance to be able to use these 

tools in regular decisionmaking.

Agency-Wide Outcomes

Quantifying, mapping, and valuing ecosystem services 

offers BLM a promising way to quantify and communicate 

resource management tradeoffs, particularly when resource 

extraction could degrade ecosystem services. Ecosystem 

services can enable BLM to better address the “tragedy 

of ecosystem services” (Lant and others, 2008) or the 

“macroallocation problem” between ecosystem structure 

and function (Farley, 2008), where commodities or ecosystem 

goods are overconsumed at the expense of ecosystem services. Ecosystem services need not be analyzed for every 

decision throughout the bureau. Ecosystem services may be 

of use in an analysis when (1) substantial social or environmental effects are likely (as identified in the NEPA process); 

(2) a substantial tradeoff may exist between preservation of 

ecosystem services and extraction of market goods (oil, gas, 

coal, minerals, timber, grazing); or (3) the proposed change is 

large (Winthrop, 2011).

The tools evaluated in this report differ in their appropriateness for use by BLM and in the value of their outputs for 

decisionmaking (tables 25–26). The mid-2012 bureau-wide 

readiness of these tools and methods is summarized below. 

Given the rapid state of evolution in the field, new tools may 

appear on the landscape and the capabilities of existing tools 

may change, so periodic review of tools of particular interest 

to BLM is desirable.

• Feasible for immediate bureau-wide use by BLM: 

• Ecosystem Services Review, UNEP-WCMC 

Ecosystem Services Toolkit, Wildlife Habitat 

Benefits Estimation Toolkit.

• Potentially feasible for bureau-wide use by BLM given 

development of supporting databases for spatial data 

and literature:

• function transfer,

• InVEST, point transfer,

• application of past primary valuation studies.

• Potentially feasible for bureau-wide use by BLM given 

pending development of global models or expanded 

underlying datasets:

• ARIES, 

• EcoServ,

• SolVES.

• Proprietary tools, feasible for use in high-profile cases 

where contracting with consultants or developers, or 

paying for a subscription is possible:

• EcoAIM

• EcoMetrix

• ESValue

• NAIS

• SERVES

• Open-source tools that are place specific, require a 

long lead time to develop, and require contracting with 

universities or consultants. If models have been previously developed for an area of interest they could be 

immediately applied:

• Ecosystem Portfolio Model

• Envision

• InFOREST

• MIMES

BLM has made at least one previous effort to quantify 

ecosystem services prior to this pilot study. BLM’s California 

State Office contracted with Spatial Informatics Group to use 

NAIS to estimate ecosystem services in Napa, Humboldt, and 

San Bernardino counties in association with the State’s fire 

management program (TSS Consultants, 2005). The intent 

of the study was to provide monetary values for ecosystem 
56 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

services to compare with cost estimates for firefighting, fuels 

management, and property loss from wildfire. The study used 

point transfer, identified land-cover types in each county and 

assigned ecosystem service values from the literature based 

on these land-cover types. The California BLM noted that 

ecosystem services have not yet been an effective argument in 

increasing funding for their fire and fuels management program, 

and that the state, district, and field offices are waiting for 

national guidance on how and when to use ecosystem services 

in decisionmaking. Despite these limitations, the study did 

provide potentially useful information to inform land management and funding allocation decisions, and it has been used 

internally by California BLM. The study would have been more 

useful had it provided values for the entire state, rather than just 

three counties, and California BLM would have preferred not to 

have to contract out the work. Because national-level data may 

be too imprecise to inform local decisionmaking, giving BLM 

state, district, and field offices the tools and guidance to run 

consistent analyses would be highly desirable (Craig Barnes, 

California State BLM, oral commun., 2010). Although the 

California study did not result in on-the-ground changes in 

ecosystem-services-based management for BLM, it further 

demonstrated the importance of this pilot study and the need for 

national-level guidance on ecosystem services to be provided to 

BLM state, district, and field offices.

Implications for Future Work

Incorporating Ecosystem Service and Value 

Information into the Decision Process

The promise of using ecosystem service assessment 

tools to support decisionmaking, particularly within BLM’s 

NEPA and RMP processes, rests on being able to systematically measure impacts in a way that is credible, quantifiable, 

and replicable. Ecosystem service impacts may already be 

described qualitatively without the use of valuation methods 

or models. However, the goal is to present results as a change 

in ecosystem services measured in biophysical units, weighted 

preferences, or dollars for baseline conditions and for potential 

management scenarios. Quantified changes as presented in 

the Results section of this report are thus desirable. Maps of 

impacts, tradeoffs, and values can facilitate clearer communication to decisionmakers and the public, and they can be an 

important addition to the decision process.

Given the complexity inherent in modeling and valuing ecosystem services and the desire to incorporate them 

into decisionmaking, it makes sense to estimate and convey 

uncertainty in model outputs and valuation results. Reporting a single value can inspire false confidence in the certainty 

of results. Many primary valuation studies report a range 

of values, and function transfers report standard errors for 

regression coefficients. ARIES reports uncertainty associated with Bayesian network models and can use Monte Carlo 

simulation. ESValue can also use Monte Carlo simulation to 

estimate and report uncertainty. It would be highly useful for 

model developers to know what uncertainty metrics are most 

valuable to decisionmakers, and how uncertainty is or can be 

used in decisionmaking. Are outcomes with a highly uncertain 

but large gain in ecosystem service provision preferable over 

a small gain with greater certainty? A better understanding of 

how to communicate and use of uncertainty information would 

thus be desirable for the entire ecosystem services science and 

policy community. Such guidance might be best provided by 

BLM and DOI’s Office of Policy Analysis.

Along with uncertainty, ARIES reports a number of 

outputs to fully map spatial flows of ecosystem services, in 

some cases as many as 16 maps for each ecosystem service’s 

theoretical, possible, actual, inaccessible, and blocked service 

provision, sinks, uses, and flows (Bagstad and others 2011; 

Johnson and others, 2012). As is true for reporting of uncertainty estimates, such outputs will need to be reviewed and 

considered in decision contexts so that they can be presented 

in a way that adds value to the decision process rather than 

leaving a potential user “drowning in data.”

It would be also useful to better define the specific 

ecosystem service metrics that are most useful for economic 

valuation and decisionmaking. The lack of clearly defined 

metrics is a broad issue faced by researchers throughout the 

field of ecosystem services who are working to better quantify 

ecosystem services tradeoffs to inform resource management 

(Boyd and Krupnick, 2009). Some outputs of the San Pedro 

River watershed modeling exercise, particularly for water 

yield, translated poorly into metrics that were amenable to 

valuation. Models that can better quantify ecological endpoints will generally be more useful for economic valuation. 

Few ARIES and InVEST outputs are directly comparable. For 

carbon, for instance, it would be useful to better know which 

metrics decisionmakers found to be most useful: carbon storage and storage change through time (reported in InVEST), 

potential stored carbon release, carbon sequestration, and 

greenhouse gas emissions (reported in ARIES), or some combination? In each case, a better understanding of what BLM 

and other users need to support decisionmaking and how tools 

can better meet those needs would be helpful. The concept of 

ecological endpoints or “final ecosystem goods and services” 

is gaining preliminary consensus (Boyd and Banzhaf, 2007; 

Nahlik and others, 2012) as metrics that can be measured or 

modeled. Final Ecosystem Goods and Services link directly to 

green accounting frameworks and avoid the problem of double 

counting of benefits that has plagued past efforts at quantifying ecosystem services. This approach also meshes well with 

the beneficiaries-centric approach taken by ARIES (Villa and 

others, 2011).

Lastly, consistent application of models or tools that 

produces replicable results is important to maintain credibility in ecosystem service valuation, because the tools used to 

estimate these values are newer and less well established than 

nonmarket valuation methods or biophysical-process models. 

Using consistent data sources, approaches to tool applications, 

and reporting of results is critical. It also means that consistent 
Conclusions 57

use of generalizable tools such as those tested in this pilot 

study may be of more value bureau-wide than a series of 

independent, special-purpose tools applied to multiple sites at 

regional scales, where cross-comparison and development of 

best practices for each tool becomes less feasible.

Data Sharing to Support Widespread Ecosystem 

Services Mapping and Valuation

A system of data sharing for spatial data, ecological 

studies needed to parameterize the ecosystem service models, and economic studies needed to conduct valuation would 

immensely aid ecosystem service valuation efforts within 

BLM. The time spent on this pilot study would have been substantially reduced had such resources been available. Although 

such a system could be developed by and housed within BLM, 

it might be more sensible to pool resources with other Federal 

agencies and departments to create a system for ecosystem 

services-based decisionmaking on public lands for all agencies 

within the Department of the Interior, perhaps housed within 

USGS but ideally also including agencies outside the Department of the Interior that are involved in resource management 

and ecosystem services research (such as the National Oceanic and Atmospheric Administration (NOAA), USDA Forest 

Service, US EPA, and DOD). Spatial data underlie nearly all 

ecosystem service modeling and valuation tools. Although the 

USGS houses abundant public data on land cover, hydrology, 

and geology, and other sources such as the Natural Resources 

Conservation Service (NRCS) Spatial Data Gateway offer 

one-stop locations for data downloads, no single site contained all the spatial data needed to run the ecosystem service 

models (appendixes A, B). Collecting, storing, and preprocessing relevant spatial data in a single location could save 

future users substantial time and effort. In this regard, the 

ARIES approach to handling spatial data is sophisticated and 

worth further investigation. ARIES uses a GeoServer that can 

call on annotated spatial data to support multiple ecosystem 

service models, and it also automatically handles simple GIS 

operations such as rasterization, resampling, reprojection, and 

reclassification, which would otherwise require substantial 

time and expertise by a GIS technician. Preparing data to 

run the InVEST models, for instance, was the second most 

time-consuming part of running those models, aside from the 

substantial time required to collect the ecological data needed 

to parameterize the InVEST data tables.

Ecosystem service valuation databases have been completed in the past (McComb and others, 2006). The Ecosystem 

Services Database (Villa and others, 2002) was a National 

Science Foundation–funded valuation database, but it has not 

received funding for maintenance and its underlying database 

has not been updated in nearly 10 years, a problem common 

to ecosystem-based management tools (Curtice and others, 

2012). The NAIS and the SERVES databases are both outgrowths of the ESD. NAIS is a proprietary database and is 

not available for public access, and a subscription service is 

planned to provide access to SERVES. The EVRI database has 

been relatively well-maintained, but searches of this database 

for this pilot project revealed that it is missing a number of 

key valuation studies for the Western United States. A 2011 

release of the database also eliminated the ability to search for 

studies by state in the United States, making searches more 

cumbersome and time consuming. Each of these databases 

thus has its own limitations. However, with better support and 

cooperation from Federal agencies, expansion and updating of 

such databases could become more feasible.

Just as databases cataloging primary economic studies 

can support valuation, databases could also be constructed 

for the ecological studies needed to support modeling efforts. 

As we better understand the data needs for major ecosystem 

service models, it would be valuable to have a database that 

can be used to identify key ecological parameters to support 

such models. For instance, the tier 1 InVEST models link 

ecosystem service provision to land use and land cover by 

means of tables. Having accurate coefficients (for example, 

for carbon storage, rooting depth, nutrient loading, and 

evapotranspiration coefficients by land-use and land-cover 

type) for use in these tables is needed to run the models and 

obtain credible results. ARIES offers flexibility in its capacity 

to build and use models for local cases where influences on 

ecosystem services supply and demand vary. However, this 

modular approach and the ARIES global models under development needs to be underlain by relevant ecological research, 

particularly to identify relevant ecological production functions (Daily and others, 2009). As previously mentioned, 

US EPA–ORD’s work on ecological production functions may 

be helpful for modelers.

If centralized sources of spatial data and underlying 

ecological and economic knowledge were available, it would 

be feasible for BLM district and field offices to regularly run 

more complex ecosystem service models with greatly reduced 

resource requirements.

Follow-Up Studies

At the beginning of this project, BLM and USGS agreed 

that it would be useful to identify a second site for a companion study, preferably in a region with greatly different ecological and socioeconomic characteristics than southeast Arizona. 

With the completion of this study, we are now aware that the 

San Pedro’s strong prior research base was not as important 

as initially thought in supporting modeling and valuation. 

Although sophisticated process models have been developed 

linking the San Pedro’s groundwater, surface water, vegetation, and avian habitat (Serrat-Capdevila and others, 2009), 

ecosystem service models as of early 2012 did not support 

integration of external-process models. Future versions of 

several models, however, intend to link accepted hydrologic 

and ecological models with ecosystem services provision 

and use, which would be a major step forward in ecosystem 

service modeling. In this pilot study, the largest contribution 
58 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

from other researchers was their expert assistance in reviewing model inputs (appendix D), rather than in providing a 

library of past studies that seamlessly filled our valuation and 

modeling needs.

As a result, BLM can be less hesitant about attempting to 

use these methods and models in poorly studied environments, 

because all sites can be considered data poor to some degree 

for the purposes of ecosystem service modeling. The data and 

models assembled and documented for this project can assist 

future researchers, particularly those working on ecosystem 

services-based decisionmaking for public lands. Since the 

results of this project represent state-of-the-modeling and 

valuation literature as of mid-2012, and because of the rapid 

evolution and proliferation of models, periodic follow-up 

projects would be sensible.

Aside from these projected future improvements to ecosystem services tools, other tools may offer improved features 

and documentation, and growth of the valuation literature will 

offer continual, even if slow, change in the state of the science. 

Given the speed of evolution in the science of ecosystem services, a periodic (annual to biennial) review of the science as it 

applies to decisionmaking on public lands would be desirable 

both for BLM and other public land management agencies. 

Such reviews could take the form of more focused case studies that explore particular nuances of new tools and methods, 

rather than the broad survey undertaken for this initial study.

Lessons learned from this project are applicable for 

decisionmakers on public land outside of BLM. Given the 

importance of ecosystem services for a variety of Federal 

departments and agencies (for example, DOD, NOAA, DOI, 

USDA, and US EPA), BLM need not necessarily shoulder all 

the burden of testing tools for all Federal agencies. Ideally, 

Federal agencies would share funding, research responsibilities, and scientific findings; in return, well-documented data, 

case studies, and lessons learned could be shared throughout 

departments and agencies with a vested interest in facilitating ecosystem-services-based management. At a minimum, 

DOI, with USGS as the lead scientific agency, could implement such sharing internally although participation of other 

departments and agencies is desirable. Emerging efforts 

such as the National Ecosystem Services Partnership (http://

nicholasinstitute.duke.edu/ecosystem/nesp), which brings 

together Federal and non-Federal researchers, practitioners, 

and decisionmakers, could also provide a platform for such 

collaboration, data sharing, and periodic reviews of the state of 

ecosystem service tools.

Acknowledgments

Chris Colvin, Driss Ennaanay, Gary Johnson, 

Heather Tallis, Ferdinando Villa, Brian Voigt, and Stacie Wolny 

greatly assisted with data and model development for the ARIES 

and InVEST models. Additionally, numerous other individuals 

assisted the coauthors of this report with project scoping, model 

review, and report development. A full list of these individuals is 

provided in appendix D.

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Appendixes

Appendix A 69

Appendix A. Spatial Data and Assumptions Used in InVEST Models

Martens and McLain (2005) and Martens and others (2005) 

provide soil carbon storage data for western agricultural and 

rangeland ecosystems in general and for a single specific site 

on the San Pedro River, respectively. However, Schlessinger’s 

(1982) study was an extremely comprehensive statewide study 

so for the purpose of providing generalizable rather than sitespecific data to use at the watershed scale, we chose to use this 

study rather than those by Martens and McLain.

Much of the carbon literature, at least for the arid 

Southwest (for example, Huxman and others, 2004; Svejcar 

and others, 2008; Scott and others, 2009), reports carbon 

fluxes, net ecosystem exchange, net primary productivity, or 

carbon sequestration rather than carbon storage, which is the 

input data required by the InVEST carbon model. References 

such as McLain and Martens (2006) report carbon storage in 

the soil in grams of carbon per kilogram of soil, which is not a 

useful measurement unless soil bulk density is also reported. 

The large number of zeroes in the input data table (table A5) is 

problematic. Many carbon pools lacked data, meaning that the 

amount of carbon actually stored by ecosystems is uncertain 

and likely to be underestimated. Still, the resulting values 

for relative change in carbon pools can be informative for 

decisionmaking.

To value carbon storage, we used a set of conservative 

valuation assumptions ($22/ton social cost of carbon (2011 

United States dollars), 7 percent discount rate, 0 percent 

rate of annual change in the value of carbon, median values 

from Tol’s (2005, 2008) meta-analyses) and a set of nonconservative valuation assumptions ($89/ton social cost of 

carbon, 1 percent discount rate, 6 percent annual change in 

the value of carbon, for the most part based on Stern, 2007). 

The value of carbon and the assumptions that go into these 

estimates are debated in the literature, and even high values 

may underestimate the social cost of carbon (Ackerman and 

Stanton, 2012). However, these estimates do provide defensible high and low values. We did not use market prices for 

carbon owing to the extreme fluctuation in prices on European 

markets and the now-defunct Chicago Climate Exchange, due 

largely to the artificial constraints imposed on these markets.

The InVEST aesthetic-quality model used features identified by Steinitz and others (2003) as contributing to visual 

blight or undesirable view features, such as mines, transmission lines, highways, and development. We rasterized all data 

and combined them into a single layer, then converted the data 

to points. When these data were combined with a digital elevation model and population counts raster data for the San Pedro 

River watershed, the model produced results that identify the 

population with views of a given number of blight features 

along with areas of the landscape that have a given number of 

blight features visible.

The InVEST water model used root-depth data for key 

species for which measured rooting depths were available 

within each of the land-use–land-cover types (data from 

Canadell and others, 1996; and Schenk and Jackson, 2002a,b) 

This appendix describes the data sources and assumptions 

used to populate the InVEST biodiversity, carbon, aesthetic 

quality, and water yield and demand models for the San Pedro 

River watershed. Table A1 lists spatial data sources used for 

the San Pedro application of the InVEST models, and table A2 

describes the general data needs, both spatial and nonspatial, 

of the InVEST models described by Tallis and others (2011).

The InVEST biodiversity model used the 30-class 

Steinitz and others (2003) land-use–land-cover layer to 

estimate baseline habitat quality for year 2000 and year 2020 

open- and constrained-development scenarios. Five items 

mapped drivers of biodiversity change: (1) highways (because 

they fragment habitat and act as vectors for movement of 

invasive species), (2) a density layer of groundwater wells 

(representing the impacts of groundwater pumping), (3) the 

United States–Mexico border fence (for its habitat fragmentation), (4) Arizona State Trust land grazing allotments (representing the effects of grazing), and (5) mountains at elevations 

over 2,000 meters (potentially susceptible to loss of alpine 

communities owing to climate change). The intensity of the 

effects of these drivers of change was based on land ownership, by using values of 0.1 for land owned and managed by 

NPS, The Nature Conservancy, and BLM and USDA Forest 

Service wilderness (including the SPRNCA), military, and 

tribal lands; 0.33 for county land and nonwilderness BLM and 

national forest land; 0.67 for State trust lands and ejidos (communally owned parcels of land, primarily used for agriculture, in Mexico); and 0.9 for private land. Model coefficients 

linked land use and land cover to the various drivers of change 

listed in tables A3 and A4. Like all InVEST coefficient tables, 

these were reviewed by a group of researchers who work in 

Southwestern ecosystems. These tables have been reformatted here for presentation purposes, and users who wish to use 

them as inputs to the InVEST models must alter table headings to conform to those supplied in the InVEST user’s guide 

(Tallis and others, 2011).

The InVEST carbon model used the 10-class Steinitz and 

others (2003) land-use–land cover-data for the year 2000 baseline and year 2020 open- and constrained- development scenarios. Estimating carbon storage for these 10 land-use–landcover types was challenging, because data on carbon storage 

by Southwestern ecosystems are sparse. Standard references 

such as J.E. Smith and others (2006) are more useful for forested ecosystems in other parts of the United States, although 

their data were used for aboveground and woody debris carbon 

storage in montane forests. We took the average of tables A34, 

A36, and A38 from J.E. Smith and others (2006), assuming 

65-year old stands, and used this value for montane and riparian forests, half this value for oak woodlands, and one third of 

this value for mesquite woodlands. We drew aboveground and 

belowground carbon storage in desert scrub from Schlessinger 

and others (2009). We used soil carbon data for all ecosystem 

types except urban from Schlessinger (1982), and used Kaye 

and others (2008) for data on urban soil carbon storage. 
70 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Table A1. Data used in InVEST (integrated valuation of ecosystem services and tradeoffs) models, San Pedro River watershed, Arizona.

[--, not applicable; m, meter; U.S. EPA, U.S. Environmental Protection Agency]

Layer Source Resolution Extent Year

Biodiversity

Current land use and land cover Steinitz and others (2003) 60×60 m Upper San Pedro River watershed 2000, 2020

Urban growth scenarios Steinitz and others (2003) 60×60 m Upper San Pedro River watershed 2000, 2020

Mesquite management scenarios Derived from Steinitz and others (2003) 60×60 m Upper San Pedro River watershed 2000

Access to biodiversity drivers 

of change

Arizona Geographic Information Council land 

ownership and U.S. EPA San Pedro Data 

Browser data, Mexico

Vector shapefile Arizona and 

Sonora San Pedro River watershed

--

Biodiversity drivers of change

Groundwater wells Arizona Department of Water Resources Well 

Registry, Instituto de Ecologia

100×100 ma Arizona and 

Sonora San Pedro River watershed

2010

Highways Arizona Geographic Information Council, EPA 

San Pedro Data Browser

Rasterized 

vector dataset

Arizona and 

Sonora San Pedro River watershed

2000

Grazing allotments State lands, selected from state public lands data Vector shapefile Arizona --

Border fence Digitized layer along United States–Mexico 

border

Vector line data San Pedro River watershed --

Climate change Areas above 2,000 m elevation 90×90 m San Pedro River watershed --

Carbon

Current land use and 

land cover

Steinitz and others (2003) 60×60 m Upper San Pedro River watershed 2000, 2020

Urban growth scenarios Steinitz and others (2003) 60×60 m Upper San Pedro River watershed 2000, 2020

Mesquite management scenarios Derived from Steinitz and 

others (2003)

60×60 m Upper San Pedro River watershed 2000

Aesthetic quality (viewshed)

Digital elevation model Shuttle Radar Topography Mission 90×90 m San Pedro River watershed --

Population counts map LandScan/Oak Ridge National Laboratory Global 30 arc-second2 2006

Visual blight features

Highways Arizona Geographic Information Council, U.S. 

EPA San Pedro Data Browser

Converted to points 

and merged with other 

blight features Arizona 

Upper San Pedro 

River watershed

Arizona and 

Sonora San Pedro River watershed

2000

Transmission lines Topologically Integrated Geographic Encoding 

and Referencing system (TIGER)/Line files

2000

Development and mines Steinitz and others (2003) 2000, 2020

Water

Current land use 

and land cover

Steinitz and others (2003) 60×60 m Upper San Pedro River watershed 2000, 2020

Urban growth scenarios Steinitz and others (2003) 60×60 m Upper San Pedro River watershed 2000, 2020

Mesquite management scenarios Derived from Steinitz and others (2003) 60×60 m Upper San Pedro River watershed 2000

Digital elevation model Shuttle Radar Topography Mission 90×90 m San Pedro River watershed --

Soil depth STATSGO Vector shapefile Arizona --

Annual precipitation PRISM Climate Group 4×4 km San Pedro River watershed 2002,b 2007c

Plant available water content STATSGO Vector shapefile Arizona --

Average annual potential 

evapotranspiration

Consultative Group on International Agricultural 

Research— Global Aridity and Potential 

Evapotranspiration Database

30 arc second San Pedro River watershed 1950–2000

Watershed boundaries Arizona Department of Environmental Quality Vector shapefile Arizona --

a

Point density map of wells per square kilometer.

b

Representative dry year.

c

Representative wet year

(table A6). The evapotranspiration coefficient value for riparian species came from Scott and others (2008). For other 

land-use–land-cover types, alfalfa was set equal to 1,000 and 

all other types equal to the mean annual precipitation received 

by that vegetation type in the San Pedro River watershed, on 

the basis of the spatial extent of each vegetation type. These 

data were overlaid on Parameter-elevation Regressions on 

Independent Slopes Model (PRISM) 30-year mean annual 

precipitation data. The water yield models were run with 

rainfall data from 2002, a relatively dry year, and 2007, a 

relatively wet year, rather than with mean annual precipitation data. Average precipitation values are less meaningful 

in arid climates than in humid climates, which typically have 

less interannual variation in precipitation. Values for water 

demand by land-use–land-cover types (table A7) were derived 

by multiplying population times per capita use (Arizona 

Department of Water Resources, 2005) and then dividing the 

product by the area of developed land in the watershed. This 
Appendix A 71

Table A2. Data needs for other InVEST models.

[--, not applicable; R, required; O, optional; see Tallis and others (2011) for details]

Types of data Biodiversity Carbon

Reservoir 

hydropower

Nutrient 

retention

Avoided

reservoir

sedimentation

Timber

production

PollinationWave

energy

Coastal

vulnerability

AquacultureViewsheds

Spatial data

Current land use and land cover R R R R R -- R -- -- -- --

Future land use and land cover O O -- -- -- -- O -- -- -- --

Sources of biodiversity threats R -- -- -- -- -- -- -- -- -- --

Accessibility to biodiversity threats O -- -- -- -- -- -- -- -- -- --

Timber harvest rates, present and future -- O -- -- -- -- -- -- -- -- --

Digital elevation model -- -- R R R -- -- -- R -- R

Soil depth -- -- R R -- -- -- -- -- -- --

Average annual precipitation -- -- R R -- -- -- -- -- -- --

Plant available water content -- -- R R -- -- -- -- -- -- --

Average annual potential evapotranspiration -- -- R R -- -- -- -- -- -- --

Watershed boundaries -- -- R R R -- -- -- -- -- --

Subwatershed boundaries -- -- -- R R -- -- -- -- -- --

Rainfall erosivity index ‘r’ factor -- -- -- -- R -- -- -- -- -- --

Soil erodibility ‘k’ factor -- -- -- -- R -- -- -- -- -- --

Timber production parcels -- -- -- -- -- R -- -- -- -- --

Wave energy data -- -- -- -- -- -- -- R -- -- --

Landing and power grid points -- -- -- -- -- -- -- O -- -- --

Population density -- -- -- -- -- -- -- -- R -- R

Kelp locations -- -- -- -- -- -- -- -- O -- --

Seagrass locations -- -- -- -- -- -- -- -- O -- --

Sea level rise polygon -- -- -- -- -- -- -- -- O -- --

Shoreline type -- -- -- -- -- -- -- -- R -- --

Land polygon and polyline -- -- -- -- -- -- -- -- R -- --

Fish farm locations -- -- -- -- -- -- -- -- -- R --

Visual features points -- -- -- -- -- -- -- -- -- -- R

Tabular data (typically requiring literature searches or expert input)

Biodiversity threats R -- -- -- -- -- -- -- -- -- --

Habitat types and sensitivity to threats R -- -- -- -- -- -- -- -- -- --

Carbon pools by land use or land cover type -- R -- -- -- -- -- -- -- -- --

Market price or social cost of carbon -- O -- -- -- -- -- -- -- -- --

Discount rate -- O -- -- -- O -- -- -- -- --

Evapotranspiration and rooting depth by land 

use and land cover type

-- -- R -- -- -- -- -- -- -- --

Water demand by land use or land cover type -- -- R -- -- -- -- -- -- -- --

Hydro power station attributes -- -- O -- -- -- -- -- -- -- --

Root depth and nutrient loading coefficients 

by land use or land cover type

-- -- -- R -- -- -- -- -- -- --

Threshold flow accumulation value -- -- -- R R -- -- -- -- -- --

Watershed nutrient loading and costs -- -- -- O -- -- -- -- -- -- --

Biophysical sedimentation attributes -- -- -- -- R -- -- -- -- -- --

Slope threshold -- -- -- -- R -- -- -- -- -- --

Reservoir sediment table -- -- -- -- O -- -- -- -- -- --

Timber production table -- -- -- -- -- R -- -- -- -- --

Pollinator species table -- -- -- -- -- -- R -- -- -- --

Pollinator and land-cover attributes -- -- -- -- -- -- R -- -- -- --

Half saturation constant R -- -- -- -- -- O -- -- -- --

Machine performance table -- -- -- -- -- -- -- R -- -- --

Machine parameters table -- -- -- -- -- -- -- R -- -- --

Economic parameter table -- -- -- -- -- -- -- O -- -- --

Exposure index table -- -- -- -- -- -- -- -- O -- --

Wind-wave exposure: Wind vector list -- -- -- -- -- -- -- -- O -- --

Daily water temperature at fish farms -- -- -- -- -- -- -- -- -- R --

Farm operations table -- -- -- -- -- -- -- -- -- R --

Aquaculture valuation parameters -- -- -- -- -- -- -- -- -- O --
72 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

procedure gave a value for human consumption of water, 

which was then divided by the existing acreage of developed 

land. To estimate water consumption by livestock, the total 

number of cattle in Cochise County (U.S. Department of 

Agriculture, 2007) was multiplied by per capita water use 

for cattle (Texas Agricultural Extension Service, [n.d.]) and 

divided by the acreage of state trust land and BLM land used 

for grazing. We then applied this value to mesquite, grassland, 

desert scrub, and riparian land-cover types.

Limitations and Areas for Improvement

The InVEST carbon model would be improved if 

better carbon storage values were available for Southwestern 

ecosystems. Existing carbon storage data do not support 

use of land-use–land cover maps with complex typologies. 

For instance, completing a carbon-pools table based on 

Table A3. Threats to biodiversity, San Pedro River watershed, 

Arizona.

[Decay: 0, exponential decay; 1, linear decay]

Threat

Maximum

distance

(kilometers)

Weight Decay

Roads 0.5 0.5 0

Grazing land 0.25 0.25 1

Groundwater pumping 20 1 1

United States–Mexico border fence 1 0.75 1

Climate change (alpine communities) 0.1 1 1

Table A4. Habitat type and sensitivity to threats, San Pedro River watershed, Arizona.

[Sensitivity: 0, minimum; 1, maximum]

Land use or land cover Habitat Road Graze Pump Fence Climate Code Name

1 Forest 0.8 0.5 0.5 0 0.5 0.5

2 Oak 0.8 0.5 0.5 0 0.5 0

4 Grassland (native) 0.8 1 1 0.1 0.5 0

5 Grassland (introduced) 0.3 1 1 0.1 0.5 0

6 Mesquite 0.4 1 1 0.1 0.5 0

7 Desert scrub 0.4 1 1 0 0.5 0

8 Active agriculture 0.1 0 0 0 0.5 0

9 Inactive agriculture 0.2 0 0 0 0.5 0

10 Urban 0 0 0 0 0 0

11 Suburban 0.1 0 0 0 0 0

12 Rural residential 0.2 0 0.5 0 0.5 0

13 Mines 0 0 0 0 0 0

14 Water 1 0.1 0.75 1 0.5 0

16 Barren 0 0 0 0 0 0

19 Industrial/airport 0 0 0 0 0 0

22 Exurban 0.1 0 0 0 0.5 0

23 Golf course 0.2 0 0 0 0.5 0

25 Commercial 0 0 0 0 0 0

26 Mesquite bosque 0.5 1 1 0.1 0.5 0

27 Cottonwood-willow bosque 0.9 0.1 0.75 1 0.5 0

28 Cottonwood-willow/mesquite bosque 1 0.1 0.75 1 0.5 0

the Southwest Regional Gap Analysis land-cover dataset, 

which categorizes land-cover into 125 different types, would 

have been nearly impossible given the state of the data in 

early 2012.

The InVEST biodiversity model provides a simple model 

of habitat quality and degradation that is compatible with 

other ecosystem service models, enabling tradeoff estimates 

between biodiversity and other ecosystem services. Because 

the supporting data tables for the biodiversity model rely on 

rankings and expert opinion rather than on measured data, it 

was easier to complete the biodiversity data tables for more 

complex land-use–land-cover typologies. The portion of the 

watershed located in Mexico lacked data on grazing lands or 

intensity, so the model was run only for the United States portion of the San Pedro River watershed. An improved understanding of grazing patterns in Mexico could allow the model 

run to be extended beyond the United States portion of the 

watershed. The wells data, which relied on well locations as a 

measure of degradation, do not precisely measure the effects 

of groundwater pumping, because effects are felt in areas 

where groundwater flow is influenced by cones of depression. A better indicator of the consequences of groundwater 

depletion is thus desirable for use in the InVEST biodiversity 

model. As noted in the InVEST user’s guide, biodiversity 

drivers of change can be split into many categories indicating degree of use (for example, dirt road, paved road, divided 

highway), each with different levels of impact (Tallis and others, 2011). In this application different levels of infrastructure 

use were not specified, but this additional information could be 

desirable for measuring effects on biodiversity. A final limitation of our application of the biodiversity model is that not all 
Appendix A 73

As discussed in the body of the report, interpretation of results of the InVEST viewshed model is relatively 

straightforward. However, because users are unable to 

manually control the breakpoints for visualizing results, 

as of early 2012 there was no way to quantitatively compare the increase in viewshed impacts between scenarios. 

For example, in our baseline scenario, “low impacts” 

were classified as 0–2,608 features visible and “high 

impacts” as 30,214–193,150 features visible. In the opendevelopment scenario, “low impacts” are classified as 

0–7,308 features visible while “high impacts” are classified 

as 145,360–738,436 features visible. Although the opendevelopment scenario has greater visual impacts, the two sets 

of results cannot be directly compared given the constraints of 

the early 2012 model release.

Spatially explicit hydrologic modeling is challenging in 

arid regions where precipitation is patchy, infiltration dynamics differ from those in humid systems, and the signal:noise 

ratio for runoff is very small. Hydrologists working in the area 

expressed concern about whether the InVEST water models 

would capture these nuances. However, InVEST water yield 

models have been tested in other groundwater-dominated 

systems, where they were deemed to perform acceptably, 

provided that the results could be calibrated to time-series 

streamflow data (Mendoza and others, 2011, their box 4.1). 

Data integration to support water modeling between United 

States and Mexican or global datasets was cumbersome. In 

particular, integrating United Nations Food and Agriculture 

Organization and State Soil Geographic (STATSGO) soils 

data was an extremely cumbersome operation that left us 

unable to model water yield in the Mexican portion of the 

watershed. Finally, it is critical to display water yield and 

water demand maps side by side, in order to present a balanced view of supply and demand for water. Presentation of 

water yield maps alone could provide a misleading view that 

since development increases runoff, more development would 

lead to greater ecosystem services provision. By also showing 

changes to water demand, we can get a more complete view of 

changes to supply of and demand for water under alternative 

development scenarios.

Because InVEST relies on land-use–land-cover changes 

to map ecosystem services changes, it could not be used for 

the groundwater augmentation scenarios owing to the much 

finer detail in the land-use–land cover typologies upon which 

InVEST relies. The underlying data do not support use of 

detailed typologies, but the simplistic 10-class typologies 

cannot represent fine-scale changes in riparian habitat (for 

example, from tamarisk to cottonwood-willow). There is thus 

a disconnect between the data available and the applicability 

of complex scenarios in InVEST. In some cases, this disconnect can be resolved through creative changes to land-use–

land cover typologies (for example, combining land-use–

land-cover data with another layer to generate a slightly more 

complex, but realistic, land-use–land-cover dataset), although 

so doing can be time consuming.

Table A5. Carbon pools, San Pedro River watershed, Arizona.

Land use or land cover Carbon pool

(tons/hectare/year)

Code Name Aboveground

Belowground Soil Dead plant 

debris

1 Forest 59.2 0 76 38

2 Oak woodland 29.6 0 76 19

3 Mesquite woodland 19.7 0 76 12.7

4 Grassland 0 0 73 0

5 Desert scrub 0.6 1 22 0

6 Riparian 59.2 0 76 38

7 Agriculture 0 0 42 0

8 Urban 0 0 12.6 0

9 Water 0 0 0 0

10 Barren 0 0 0 0

Table A6. Biophysical characteristics used in water yield model, 

San Pedro River watershed, Arizona.

Land use or land cover Root depth

(millimeters)

Evapotranspiration

coefficient Code Name

1 Forest 4,000 420

2 Oak woodland 8,000 330

3 Mesquite woodland 14,000 260

4 Grassland 1,000 275

5 Desert scrub 1,500 245

6 Riparian 6,000 750

7 Agriculture 3,700 1,000

8 Urban 500 1

9 Water 1 1

10 Barren 1 1

Table A7. Water demand, San Pedro River watershed, Arizona.

Land use or land cover Demand

Code Name (cubic meters/year)

1 Forest 0

2 Oak woodland 0

3 Mesquite woodland 0.05

4 Grassland 0.05

5 Desert scrub 0.05

6 Riparian 0.05

7 Agriculture 0

8 Urban 110

9 Water 0

10 Barren 0

of the drivers of change layers were updated for each scenario. 

For instance, to properly update urban growth scenarios in 

the biodiversity model, well-pumping and roads data would 

have been needed for each new scenario. Acquiring these data 

would have been too time consuming and subjective a process 

for this pilot study, so our impact maps for future scenarios are 

somewhat conservative. In a full application of InVEST, it is 

better if modelers use scenario data that incorporate not just 

changes in land use and land cover but also in the accompanying infrastructure that can affect biodiversity.
74 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Appendix B. Spatial Data and Assumptions Used in ARIES Models

speed the modeling process for new case studies are unlikely 

to be completed by this date, and the interface and functionality are likely to go through substantial evolution through 

future releases. Construction of new ARIES models for the 

San Pedro River watershed was extremely time consuming 

and took more time than envisioned at the start of the project. 

Although ARIES offers the user great flexibility in constructing ecosystem service models and scenarios, its early stage of 

development led to numerous time-consuming bugs, errors, 

and new feature requests that delayed delivery of final results. 

The system’s stability improved greatly during the project, and 

development of future models will be less time consuming 

than those of the pilot study.

Like InVEST, ARIES performed poorly in handling 

cross-border datasets. Although improved handling of crossborder datasets is planned for future ARIES releases, this 

model comparison did not enable ecosystem-services mapping 

for Mexican portions of the watershed.

Underlying data and assumptions for the Artificial Intelligence for Ecosystem Services (ARIES) models, which are 

provided in the ARIES modeling guide (Bagstad and others, 

2011), are too detailed to include in this appendix. Chapters 2, 

3, 8, and 9 of that guide describe the models, underlying data, 

and assumptions used for carbon sequestration and storage, 

aesthetic views and proximity, water supply, and recreation, 

respectively. Chapter 1 of that guide provides an overview of 

the ARIES modeling system and includes an introduction to 

spatial flow concepts for ecosystem services.

Limitations and areas for improvement

Many of ARIES’ limitations relate to its relatively early 

stage of development at the start of this pilot project. A release 

of ARIES models and full documentation by means of a web 

tool (http://ariesonline.org) was scheduled for late 2012. However, full functionality, availability of global models, deployment of a more user-friendly modeling language, and tools to 
Appendix C 75

Appendix C. Literature Review of Value Transfer and Best Practices

value of nonmarket goods and services, so the values obtained 

from primary research are generally used as proxies for true 

values (although like all studies they contain some margin of 

error). All subsequent discussion of transfer errors are based 

on the assumption that primary valuation estimates are the 

true values.

Point Transfer

The earliest method of value transfer was point transfer, 

which directly applies value estimates from the study site 

to the policy site. Point transfer may use a benefit estimate 

from a single primary study or an average of several studies 

(Boyle and Bergstrom, 1992; Bergstrom and De Civita, 1999; 

Brouwer, 2000; Baskaran and others, 2010). The primary 

weakness of point transfer is its inability to systematically 

account for differences between sites. Any adjustments to the 

transferred values are made on a case-by-case basis that relies 

on the professional judgment of the analyst. The simplicity of 

this method is attractive in situations with limited staff expertise and quick deadlines. However, the defensibility of this 

method is low unless there is a high degree of correspondence 

between the study site and the policy site (Bergstrom and 

De Civita, 1999; Brouwer, 2000).

Benefit Function Transfer

Benefit function transfers are based on the notion that 

environmental and resource values are a function of numerous 

attributes, including biophysical attributes, market characteristics, and the socioeconomic profile. A benefit function 

transfer uses the statistical model from an existing study and 

applies it to the policy site, using data from the policy site for 

the independent variables (Groothius, 2005). For example, a 

function may relate the WTP for improved water quality for 

recreation (the dependent variable) to a series of independent 

variables, such as baseline water quality, type and intensity 

of recreational use, population within driving distance of the 

recreation site, number of substitute sites within a specified 

radius, and per capita income of the user group. The previously specified relationship between the independent variables 

and the dependent variable will translate the site-specific data 

inputs into an estimate of WTP at the policy site.

Loomis (1992) found that benefit function transfers 

resulted in lower errors than point transfers 70 percent of the 

time. Transferring benefit functions is more appropriate than 

point estimates when the policy site and the study site correspond less well (Loomis, 1992). For instance, where the study 

site and the policy site correspond closely, point transfer can 

perform as well as benefit function transfer (Chattopadhyay, 

Introduction and Overview of Value Transfer

Benefit transfer is the practice of taking valuation estimates from one location (study site) and applying them in a 

new context (policy site). Value transfer has become tremendously popular, especially among Federal agencies, which 

are faced with constrained budgets, limited staff, and short 

deadlines. Although original valuation studies continue to be 

viewed as the “first-best” option for nonmarket valuation and 

ecosystem services research, value transfer has the potential 

to inform decisionmaking in situations where primary research 

is not feasible. 

The increased use of value transfer has sparked a substantial amount of research into its capabilities and limitations. At 

least two major natural resource or environmental publications 

devoted entire issues to this topic (Water Resources Research, 

1992, v. 28, no. 3; Ecological Economics, 2006, v. 60, no. 2). 

Despite advancements in techniques and best-practice principles during the past 20 years, the use of value transfer remains 

controversial. Compounding the controversy is that the underlying primary research methods, such as contingent valuation, 

are commonly disputed as well.

As long as Federal agencies face resource constraints and 

demands for environmental valuation, value transfer is likely 

to remain an important valuation method. Focus, therefore, 

is best placed on how to best utilize value transfer given its 

limitations. This literature review will focus on types of value 

transfer, the advantages and disadvantages of each method, 

sources of transfer errors and how to best address them, and 

best-practice principles for using value transfer in decisionmaking and policy analysis.

Types of Value Transfer

The literature describes numerous value transfer methods 

that have been studied and implemented (Wilson and Hoehn, 

2006). These methods range from simple (point transfer) to 

complex (meta-regression analysis). Choosing the appropriate 

method for the situation will depend on the time and expertise 

of the value transfer analyst, the availability of primary studies, the availability of policy site data, and the need for precision. No single method will be appropriate in all situations.

In most value transfer studies, the “true” value of a 

nonmarket good or service is assumed to be the value obtained 

from primary research. The difficulties in estimating economic 

values of nonmarket goods and services are well documented, 

which makes it likely that estimates from primary research 

are not equal to the true (but unknown) value (for example, 

Arrow and others, 1993). However, all else being equal, primary valuation is the best available method for estimating the 
76 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

2003). However, as conditions become less favorable, benefit 

function transfer outperforms point transfer. Nevertheless, 

as correspondence between sites deteriorates, transfer errors 

increase for both point transfer and function transfer.

The relative advantage of function transfer over point 

transfer may be debatable, as both methods can yield high 

transfer error (Navrud and Ready, 2007). Part of the reason 

that benefit functions may not consistently outperform point 

transfer is that a point transfer based on a study with a high 

degree of correspondence to the policy site may be expected to 

outperform a function based on studies of an area with weaker 

correspondence to the policy site. Some authors recommend 

that unless a meta-valuation function is defined (discussed in the 

next section), only studies with a high degree of correspondence 

are appropriate to use in the benefit function (Rosenberger and 

Phipps, 2007).

Meta-Regression Analysis

Meta-regression analysis is similar to benefit function 

transfer in that it assumes that resource values are systematically related to study site attributes through a “meta-valuation 

function” (Rosenberger and Stanley, 2006; Rosenberger and 

Phipps, 2007). The key difference between these functions and 

the benefit functions described in the previous section is the 

number of studies used to develop the function. In a function transfer, a function from a single study is applied to the 

policy area. In contrast, meta-regression analysis treats each 

study as an observation. The differences between the observations (for example, WTP) can be systematically related to the 

different attributes of the study site (for example, differing 

socioeconomic characteristics). This analysis may be particularly valuable when substantial differences exist between the 

study site or sites and the policy site. Although the explanatory 

power of function transfer is typically reduced when substantial differences exist between the study site and the policy site, 

these differences can be exploited by meta-regression analysis 

to identify relationships between the resource value and attributes of the site, population, or study design (Rosenberger and 

Loomis, 2000; Morrison and Bergland, 2006).

The general form of a meta-regression analysis includes 

market prices, household income, quantity of nonmarket 

commodity available, quality of the nonmarket commodity, a measure of substitutes for the nonmarket commodity, 

nonincome characteristics of the household, and the information available (Bergstrom and Taylor, 2006). Microeconomic 

theory assumes that a utility function can be defined that 

accounts for an individual’s consumption of all goods and 

services. The utility function can be maximized such that 

well-being (utility) is maximized subject to income limits. The 

maximization problem changes when the price of a good or 

service changes (as a result of both income and substitution 

effects). A change in market prices will affect consumption of 

nonmarket goods and services. Therefore, it is important that 

market information is included in the regression equation. The 

complexity of ecosystem processes makes their decomposition into ecosystem services difficult even for professional 

ecologists. Without adequate knowledge on how an individual 

benefits from an ecosystem service, valuation estimates are 

unlikely to reflect the true value. A measure of the information 

available helps to identify the uses for which the individual is 

expressing value. The explanatory variables used will depend 

on the specific context as well as the available studies.

Meta-regression analysis, however, is not without its 

difficulties. Several key problems plague it, such as data 

heterogeneity (for example, different dependent variables), 

heteroskedasticity (differing variances), correlated observations (for example, several studies may use the same 

underlying dataset), and the conflation of different economic 

concepts (for example, Hicksian and Marshallian consumer 

surplus (Nelson and Kennedy, 2008)). Another difficulty with 

this type of analysis is how to synthesize studies that use different sets of regressors (Moeltner and others, 2007). Bayesian 

modeling, discussed next, can help to overcome some of the 

difficulties with classical meta-analyses.

Bayesian Meta-Regression Analysis 

Bayesian statistics differs from classical statistics principally in its use of past experiences to derive an expected 

probability distribution. Bayesian value transfer uses information from past studies and expert opinion to establish a prior 

distribution (for example, our belief of how observations such 

as WTP are distributed). The predictive distribution gives the 

probability of observing data that do not correspond with the 

prior distribution. As new information becomes available, the 

distribution may be updated to reflect changes. This updated 

set then becomes the posterior distribution (Atkinson and 

others, 1992; Morrison and Bergland, 2006; Moeltner and 

others, 2007).

Bayesian meta-regression analysis can solve several difficulties that arise in its classical counterpart. For instance, the 

Bayesian version can help to overcome the “n vs. k” dilemma 

that is present in most classical versions (Bergstrom and Taylor, 

2006; Moeltner and others, 2007). The “n vs. k” dilemma 

refers to the tradeoff between the number of studies (n) and 

the number of explanatory variables (k) to be included in the 

analysis. A larger number of studies can improve estimation; 

however, it is unlikely that all studies will contain the desired 

explanatory variables. Reducing the number of independent 

variables can lead to omitted-variable bias. Bayesian metaregression analysis, however, is able to incorporate a large 

number of studies without losing explanatory variables. It is a 

two-step process: all available studies are first used to inform 

the development of the prior distribution, and then all studies 

with the desired explanatory variables are used to develop the 

meta-regression (Moeltner and others, 2007).
Appendix C 77

Additionally, Bayesian meta-regression analysis may be 

able to provide more robust estimates in the presence of collinearity than its classical counterpart (Chattopadhyay, 2003). 

Moreover, the Bayesian version has a comparative advantage in value transfers when only a small sample is available 

(Moeltner and others, 2007). This advantage is driven by the 

use of a prior distribution to determine model parameters.

Bayesian meta-regression analysis requires specialized 

statistical expertise, and thus it may not be feasible for use in 

all situations.

Preference Calibration

The preference calibration method was developed in 

response to perceived shortcomings in more common value 

transfer methods, particularly point transfer and function 

transfer (Smith and others, 2000). The researchers who 

pioneered this approach attempted to more closely align the 

value transfer process with economic theory. Major problems 

with previous methods include the possibility of obtaining 

WTP estimates that exceed available income; the assumption 

that WTP is linear (that is, each additional unit of the good or 

service adds an equal amount to value), which is counter to 

the principle of diminishing marginal utility; and the limited 

consideration of other uses of the same resource (for example, 

improved water quality may lead to increased water consumption, increased recreational uses, and higher nonuse values) 

that may affect its total value (Smith, Van Houtven and others, 

2000, 2006; Smith, Pattanayak and others, 2002).

Preference calibration attempts to overcome these shortcomings with an approach that is explicitly linked to microeconomic theory. Preference calibration, also known as the 

structural approach, begins with the selection of a “preference 

specification,” which describes an individual’s choices from a 

set of goods and services (market and nonmarket). The value 

transfer analyst is responsible for making explicit assumptions 

about the functional form of the individual’s utility function 

(Smith and others, 2000). The “utility function” defines how 

an individual’s well-being (utility) is related to the consumption of various goods and services. Among those goods and 

services is the ecosystem service of interest. Once the utility 

function is defined, determining WTP for the ecosystem service 

of interest becomes a standard maximization problem (that is, 

given income constraints, what is the highest level of utility 

possible?). The analyst can observe how a change in consumption of the ecosystem service affects utility, and can then infer 

the income necessary to offset the change, either positive or 

negative (Smith and others, 2002; Groothius, 2005).

This approach has four advantages: (1) the transfers 

are consistent with economic theory (for example, estimates 

cannot exceed household income), (2) the method can deliver 

“observable predictions” to assess validity of transfers, (3) the 

structural method can reconcile different value estimates from 

different studies, and (4) the method systematically adjusts 

for changes in the baseline and extent and resource changes 

(Smith and others, 2002). In addition, preference calibration 

makes analyst judgment more transparent. Although professional judgment is present in all value transfers, the preference 

calibration approach is explicit in its incorporation of analyst 

judgment in defining the parameters of the utility function 

(Smith and others, 2000).

As is true for meta-regression analysis (Bayesian and 

classical), preference calibration requires economic modeling 

expertise that is not readily available to Federal agencies. An 

additional limitation is that only a handful of value-transfer 

researchers have tested this approach. Thus, it may be less 

well scrutinized than more common value transfer approaches.

Expert Judgment

Several authors identify expert opinion as a reasonable 

means of conducting value transfer. Expert opinion may be 

used to establish an expected value distribution at the policy 

site, to adjust transferred values based on knowledge of the 

policy site, or to predict results (Leon and others, 2003). 

Generally, expert opinion is used in combination with other 

methods. This overlap between methods may be beneficial 

(French and Hitzhusen, 2001). For instance, the development of a prior distribution, which may incorporate expert 

judgment, is a central element of Bayesian meta-regression 

analysis. Also, as discussed in the preference calibration 

section, the analyst’s professional judgment is an explicit 

component of the approach.

Sources of Error

The sources of error discussed in this section relate 

only to errors that arise from the value transfer process, not 

the challenges inherent in estimating the value of nonmarket 

goods and services. A substantial body of literature exists on 

the limitations of primary valuation of nonmarket goods and 

services (for example, Arrow and others, 1993; Bateman and 

Willis, 1999; Freeman, 2003), and it is not the intent of this 

report to repeat those findings. The value transfer literature 

identifies four principal sources of error: generalization error, 

measurement error, publication bias, and research-priority 

bias (Rosenberger and Stanley, 2006; Hoehn 2006).

The most commonly cited source of error is generalization error, which stems from a lack of correspondence between 

sites. This error is common where environmental resources or 

user populations differ between sites (Plummer, 2009).

Measurement error occurs when methods or empirical 

techniques reduce the accuracy of value estimates. The valuation method, elicitation method, survey design, and units of 

measurement have all been found to have statistically significant influence on value (Rosenberger and Stanley, 2006). 
78 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Additionally, lack of understanding of complex biophysical 

processes can induce measurement error (Bergstrom and 

De Civita, 1999). Measurement error may be present in both 

original studies and transferred estimates. Transferring values 

with this type of error may magnify the errors, so many studies 

advise that particular care be taken to address the potential for 

measurement error (Bergstrom and De Civita, 1999; Johnston 

and others, 2006; Rosenberger and Stanley, 2006; U.S. Environmental Protection Agency, 2006; Baskaran and others, 2010).

Publication selection bias can also be a potential source 

of bias in transferred values. Publication selection bias occurs 

when results are more likely to be published if they have 

statistically significant results, conform to theoretical expectations, or explore novel concepts in valuation (Rosenberger and 

Stanley, 2006).

Research-priority bias arises from the fact that research 

is not randomly distributed in space and resources. Instead, 

research is more likely to be driven by the importance of a 

given resource, human awareness, and potential for conflict: 

“ecosystems that are considered valuable up front by some 

segment of the public seem more likely to be researched and 

valued” (Hoehn, 2006, p. 390). These characteristics may 

upwardly bias transferred estimates of resource value.

Addressing Error

Generalization errors are reduced when functions, rather 

than point estimates, are transferred (Rosenberger and Phipps, 

2007). In-state transfers have lower generalization errors than 

between-state transfers (Loomis, 1992). This finding supports 

the conventional view that improved correspondence between 

sites reduces generalization error. However, some studies still 

find very large generalization errors even between closely corresponding sites. A value transfer between the SPRNCA and 

Ramsey Canyon, another recreation site in southern Arizona 

that is renowned for its birding opportunities, is a case in point 

(Kirchoff and others, 1997). Despite the similarities between 

these sites in resources and populations, transfer of value 

estimates from Ramsey Canyon to the SPRNCA was rejected 

(that is, the transferred estimates were statistically different 

from the primary valuation estimates). However, the rejection 

rate fell to 40 percent for transfers in the reverse direction—

from SPRNCA to Ramsey Canyon. The authors find this result 

troubling and could not explain why value transfers may be 

appropriate in only one direction.

Because measurement error is inherent in all primary 

studies, it is difficult for the value transfer analyst to control 

(Rosenberger and Stanley, 2006). However, several steps can 

be taken to reduce the likelihood of including studies that 

have excessive measurement error. The U.S. Environmental 

Protection Agency work group (2006) recommends the development of explicit and consistent research-quality criteria to 

determine which studies can usefully be included in the value 

transfer. These criteria may include adequate data, sound 

economic method, and correct empirical technique (Brouwer, 

2000; Plummer, 2009). Some types of value transfer, such 

as meta-regression analysis, are able to partially control for 

measurement error (Bergstrom and Taylor, 2006; Moeltner 

and others, 2007).

Publication selection bias may be overcome through the 

inclusion of “gray literature” such as dissertations, technical reports, and government studies (Bergstrom and Taylor, 

2006). However, the inclusion of nonpeer-reviewed literature 

may also introduce “nonpublication error,” or errors stemming 

from poorly designed and executed studies (Bergstrom and 

Taylor, 2006). The inclusion of such studies has the potential 

to increase other errors (such as measurement error).

 Heckman’s two-stage estimation method may be used 

to avoid research priority selection (Hoehn, 2006). This test is 

used to draw conclusions about the larger population, not just 

the nonrandomly selected subpopulation (that is, prioritized 

research). First, a selection equation is specified to estimate 

the probability that a given study falls within the selected subpopulation. Second, the results from the first stage are used to 

correct for selection bias in the value-estimation equation.

Defining Acceptable Errors

The tolerable error in a benefit-transfer exercise depends 

on the context in which the estimated values will be used. 

Some applications, such as a cost-benefit analysis on a smallscale project, may not require very high confidence in the 

estimates. High anticipated transfer error may also be acceptable if the estimated values will be only one of many factors 

influencing a decision. However, some applications, such as 

decisions that could lead to irreversible losses or determining 

liability for environmental damages, demand a high level of 

precision. In these situations, it is not advisable to use value 

transfer to estimate values (Bergstrom and De Civita 1999; 

Navrud and Ready, 2007; Baskaran and others, 2010). Many 

projects will fall between these extremes. Decisionmakers 

and researchers need to work together to determine if value 

transfer is appropriate for a situation and, if so, then which 

method will best meet specific objectives. Whether to use 

value transfer or to conduct original research likely will 

depend on an estimate of the expected value of a primary 

study. Except for small projects (net present value less than 

$500,000), original research typically yields positive economic 

returns through improved decisions (Allen and Loomis, 2008). 

If several projects may have positive returns from original 

research, they can be ranked from largest to smallest to 

identify research priorities.
Appendix C 79

The accuracy of value transfer may also be improved 

through better identification of the beneficiaries of the 

ecosystem services, rather than by relying on a single 

geographic area (Plummer, 2009). Benefits may have divergent flow patterns that necessitate the consideration of 

several subpopulations.

Collect Data on Market Attributes 

of Policy Site

Transferred estimates may markedly overestimate 

or underestimate resource values if the natural resource 

“markets” differ substantially across sites. Particularly, the 

benefit transfer analyst should assess the existence of resource 

substitutes and complements at each site. In addition to 

resource and site substitutes, the analyst should consider the 

market characteristics at each site. Microeconomic theory 

dictates that a change in the price of one good or service will 

affect demand for other goods and services through income 

and substitution effects. Therefore, a change in the price of 

market goods and services is expected to affect WTP for 

nonmarket goods and services. Failure to account for these 

changes violates core economic principles and could lead to 

highly inaccurate estimates (Desvousges and others, 1992; 

Bergstrom and De Civita, 1999; Bergstrom and Taylor, 2006; 

Navrud and Ready, 2007).

Research Transparency

Nelson and Kennedy (2008) urge analysts to report 

source protocol, including the criteria used to select studies 

and the sources used to gather studies (for example, 

Environmental Valuation Reference Inventory, 2011; the 

EconLit database). The number of observations obtained 

from each study should be reported, as should the method 

used to weight multiple observations from a single study 

(U.S. Environmental Protection Agency, 2006). If and 

how missing data were treated should also be included in 

the benefit transfer report (Nelson and Kennedy, 2008). In 

addition to the study selection criteria, Bergstrom and Taylor 

(2006) recommend that all studies be considered, including 

those that failed to meet selection criteria.

Conduct Sensitivity and Validity Tests

The value transfer analyst should report the sensitivity 

of the value estimates to changes in the parameters (Nelson 

and Kennedy, 2008). If meta-regression analysis is used, 

the analyst should test the sensitivity of the results to the 

exclusion of each observation to determine if a single data 

point has a disproportionate influence on the value estimate 

(U.S. Environmental Protection Agency, 2006). If the results 

Best Practice Principles

Clearly Define Aims of Study

Transfer errors are more likely to arise when the issue 

or resource to be studied is not clearly defined. Without a 

clear and common understanding of the precise services that 

are being valued, double counting is more likely to occur 

(Brouwer, 2000). Additionally, care should be taken to ensure 

that the estimates from the primary studies are valuing the 

same resource (and associated change) as is being considered 

at the policy site (Iovanna and Griffiths, 2006; Nelson and 

Kennedy, 2008). Different benefit concepts, such as Hicksian 

and Marshallian consumer surplus, should not be combined 

(V.K. Smith and others, 2006).

Collect Data on Biophysical Attributes 

of Policy Site

Much of the literature on value transfer focuses on the 

difficulty of taking economic values obtained at one site and 

applying them to another. There is much less discussion, however, of the inherent difficulty in conducting economic valuation of complex ecosystem processes. Environmental attributes 

usually contribute to economic values through many channels, 

not all of which are apparent to even experienced observers. It is 

important to identify ecological and economic linkages in both 

original research and value transfer (Bergstrom and De Civita, 

1999). Clearly identified flows and descriptions of ecological 

processes can reduce the likelihood of double counting.

Collect Data on Socioeconomic Attributes 

of a Policy Site

Most nonmarket valuation studies use aggregate measures of WTP to determine the economic value of a good 

or service. This measure is not a universal value, because 

this WTP is tempered by ability to pay. Substantial differences in income between sites may lead to large differences 

in estimates of value. Other demographic attributes, such as 

education and family status, may also systematically change 

WTP. The benefit transfer analyst should consider these and 

other demographic characteristics that may affect this WTP 

for an environmental good or service (Brouwer, 2000). These 

attributes, however, are incomplete proxies for the human 

element. The attitudes, beliefs, and values of the population 

are also key drivers of WTP (Loomis and Rosenberger, 2006; 

Baskaran and others, 2010). These variables are difficult to 

capture in value transfer function, and so they usually are 

ignored. Thus, careful consideration of characteristics of the 

public and stakeholders (discussed in the next section) is an 

important component of a robust value transfer.
80 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

are found to be fragile, the implications for policy analysis 

should be considered (for example, how to treat sensitive results 

as opposed to robust estimates (Nelson and Kennedy, 2008)).

As described above, transferred benefit estimates may be 

subject to estimation error resulting from generalization error, 

measurement error, publication selection bias, or research 

priority bias. Where feasible, tests should be undertaken to 

correct for these potential biases. If the benefit estimates are 

uncorrected, this fact should be made explicit in the report.

Stakeholder Engagement

One of the key elements in increasing correspondence 

between study site and policy site values is to include socioeconomic characteristics as an independent variable. However, 

“latent variables, such as the attitudes, beliefs, and perceptions 

of respondents,” usually need to be addressed to minimize 

transfer errors (Loomis and Rosenberger, 2006). Understanding these “latent variables” is difficult without stakeholder 

engagement. Public outreach strategies are an important 

component of value transfer. Additionally, public engagement 

can be a useful means of validating the value estimates. If the 

public finds an estimate unreasonable, it might be cause to 

reconsider the method or other inputs (Brouwer, 2000; Spash 

and Vatn, 2006).

Value Incremental Changes

The decisions that BLM makes based on the analyses 

presented in RMPs and NEPA documents tend to result in 

marginal and incremental changes in resource quality and 

quantity. Obtaining the total economic value of a resource, 

therefore, may be of limited use. Rather, BLM is advised 

to focus on transferring values from studies with resources 

changes that correspond to the expected changes at the policy 

site (Loomis and Rosenberger, 2006).

Conclusions: BLM–USGS Ecosystem Services 

Valuation Pilot Study 

Value transfers known to be inadequate (or worse, 

grossly inaccurate) are sometimes justified by noting that 

any value is better than the implicit value of zero that natural 

resources are commonly given in cost-benefit analyses and 

other monetary decisionmaking tools. However, this practice 

can discredit all environmental valuation techniques (Smith, 

1992). Most of the literature supports the notion that if a 

resource value cannot be defensibly monetized, it is preferable 

to provide a qualitative description or utilize a framework that 

allows for various types of values (for example, multicriteria 

analysis (Spash and Vatn, 2006). The benefits of a single unit 

(money) to compare disparate resources, however, can be 

substantial. OMB economists, in conversation, were skeptical that qualitative descriptions of benefits would be able to 

offset monetary estimates of cost (Loomis and Rosenberger, 

2006). What can be defensibly monetized should be monetized. However, applying highly inaccurate values just for 

the sake of having any value should be avoided. Furthermore, monetary estimates of value are only one influence 

on decisions. Economic valuation should not exclude the 

usefulness of qualitative descriptions and noneconomic 

quantitative data. Economic estimates have the advantage 

of simplifying complex information, but this advantage 

is also a constraint. The ability to explain complexity and 

nuance makes qualitative analysis central to any defensible 

environmental valuation study. Similarly, data on ecological 

thresholds and other biophysical indicators should inform 

the decisionmaking process.

BLM is encouraged to be cautious in its use of value 

transfer. Monetization may give a sense of false precision, 

particularly to nonexpert audiences. The value transfer 

literature has found value estimate errors that are as high 

as 7000 percent (Loomis, 1992). Errors ranging from 

20–40 percent are probably in the acceptable range (Navrud 

and Ready, 2007). Deciding on “acceptable errors” depends 

on the context in which the values will be used. Conducting a 

cost-benefit analysis on a small-scale project will require less 

precision than a Natural Resource Damage Assessment, where 

legal compensation is decided. In the case of the latter, value 

transfer is probably inappropriate (Navrud and Ready, 2007). 

Regardless of the errors that BLM decides are “acceptable,” 

care should be taken to follow the best practice principles 

outlined above.
Appendix D 81

Appendix D. Additional Participants in Model and Document Review Process

Table D1. Additional participants in Bureau of Land Management–U.S. Geological 

Survey ecosystem services valuation pilot study, San Pedro River watershed, Arizona.

[DOI, Department of Interior; USDA, U.S. Department of Agriculture] 

Name Affiliation

Economics review panel

Boyd, Jim Resources for the Future

Brown, Paul Bureau of Land Management

Casey, Frank U.S. Geological Survey

Gascoigne, Billy U.S. Geological Survey

Koontz, Lynne U.S. Geological Survey

Model review workshop

Boykin, Ken New Mexico State University

De Steiguer, Ed University of Arizona

Diffendorfer, Jay U.S. Geological Survey

Francis-Begay, Juanita U.S. Geological Survey

Goodrich, Dave USDA–Agricultural Research Service

Halper, Eve Bureau of Reclamation

Holler, Eric Bureau of Reclamation

Kepner, Bill U.S. Environmental Protection Agency

Kralovec, Mary Bureau of Land Management

Lansey, Kevin University of Arizona

Lomeli, Ben Bureau of Land Management

Mahoney, Jim Bureau of Land Management

Osterkamp, Waite USDA–Agricultural Research Service

Pattison, Malka DOI–Office of Policy Analysis

Perrings, Charles Arizona State University

Piper, Steve Bureau of Reclamation

Rekshynskyj, Mark Bureau of Land Management

Simms, Jeff Bureau of Land Management

Simonit, Silvio Arizona State University

Sobiech, Amy Bureau of Land Management

van Riper, Charles U.S. Geological Survey

Villareal, Miguel University of Arizona

Vukomanovic, Jelena University of Arizona

General project consultation

Pattison, Malka DOI–Office of Policy Analysis

Rekshynskyj, Mark Bureau of Land Management
82 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Appendix E. Annotated Bibliography of Ecological and Socioeconomic 

References Related to the San Pedro River Watershed and the U.S. Southwest

lease prices for grazing on public land. Average WTP was 

$21.68 per household per year.

Berrens, R.P., Bohara, A.K., Silva, C.L., Brookshire, 

D., and McKee, M., 2000, Contingent values for New 

Mexico instream flows—With tests of scope, group size 

reminder and temporal variability: Journal of Environmental Management, v. 58, p. 73–90.

This study expanded on the Berrens and others (1996) 

study to estimate public values for protecting in-stream flows 

for threatened and endangered native fish species on New 

Mexico’s rivers. The authors incorporated additional experimental work, including trying to separate how the public responds to 

the concept of paying to purchase an environmental public good 

compared with contributing to a worthy cause. The authors, 

using logistic regression models, found mean WTP for a 5-year 

period to be $26.42 for the silvery minnow on the Middle Rio 

Grande and $72.18 for 11 species on four rivers.

Brand, L.A., White, G.C., and Noon, B.R., 2008, Factors 

influencing species richness and community composition of breeding birds in a desert riparian corridor: The 

Condor, v. 110, no. 2, p. 199–210.

The authors compared bird species richness, cooccurrence, and uniqueness at multiple sites and plots on the 

San Pedro River to explore the influence of riparian vegetation 

type, surface flow permanence, and floodplain compared with 

terrace location. They found greater species richness and more 

uniqueness in cottonwood and mesquite vegetation types, 

lower species richness at ephemeral flow sites, and relatively 

high species richness but low uniqueness in tamarisk patches.

Brookshire, D.S., Goodrich, D., Dixon, M.D., Brand, L.A., 

Benedict, K., Lansey, K., Thacher, J., Broadbent, C.D., 

Stewart, S., McIntosh, M., and Kang, D., 2010, After

restoration—A framework for preserving semi-arid 

regions in the Southwest: Journal of Contemporary 

water Research and Education, v. 144, p. 60–74.

The authors described the process of integrating economic valuation into an established decision support system 

linking water and water management to surface flows, vegetation, and bird communities on the San Pedro River. They 

developed a survey for nine surface and groundwater futures 

for the San Pedro, with valuation to be conducted using contingent valuation and choice modeling for alternative attribute 

bundles of water, vegetation, and birds. They also describe a 

future study that would include comparable valuation for the 

Middle Rio Grande and explore the transferability of results 

between the two watersheds.

Bark-Hodgins, Rosalind, and Colby, B.G., 2006, An economic assessment of the Sonoran Desert Conservation 

Plan: Natural Resources Journal, v. 46, p. 709–726.

This study compared property premiums (calculated by 

using hedonic pricing) and tax revenues compared with the cost 

of water leases and ecological restoration to support waterdependent habitats from the Sonoran Desert Conservation Plan. 

The authors estimated property premiums at $127–253 million, 

generating $1.2–2.5 million in annual property tax revenues, 

and the cost of water leases to be $0.5 million per year, with 

annualized restoration costs estimated at $235/acre-year to 

restore 9,400 acres of riparian habitat.

Bark, R.H., Osgood, D.E., Colby, B.G., Katz, G., and 

Stromberg, J., 2009, Habitat preservation and restoration—Do homebuyers have preferences for quality habitat?: Ecological Economics, v. 68, p. 1465–1475.

The authors estimated property premiums using hedonic 

pricing for different types of open space and the quality of 

riparian vegetation, including biodiversity, upland connectivity, and wetness metrics. The survey included Rillito Creek 

and its tributaries in northeast Tucson. The authors found that 

wetness, biodiversity, and upland connectivity increase property value, whereas biomass does not.

Berrens, R.P., Ganderton, P., and Silva, C.L., 1996, Valuing the protection of minimum instream flows in New 

Mexico: Journal of Agricultural and Resource Economics, v. 21, no. 2, p. 294–309.

The authors used a dichotomous-choice contingentvaluation telephone survey to estimate public values for 

protecting in-stream flows on New Mexico’s rivers. They 

compared results for protecting one endangered fish species, 

the silvery minnow, on a 170-mile river stretch of the Middle 

Rio Grande with protecting 11 threatened and endangered 

fish species on four New Mexico rivers extending more than 

1,000 miles. They found mean annual WTP for a 5-year period 

to be $28.73 for the silvery minnow on the Middle Rio Grande 

and $89.68 for 11 species on four rivers.

Berrens, R.P., Brookshire, D., Ganderton, P., and McKee, 

M., 1998, Exploring nonmarket values for the social 

impacts of environmental policy change: Resource and 

Energy Economics, v. 20, p. 117–137.

The authors used a telephone survey of New Mexico residents to estimate WTP to maintain traditional extractive land 

uses, in the context of offsetting potential increases to grazing 

permit prices on public land. Two thirds of respondents were 

unwilling to pay anything, indicating that many felt that the 

public should not compensate ranchers forced to pay higher 
Appendix E 83

Brown, Joel, Angerer, J., Salley, S.W., Blaisdell, R., and 

Stuth, J.W., 2010, Improving estimates of rangeland 

carbon sequestration potential in the US Southwest: 

Rangeland Ecology and Management, v. 63, p. 147–154.

The authors estimated changes in soil carbon sequestration 

under alternative range management practices for the Southwestern United States, using the CENTURY model to estimate 

soil carbon sequestration and associated uncertainty. They found 

that current model results have uncertainty levels that are too 

high to guide management and policy, and that greater integration of site-specific data is needed to improve the quality of 

model outputs in the Southwestern United States.

Brown, T.C., Harding, B.L., and Payton, E.A., 1990, Marginal economic value of streamflow—A case study for 

the Colorado River Basin: Water Resources Research, v. 

26, no. 12, p. 2845–2859.

The authors estimated the economic value of increased 

runoff from reduced evapotranspiration caused by logging in the 

Colorado River Basin. This study combined economic valuation, water routing, basin modeling, the effect of flow changes 

on salinity, consumptive water use, and hydropower production. Economic values and costs were estimated for increased 

consumptive use, increased hydropower production, increased 

deliveries to Mexico, enlarged reservoirs for recreation, changes 

in salinity and agriculture, increased flows for recreation and 

fish habitat, dilution of dissolved solids, reduced damage to 

pipes, better agricultural yields, and increased flooding potential. Using linear programming to optimize water allocation, the 

authors found increased water availability tended to go toward 

storage in Lakes Powell and Mead rather than to lower-priority 

Lower Colorado Basin users.

Bureau of Reclamation, 2007, Appraisal report. Augmentation alternatives for the Sierra Vista Subwatershed, 

Arizona—Lower Colorado Region: U.S. Department of 

the Interior Bureau of Reclamation, 95 p.

This study compared alternatives, including their cost 

and feasibility, for increasing water supplies in the Sierra Vista 

subwatershed of the San Pedro River. The goal of potential 

water augmentation alternatives is to maintain flow in the 

San Pedro while meeting growing water needs as population 

expands in the watershed. Alternatives included water harvesting and recharge along with water transfers both within and 

outside the San Pedro River watershed.

Colby, B.G., and Orr, P., 2005, Economic tradeoffs in 

preserving riparian habitat: Natural Resources Journal, 

Winter, 17 p. (unpaged).

The authors, using payment-card contingent valuation, estimated WTP of nonlocal visitors to the San Pedro 

River watershed to avoid dewatering and degradation to the 

river. Surveys were administered to nonlocal visitors to the 

SPRNCA and to Ramsey Canyon. The authors found mean 

WTP to be a one-time payment of $79.31, with a total ranging 

from $2.1–3.5 million depending on estimates of the annual 

number of nonlocal users.

Colby, B.G., and Smith-Incer, E., 2005, Visitor values and 

local economic impacts of riparian habitat preservation—California’s Kern River Preserve: Journal of the 

American Water Resources Association, v. 41, no. 3, 

p. 709–717.

The authors used payment-card contingent valuation and 

local economic assessment, including direct expenditures, 

indirect increased economic activity, and induced local wage 

increases to estimate the economic value of southern California’s Kern River Preserve, a nationally renowned birding 

preserve. Visitors were asked their WTP to avoid streamflow 

loss, habitat degradation, and reduced numbers and diversity 

of birds and wildlife. The authors found annual values of 

$648,000–864,000 in direct spending, $1.1–1.5 million using 

economic impact including multiplier effects, and annual WTP 

of $467,000–616,000 based on annual visitation of 6,000–8,000.

Colby, B.G., and Wishart, S., 2002, Quantifying the influence of desert riparian areas on residential property values: The Appraisal Journal, v. LXX, no. 3, p. 304–308.

The authors used hedonic pricing to value riparian habitat 

in northeast Tucson for property near Tanque Verde Wash and 

adjacent tributaries. They found that by reducing a property’s 

distance to the single largest urban riparian corridor from 1.5 

to 0.1 miles, the sample mean house price rose by 6 percent. 

This relationship was nonlinear, and value declined steeply 0.1 

to 0.5 miles from the river and more gradually 0.5 to 1.5 miles 

from the river. Given that 25,560 houses are located within 

1.5 miles of Tanque Verde Wash, the total property premium is 

$103.1 million.

Crandall, K.B., Colby, B.G., and Rait, K.A., 1992, Valuing 

riparian areas—A southwestern case study: Rivers, v. 3, 

no. 2, p. 88–98.

The authors used three alternative valuation methods 

(zonal travel cost, payment-card contingent valuation, and 

local economic assessment, including direct expenditures, 

indirect increased economic activity, and induced local wage 

increases) to estimate the economic value of the 600-acre Hassayampa River Preserve near Wickenburg, Ariz. This section 

of the Hassayampa has perennial surface flow that supports 

cottonwood-willow riparian forests. The authors found annual 

values of $52,000 in direct spending, $88,000 using economic 

impact including multiplier effects, $613,000 in consumer 

surplus using the travel cost method, and annual WTP of 

$520,000 based on annual visitation of 8,000.
84 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

de Steiguer, J.E., 2008, Semi-arid rangelands and carbon offset markets—A look at the economic prospects: 

Rangelands, v. 30, p. 27–32.

The author described how rangeland management could 

be improved by changing management practices to encourage 

carbon sequestration, with financing obtained from carbon 

markets. He described the baseline compared with simulated 

carbon storage using the Erosion-productivity impact calculator (EPIC) model, and he estimated the potential value of 

carbon as compared to current returns for grazing on State 

trust lands in Arizona.

Ferguson, T.J., and Colwell-Chanthaphonh, C., 2006, History is in the land—Multivocal tribal traditions in Arizona’s San Pedro Valley: Tucson, University of Arizona 

Press, 336 p.

The authors described the connections of four tribal 

groups to the San Pedro Valley—the O’odham, Hopi, Zuni, 

and Apache. Although written Western histories most closely 

associate the San Pedro River watershed with eastern relatives 

of today’s O’Odham, the Apache, the Zuni, and Hopi have 

strong connections to the San Pedro River region by means of 

their historical migrations, as do other tribes not discussed in 

detail in this book. Although the book does not explicitly use 

the frame of ecosystem services in describing American Indian 

connections to the land, it does describe the historical, cultural, 

and spiritual links that tribes hold to the landscape.

Galbraith, Hector, Dixon, M.D., Stromberg, J.C., and 

Price, J.T., 2009, Predicting climate change risks to 

riparian ecosystems in arid watersheds—The Upper San 

Pedro as a case study, in Kapustka, L.A., and Landis, 

W.G., eds., Environmental risk and management from 

a landscape perspective: Hoboken, N.J.: John Wiley, 

p. 187–202.

The authors modeled potential climate-change effects on 

the San Pedro River watershed’s hydrology, geomorphology, 

and vegetation. Scenarios included no change, warming of 

4–6°C, and warming with a decline or increase in precipitation. Channel migration, changes in dominant vegetation, and 

shifts in avian communities were documented for the different scenarios. Wetter winters were more likely to maintain 

cottonwood and willow recruitment, which is likely to decline 

on the basis of the river’s geomorphic history. Bird species 

vary in their vulnerability to climate change, likely because of 

changes in plant communities.

Giraud, K.L., Loomis, J.B., and Johnson, R.L., 1999, Internal and external scope in willingness-to-pay estimates 

for threatened and endangered wildlife: Journal of Environmental Management, v. 56, p. 221–229.

The authors estimated WTP for protecting the Mexican spotted owl alone compared with the Mexican spotted 

owl as part of a package of 62 other Four Corners region 

threatened and endangered species. Four million dollars have 

been budgeted during 10 years for spotted owl recovery. The 

authors used dichotomous-choice mail surveys, of which half 

were sent to the Four Corners region and half to the rest of 

the United States. They found that WTP for 62 species was 

greater than for the spotted owl alone, so the “scope test” in 

contingent-valuation surveying was passed.

Hand, M.S., Thacher, J.A., McCollum, D.W., and Berrens, 

R.P., 2008. Intra-regional amenities, wages, and home 

prices—The role of forests in the Southwest: Land Economics, v. 84, no. 4, p. 635–651.

The authors used hedonic pricing to estimate the value 

of forest land and tradeoffs between forest cover and wages in 

Arizona and New Mexico. They found that forest area carries 

an implicit annual price of $27–36 per square mile, based on 

estimates of forest cover within Census Public Use Microdata 

Areas. Using the same approach, they found that wilderness 

areas carry a higher value, whereas surface water carries a 

lower value.

Housman, D.C., Powers, H.H., Collins, A.D., and Belnap, 

J., 2006, Carbon and nitrogen fixation differ between 

successional stages of biological soil crusts in the Colorado Plateau and Chihuahuan Desert: Journal of Arid 

Environments, v. 66, p. 620–634.

The authors compared rates of carbon and nitrogen 

fixation in early- and late-successional biological soil crusts 

on the Colorado Plateau and Chihuahuan Desert. Relative to 

early-successional soil crusts, late-successional soil crusts 

had fixation 1.2–1.3 times as great on the Colorado Plateau 

and fixation 2.4–2.8 times as great in the Chihuahuan Desert. 

Because succession in soil crusts is very slow, the protection 

of late-successional soil crusts in deserts is important in maintaining terrestrial carbon sequestration and storage.

Huxman, T.E., Snyder, K.A., Tissue, D., Leffler, A.J., Ogle, 

K., Pockman, W.T., Sandquist, D.R., Potts, D.L., and 

Schwinning, S., 2004, Precipitation pulses and carbon 

fluxes in semiarid and arid ecosystems: Oecologia, v. 

141, p. 254–268.

This article discussed the role of precipitation on carbon 

fluxes in desert ecosystems. Small precipitation pulses can 

initiate soil microbial respiration, leading to the release of 

carbon from ecosystems, whereas larger precipitation pulses 

or a sequence of small events are needed to trigger periods of 

greater photosynthetic activity that lead to carbon sequestration. The timing and magnitude of the precipitation pulses 

help determine rates of respiration or photosynthesis, which 

determines whether an ecosystem is a net source of or sink for 

carbon at seasonal and annual time scales.
Appendix E 85

Huxman, T.E., Wilcox, B.P., Breshears, D.D., Scott, R.L., 

Snyder, K.A., Small, E.E., Hultine, K., Pockman, W.T., 

and Jackson, R.B., 2005, Ecohydrological implications 

of woody plant encroachment: Ecology, v. 86, no. 2, 

p. 308–319.

The authors discussed the consequences of woody plant 

expansion into former grasslands on water balance, including 

streamflow, evaporation, and transpiration. Where subsurface 

flow dominates, woody plant encroachment would be expected 

to reduce streamflow. Shifts in transpiration relative to evapotranspiration are also expected, but they would differ in mesic, 

semiarid, and arid systems.

Kaye, J.P., Majumdar, A., Gries, C., Byantuyev, A., 

Grimm, N.B., Hope, D., Jenerette, G.D., Zhu, W.X., and 

Baker, L., 2008, Hierarchical Bayesian scaling of soil 

properties across urban, agricultural, and desert ecosystems: Ecological Applications, v. 18, no. 1, p. 132–145.

The authors used hierarchical Bayesian models to scale 

up results for landscape carbon, nitrogen, and phosphorus 

pools from the plot level to the regional level in the Central 

Arizona–Phoenix Long-Term Ecological Research site. They 

provided soil nutrient pool data for five land-cover types: 

urban xeric yards, urban mesic yards, urban nonresidential, 

deserts, and agriculture.

Kepner, W.G., Watts, C.J., Edmonds, C.M., Maingi, J.K., 

Marsh, S.E., and Luna, G., 2000, A landscape approach 

for detecting and evaluating change in a semi-arid environment: Environmental Monitoring and Assessment, 

v. 64, p. 179–195.

The authors estimated vegetation change with time in 

the San Pedro River watershed and quantified changes at the 

watershed scale. They measured change on the basis of classified Landsat Multispectral Scanner System images for the 

years 1973, 1986, and 1992. They found that the total area and 

patch size of grassland and desert scrub decreased, whereas 

mesquite and urban area increased.

Kepner, W.G., Semmens, D.J., Bassett, S.D., Mouat, D.A., 

and Goodrich, D.C., 2004, Scenario analysis for the San 

Pedro River, analyzing hydrological consequences for 

a future environment: Environmental Modeling and 

Assessment, v. 94, p. 115–127.

The authors used urbanization scenarios developed 

for the San Pedro by Steinitz and others (2003) to compare 

runoff and sediment under each scenario. They compared year 

2000 and three 2020 land-use–land-cover grids (constrained, 

planned, and open-development scenarios) using the Soil 

and Water Assessment Tool (SWAT) and Kinematic Runoff 

and Erosion Model (KINEROS2), with no climate change 

assumed. For runoff, sedimentation, and percolation, constrained and planned scenarios showed similar results, with 

some change in several watersheds. The open-development 

scenario predicted more intense runoff and sedimentation and 

reduced percolation, particularly near Benson, Ariz.

Kirchhof, Stephanie, Colby, B., and LaFrance, J., 1997, 

Evaluating the performance of benefit transfer—An 

empirical inquiry: Journal of Environmental Economics 

and Management, v. 33, no. 1, p. 75–93.

The authors used value transfer to compare transfer error 

for southwestern riparian recreation at four sites: Ramsey 

Canyon and the SPRNCA (both used for birdwatching), and 

Taos Box and the Lower Gorge (both used for rafting). The 

authors used payment-card contingent valuation mail surveys 

to estimate WTP. The equality of benefit functions between 

sites was rejected. Value transfer was most feasible from 

SPRNCA to Ramsey Canyon, but it was not supported from 

Ramsey Canyon to the SPRNCA or between the two New 

Mexico rafting sites.

Kroeger, Timm, 2005, Economic benefits of reintroducing 

the river otter (Lontra canadensis) into rivers in New 

Mexico: Washington, D.C., Defenders of Wildlife, 35 p.

The author used value transfer to estimate the benefits 

for New Mexico counties in which otter reintroduction was 

planned (estimated at $6–9.5 million net present value), using 

beneficiaries throughout the entire state of New Mexico ($9.8–

12.9 million net present value), using out-of-state anglers and 

wildlife watchers as beneficiaries ($5.8 million net present 

value), and using residents of the rest of the United States 

as beneficiaries ($1.2–3.2 million net present value). These 

values included recreational and nonuse values only; they did 

not include ecosystem services generated by otters and effects 

on fisheries themselves. This study is most useful in showing 

how to use a transfer function to look at the value of species 

reintroduction or population increases for the San Pedro, as it 

realistically accounts for beneficiary group size and WTP at 

different spatial scales.

Leenhouts, J.M., Stromberg, J.C., and Scott, R.L., 2005, 

Hydrologic requirements of and consumptive groundwater use by riparian vegetation along the San Pedro 

River, Arizona: U.S. Geological Survey Scientific Investigations Report 2005–5163, 211 p.

The authors estimated the riparian water needs for the 

Upper San Pedro River, on the basis of annual-water-use data 

for individual species and open water, the spatial extent of 

riparian vegetation, and the relationships between hydrology, 

water use, and species distribution. Evapotranspiration data 

are provided for mesquite woodland, mesquite shrubland, 

cottonwood in perennial and intermittent-flow stream reaches, 

sacaton, open water, and seepwillow vegetation types.
86 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Loomis, J.B., Douglas, A.J., and Harpman, D.A., 2005, 

Recreation use values and nonuse values of Glen and 

Grand Canyons, in Gloss, S.P., Lovich, J.E., and Melis, 

T.S., eds., The State of the Colorado River Ecosystem in Grand Canyon—A report of the Grand Canyon Monitoring and Research Center, 1991–2004: 

U.S. Geological Survey Circular 1282, p. 153–164.

The authors estimated recreational, regional economic, 

and nonuse values for Glen Canyon and the Grand Canyon 

under various dam-management scenarios. Recreational 

values included fishing- and rafting-associated expenditures, 

and local economic impacts estimated by using input-output 

modeling. Nonuse values were derived from a national survey 

and showed substantial WTP to improve flows and ecological conditions in the canyons, as might be expected for such 

well-recognized and charismatic sites.

Lowry, John, Ramsey, R.D., Thomas, K., Schrupp, 

D., Sajwaj, T., Kirby, J., Waller, E., Schrader, S., 

Falzarano, S., Langs, L., Manis, G., Wallace, C., Schulz, 

K., Comer, P., Pohs, K., Reith, W., Velasquez, C., Wolk, 

B., Kepner, W., Boykin, K., O’Brien, L., Bradford, D., 

Thompson, B., and Prior-Magee, J., 2007, Mapping 

moderate-scale land-cover over very large geographic 

areas within a collaborative framework—A case 

study of the Southwest Regional Gap Analysis Project 

(SWReGAP): Remote Sensing of Environment, v. 108, 

p. 59–73.

This paper described the results of the first multistate 

USGS Gap Analysis Program, which produced consistent 

mapping products for Arizona, Colorado, Nevada, New 

Mexico, and Utah. Landsat ETM+ data for 1999–2001 and 

digital elevation model derivatives were used to build a publicly available land-cover dataset with 125 land-cover classes, 

at 30 × 30 meter spatial resolution. Although not described in 

this paper, the group has since produced Southwest Regional 

Gap Analysis Project habitat models for 819 species in the 

five-state region.

Martens, D.A., Emmerich, W., McLain, J.E.T., and Johnsen, T.N., 2005, Atmospheric carbon mitigation potential 

of agricultural management in the southwestern USA: 

Soil and Tillage Research, v. 83, p. 95–119.

The authors reviewed and synthesized articles that report 

soil carbon storage, soil carbon sequestration, nitrous oxide, and 

methane fluxes from native ecosystems and agricultural systems 

under different crops and agricultural-management practices. 

Values were reported from the entire intermountain west—all 

four major North American deserts, California Mediterranean 

ecosystems, and tallgrass prairie in Texas and Oklahoma. 

Conservation tillage, crop rotation, past cropping, irrigation, 

salinity, grazing, soil texture, temperature, and precipitation are 

important influences on soil carbon in these ecosystems.

Martens, D.A., and McLain, J.E.T., 2005, Climate mitigation potential of the San Pedro River riparian zone, in 

Gottfried, G.J., Gebow, B.S., Eskew, L.G., Edminster, 

C.B., comps. Connecting mountain islands and desert 

seas—Biodiversity and management of the Madrean 

Archipelago II conference, May 11–14, Tucson, Ariz.: 

Fort Collins, Colo., U.S. Department of Agriculture Forest Service Rocky Mountain Research Station: Proceedings, RMRS–P–36, p. 491-495.

The authors estimated soil carbon content of soils in 

mesquite, acacia, mesquite-open, sacaton-mesquite, sacaton, 

and open herbaceous plant communities. Unlike McLain and 

Martens (2003), this report provides bulk density data, allowing estimation of carbon storage per unit area under various 

vegetation types.

McLain, J.E.T., and Martens, D.A., 2003, Vegetation community impacts on soil carbon, nitrogen, and trace gas 

fluxes, in Renard, K.G., McElroy, S.A., Gburek, W.J., 

Canfield, H.E., and Scott, R.L., eds., First Interagency 

Conference on Research in the Watersheds, October 

27–30, 2003, U.S. Department of Agriculture Agricultural Research Service, p. 542–547.

The authors reported on soil carbon storage in mesquite, 

sacaton, and annual herbaceous communities on the San Pedro 

River. They found mesquite patches to have about twice the 

soil carbon content of sacaton patches, mesquite-sacaton 

patches to have an intermediate content, and open herbaceous 

communities to have the lowest soil carbon content.

McLain, J.E.T., and Martens, D.A., 2005, Studies of methane fluxes reveal that desert soils can mitigate global 

change, in Gottfried, G.J., Gebow, B.S., Eskew, L.G., 

Edminster, C.B., comps. Connecting mountain islands 

and desert seas—Biodiversity and management of the 

Madrean Archipelago II conference, May 11–14, Tucson, 

Ariz.: Fort Collins, Colo., U.S. Department of Agriculture Forest Service Rocky Mountain Research Station: 

Proceedings, RMRS–P–36, p. 496–499.

The authors estimated methane consumption by 

microorganisms in desert soils for the San Pedro River and 

Santa Rita Experimental Range. They found methane consumption to be greatest in open patches, intermediate in 

mesquite, and lowest in sacaton grasslands. Methane consumption was strongly seasonal—rainfall pulses stimulated 

methane consumption, whereas the driest periods of the year 

had negligible consumption.
Appendix E 87

McLain, J.E.T., and Martens, D.A., 2006, Moisture controls on trace gas fluxes in semiarid riparian soils: Soil 

Science Society of America Journal, v. 70, p. 367–377.

The authors reported similar results as their 2003 study 

on carbon and nitrous oxide fluxes in mesquite, sacaton, and 

annual herbaceous communities on the San Pedro River. However, this study reported results for a 15-month period, providing results for two growing seasons with varying precipitation. 

As in past studies, precipitation strongly controlled gas flux 

patterns; greater activity generally characterized wetter seasons of the year and wetter compared with drier years.

McLain, J.E.T., Martens, D.A., and McClaran, M.P., 

2008, Soil cycling of trace gases in response to mesquite 

management in a semiarid grassland: Journal of Arid 

Environments, v. 72, p. 1654–1665.

The authors reported soil carbon storage data for live 

mesquite, dead mesquite, and open herbaceous patches in the 

Santa Rita Experimental Range, southern Arizona, and nitrous 

oxide and methane fluxes. They reported results from 2003 

and 2004 which, as did the San Pedro data from their 2006 

study, showed strong seasonal variation in soil moisture that 

subsequently drives respiration, photosynthesis, nitrous oxide 

fluxes, and methane consumption.

Miller S.N., Kepner, W.G., Mehaffey, M.H., Hernandez, 

M., Miller, R.C., Goodrich, D.C., Devonald, K.K., Heggem, D.T., and Miller, W.P., 2002, Integrating landscape 

assessment and hydrologic modeling for land-cover 

change analysis: Journal of the American Water 

Resources Association, v. 38, no. 4, p. 915–929.

The authors applied the Soil and Water Assessment Tool 

(SWAT) and Kinematic Runoff and Erosion Model (KINEROS) 

hydrologic models to the San Pedro (Arizona) and Cannonsville 

(New York) watersheds. Increasing urbanization, agriculture, 

and invasion of grasslands by woody plants led to greater 

annual and event runoff, flashier floods, and increased sedimentation, which can be used as indicators of watershed condition. 

The authors used KINEROS to model rainfall-runoff relationships on small watersheds and used SWAT for modeling larger 

watersheds at coarser time scales. The most useful hydrologic 

models in arid regions account for intense local rainfall, transmission losses into ephemeral stream channels, high evapotranspiration, lower total runoff, flashier events, less vegetation, and 

greater erosion potential. A lack of gaging stations was problematic for both models, because in arid environments regular gaging is needed for small watersheds, which respond differently to 

patchily distributed rainfall events.

Moeltner, Klaus, and Woodward, R., 2009, Meta-functional benefit transfer for wetland valuation—Making 

the most of small samples: Environmental and Resource 

Economics, v. 42, p. 89–108.

The authors used Bayesian meta-regression modeling 

to estimate the value of aridland wetlands in Nevada. These 

wetlands are fed by groundwater that is proposed for a transfer 

out of the basin to Las Vegas. Although the authors valued rare 

and unique wetlands using a sophisticated statistical technique, 

they did not draw on any primary wetland valuation studies 

that were specific to arid and semiarid environments or the 

Intermountain West.

Norman, Laura, Tallent-Halsell, N., Labiosa, W., Weber, 

M., McCoy, A., Hirschboeck, K., Callegary, J., van 

Riper, C., III, and Gray, F., 2010, Developing an ecosystem services online decision support tool to assess 

the impacts of climate change and urban growth in the 

Santa Cruz watershed—Where we live, work, and play: 

Sustainability, v. 2, no. 7, p. 2044–2069.

The authors described an application of the Ecosystem 

Portfolio Model to the Santa Cruz watershed in Arizona. This 

model incorporates environmental, economic, and quality-oflife metrics, such as human health and environmental justice, 

key considerations for cross-boundary watersheds. This model 

contains underlying hydrologic, urban growth, and habitat 

models based on Soil and Water Assessment Tool, Slope, Land 

cover, Exclusion, Urbanization, Transportation, and Hillshade 

model, and Southwest Regional Gap Analysis Project, respectively, and it uses an Intergovernmental Panel on Climate 

Change scenario to evaluate the effects of climate change on 

the watershed.

Orr, Patricia, and Colby, B.G., 2002, Nature-oriented 

visitors and their expenditures–Upper San Pedro 

River Basin: Department of Agricultural and Resource 

Economics, University of Arizona. Available at: http://

ag.arizona.edu/AREC/pubs/san_pedro_report.pdf, p. 17.

The authors administered expenditure surveys to 843 

visitors living outside the Upper San Pedro River watershed; 

the surveys were collected in February to May and in August 

2001 from the Ramsey Canyon Preserve and SPRNCA. 

Annual expenditures totaled $10.1–16.9 million. Use of a 1.68 

local multiplier gives $17–28.3 million per year in total local 

economic effects, providing 350–590 jobs. These expenditures do not include the opportunity cost of time and are only 

a partial component of consumer surplus from birding on the 

San Pedro River.
88 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Piper, Steven, and Martin, W.E., 1997, Household willingness to pay for improved rural water supplies—A 

comparison of four sites: Water Resources Research, v. 

33, no. 9, p. 2153–2163.

The authors estimated WTP for improved rural water 

at four sites in the Western United States: southeast South 

Dakota, northwest Oklahoma, north-central Montana, and the 

Navajo reservation of western New Mexico. Values ranged 

from $4.43 to $17.29 per household per month, which are 

comparable to values used in previous contingent-valuation 

studies of rural water supply. The authors concluded that value 

transfer is permissible using demographic and situational characteristics in a transfer function, including income, the cost of 

obtaining water, and measures of use.

Pool, D.R., and Dickinson, J.E., 2007, Ground-water flow 

model of the Sierra Vista and Sonoran portions of the 

Upper San Pedro Basin, southeastern Arizona, United 

States and northern Sonora, Mexico: U.S. Geological 

Survey Scientific Investigations Report 2006–5228, 60 p.

The authors described a groundwater model for portions 

of the Upper San Pedro River watershed using MODFLOW 

2000. Key elements in the model included groundwater 

flow in sedimentary rocks underlying alluvial basin deposits, withdrawals for dewatering at the Tombstone mine, 

discharge to springs in the Huachuca Mountains, thick lowpermeability intervals of silt and clay that separate the groundwater flow system into deep-confined and shallow-unconfined 

systems, ephemeral-channel recharge, and seasonal variations 

in groundwater discharge by wells and evapotranspiration.

Rankin, A.G., Eiler, L.M., and Joaquin, J.T., 2008, Water 

and the human spirit—Traditional sacred natural 

surface waters, in Altschul, J.H., and Rankin, A.G., eds., 

Fragile patterns—The archaeology of the western Papagueria: Tucson, Ariz., SRI Press, p. 595–604.

The authors described how American Indians value the 

Western Papagueria region of southwest Arizona and northwestern Sonora. American Indians in the region view the land 

itself as sacred, with the landscape “dotted with special places 

of spiritual value and power,” that are intersections of the 

physical and spirit worlds, such as Baboquivari Peak for the 

Tohono O’odham. Water plays a critical role, both physically 

and spiritually. Although the region has extremely limited 

surface waters (most streams are ephemeral), locally important 

features such as charcos (mudholes found on adobe flats and 

washes), playas, springs, and tinajas (rock-cut tanks, plunge 

pools, or potholes) are key resources for survival and are also 

held as sacred sites. These sites also have historical value as 

part of cultural migrations, trade routes, and creation or origin 

stories, as sources of healing.

Richardson, Leslie, and Loomis, J., 2009, The total economic value of threatened, endangered, and rare species—An update meta-analysis: Ecological Economics, 

v. 68, no. 5, p. 1535–1548.

The authors updated Loomis and White’s 1996 metaanalysis of endangered species values, incorporating newer 

studies (which have generally yielded higher WTP); along 

with survey, species, and respondent characteristics to identify 

how these factors influence WTP. The authors found an 

average transfer error of 34 percent for annual payments and 

45 percent for lump-sum payments.

Richardson, R.B., 2008, Conceptualizing the value of 

ecosystem services in deserts, in Chapman, R.L., ed., 

Creating sustainability within our midst—Challenges 

for the 21st century: New York, Pace University Press, 

p. 225–248.

The author qualitatively described various ecosystem 

services provided by deserts, with a focus on public lands 

and their component wilderness areas within the Great Basin, 

Mojave, and Sonoran desert region of southeastern California. 

The area included 10 million acres of BLM land, 3 million 

acres of which are designated as wilderness. The study provided 

economic estimates for the value of dust control in reducing 

respiratory health problems for people, valued at $24 million 

per year, the value of increased visibility for recreation in 

national parks, valued at $48 million per year, and recreation, 

valued at $159 million per year.

Rimbey, N.R., Torell, L.A., and Tanaka, J.A., 2007, Why 

grazing permits have economic value: Journal of Agricultural and Resource Economics, v. 32, no. 1, p. 20–40.

The authors explored the question of why grazing permits 

have value in regions where livestock production potential 

is very low. The authors used a dataset of ranch sales from 

New Mexico and the Great Basin to show that less than 

16 percent of the marginal value of grazing permits in New 

Mexico is attributed to livestock production, and this value 

is zero for some Great Basin ranches. The area of deeded 

land plus leased public land did matter, however, and size 

rather than productivity determined price. The market value 

of grazing permits has declined as public lands are managed 

more for other values, whereas the value of ranches themselves is increasingly determined by productivity, minus a 

discount for public land leases where the rancher can run cattle 

but does not own the land, plus other aesthetic, recreational, 

and locational values.

Schlessinger, W.H., 1982, Carbon storage in the caliche of 

arid soils—A case study from Arizona: Soil Science, v. 

133, no. 4, p. 247–255. 

The author recorded soil carbon storage in 91 of 189 

Arizona soil profiles, which were sampled to depths of at least 

125 centimeters. Noting the importance of carbon storage in 
Appendix E 89

carbonate minerals in the soil in arid environments, this study 

found overall carbonate carbon to be five times as great as soil 

organic carbon statewide. However, carbonate carbon is more 

prevalent on limestone-soil parent material and less important 

in regions with greater mean annual precipitation.

Schlessinger, W.H., Belnap, J., and Marion, G., 2009, 

On carbon sequestration in desert ecosystems: Global 

Change Biology, v. 15, no. 6, p. 1488–1490.

The authors compared recent reports of high levels of 

net ecosystem production in desert ecosystems (more than 

100 grams of carbon per square meter per year) to numerous 

other values in the literature. They argued that these recent 

measurements are unrealistically high, and that gas exchange 

measurements need to be better compared to empirical data 

previously collected for desert carbon pools in vegetation 

and soils.

Scott, R.L., Edwards, E.A., Shuttleworth, W.J., Huxman, 

T.E., Watts, C., and Goodrich, D.C., 2004, Interannual 

and seasonal variation in fluxes of water and carbon 

dioxide from a riparian woodland ecosystems: Agricultural and Forest Meteorology, v. 122, p. 65–84.

The authors measured water, energy, and CO2 fluxes from 

a mesquite woodland along the San Pedro River using the 

eddy covariance method for the 2001 and 2002 growing seasons. Mesquite accessed groundwater when precipitation was 

scarce (particularly in 2002, the drier year), and evapotranspiration and carbon sequestration varied throughout the growing 

season on the basis of water availability and leaf biomass.

Scott, R.L., Huxman, T.E., Williams, D.G., and Goodrich, 

D.C., 2006, Ecohydrological impacts of woody-plant 

encroachment—Seasonal patterns of water and carbon 

dioxide exchange within a semiarid riparian environment: Global Change Biology, v. 12, p. 311–324.

This study compared carbon fluxes in riparian grasslands, grassland-shrubland mosaics, and woodlands on the 

San Pedro River. Data were for the year 2003, which was a 

dry year. Generally, woody plants can sequester more carbon, 

as they are better able to tap shallow groundwater and avoid 

drought limitation, but they also have greater litterfall, which 

enables more respiration, offsetting its higher photosynthetic 

potential. The authors found that before leaf-out, precipitation 

drove respiration (that is, respiration occurred when there was 

enough water), leading the ecosystem to be a net source of 

carbon. During the growing season, sequestration led vegetation to be a net sink for CO2

, and the greatest sequestration and 

evapotranspiration was in woodlands, followed by shrublands, 

then grasslands.

Scott, R.L., Jenerette, G.D., Potts, D.L., and Huxman, T.E., 

2009, Effects of seasonal drought on net carbon dioxide exchange from a woody-plant-encroached semiarid 

grassland: Journal of Geophysical Research, v. 114, 

G04004.

The authors compared seasonal and interannual effects 

of rainfall on carbon dioxide exchange, using 2004–2007 data 

for mesquite savannas at the Santa Rita Experimental Range. 

During this period, annual precipitation was below average, 

and monsoon precipitation was both above and below average 

in different years while winter rain totals were below average 

in all years. Ecosystems emitted low to moderate quantities of 

CO2 to the atmosphere. This effect was especially pronounced 

during drought years, when growth is water-limited and less 

overall photosynthesis takes place. The ecosystem was carbon 

neutral when rainfall was close to the long-term average in 

distribution and amount. Cool-season drought tended to lead 

to lower spring carbon uptake, greater summer respiration, and 

high net carbon loss. Thus, while these ecosystems are carbon 

sinks in wetter years, the dry winter rain periods led to their 

being sources of atmospheric carbon during this study period.

Sengupta, Sanchita, and Osgood, D.E., 2003, The value of 

remoteness—A hedonic estimation of ranchette prices: 

Ecological Economics, v. 44, p. 91–103.

The authors presented a hedonic model of ranchette 

prices in central Arizona, exploring the influence of greenness, 

access to roads, cities, and neighbors, adjacency to public 

land, and distance to rivers on property values. They found 

that greenness and proximity to rivers increased sale price, as 

did access to roads, cities, and neighbors—implying that isolation was a disamenity and water availability was valued as an 

amenity.

Serrat-Capdevila, Aleix, Valdes, J.B., Gonzalez Perez, J., 

Baird, K., Mata, L.J., and Maddock, T., III, 2007, Modeling climate change impacts—and uncertainty—on the 

hydrology of a riparian system—The San Pedro Basin 

(Arizona/Sonora): Journal of Hydrology, v. 347, p. 48–66.

The authors used seventeen general circulation models and four Intergovernmental Panel on Climate Change 

climate-change scenarios to explore their effects on the San 

Pedro River watershed for the period 2000–2100. They linked 

rainfall, recharge, groundwater extraction, and riparian health. 

The authors used an average of 17 models, along with high 

and low values for temperature and precipitation, to generate 

estimates of uncertainty. The authors estimated that recharge 

in the SPRNCA would decline 25–80 percent depending on 

the severity of precipitation decline in the climate scenario. 

Subsequently, cottonwood, willow, and sacaton likely would 

decline and be replaced with more-drought-tolerant mesquite 

and tamarisk.
90 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Serrat-Capdevila, Aleix, Browning-Aiken, A., Lansey, 

K., Finan, T., and Valdes, J.B., 2009, Increasing socioecological resilience by placing science at the decision 

table—The role of the San Pedro Basin (Arizona) Decision Support System Model: Ecology and Society, v. 14, 

no. 1, p. 37. 

This paper described the decision support system built to 

aid decisionmaking in the Upper San Pedro watershed. This 

system integrates hydrologic and ecologic models that allow 

users to explore alternative means to reduce the watershed’s 

hydrologic deficit. The authors discussed the decision support 

system in the context of building resilience into the system 

and the involvement of stakeholders in the process of developing and using it.

Steinitz, Carl, Arias, H., Bassett, S., Flaxman, M., Goode, 

T., Maddock, T., III, Mouat, D., Peiser, R., and Shearer, 

A., 2003, Alternative futures for changing landscapes—

The Upper San Pedro River Basin in Arizona and 

Sonora: Washington, D.C., Island Press, 202 p.

This book described model outputs that explored the consequences of alternative growth scenarios on numerous landscape and ecological metrics, including hydrology, vegetation, 

landscape connectivity, species of conservation concern, vertebrate species richness, and visual preference. They compared 

9 urban growth scenarios for the year 2020, 3 each showing 

“open,” “constrained,” and intermediate “planned” scenarios 

compared with a year 2000 baseline. These scenarios differed 

in the degree of growth, its specific locations, the future of 

Fort Huachuca, and trends in conservation and agriculture in 

both the United States and Mexican parts of the watershed.

Striegl, R.G., McConnaughey, T.A., Thorstenson, D.C., 

Weeks, E.P., and Woodward, J.C., 1992, Consumption 

of atmospheric methane by desert soils: Nature, v. 357, 

p. 145–147.

The authors estimated consumption of atmospheric methane, a strong greenhouse gas, by microorganisms in desert soils. 

Like carbon sequestration, aridland methane consumption was 

highly seasonal, and rainfall was a strong driver of methane 

consumption. While per-acre methane consumption was relatively small when compared to global carbon sinks and greenhouse gas emissions, deserts had a larger combined influence on 

atmospheric methane owing to their large extent globally.

Stromberg, J.C., Lite, S.J., Rychener, T.J., Levick, L.R., 

Dixon, M.D., and Watts, J.M., 2006, Status of the riparian ecosystem in the Upper San Pedro River—Application of an assessment model: Environmental Monitoring 

and Assessment, v. 115, p. 145–173.

The authors developed and applied a model to assess 

stream segments on the basis of hydrologic, geomorphic, 

and biological indicators. They delineated 14 reaches on the 

Upper San Pedro River within the SPRNCA, and classified 

these reaches as dry (1 reach), intermediate (8 reaches), or wet 

(5 reaches). This assessment model can be used to quantify 

riparian condition and has been used in the Upper San Pedro 

Decision Support System and in studies to economically value 

restoration or avoided degradation of the riparian ecosystem.

Stromberg, J.C., Beauchamp, V.B., Dixon, M.D., Lite, 

S.J., and Paradzick, C., 2007, Importance of low-flow 

and high-flow characteristics to restoration of riparian 

vegetation along rivers in the arid south-western United 

States: Freshwater Biology, v. 52, p. 651–679.

The authors described the effect of surface-flow 

frequency (perennial to intermittent to ephemeral) and 

the role of flooding and flow regulation on riparian plant 

communities and processes. Species diversity and nativespecies dominance were influenced by surface flow and 

flood frequency, and “reference” rivers where these natural 

processes are still intact harbor greater species diversity, 

landscape heterogeneity, and abundance of wetland plant 

species. Maintaining these processes on reference rivers 

and restoring them on degraded rivers (for example, by 

dam removal, controlled dam releases, or purchase of water 

rights) is important to riparian restoration.

Stromberg, J.C., Lite, S.J., Marler, R., Paradzick, C., 

Shafroth, P.B., Shorrock, D., White, J.M., and White, 

M.S., 2007, Altered stream-flow regimes and invasive 

plant species—The Tamarix case: Global Ecology and 

Biogeography, v. 16, p. 381–393.

This study examined the relationship between flow regulation and the dominance of woody plant species within the 

Gila River and Lower Colorado River riparian corridors. Nonnative and more-drought-resistant species such as tamarisk are 

more common on flow-regulated regions, where typical dam 

management creates flows timed to favor their establishment 

rather than establishment of native cottonwood and willow. 

This observation shows the importance of flow management 
Appendix E 91

for maintaining and restoring native riparian communities, and 

the impracticality of restoration without addressing flooding, 

the key disturbance process in these systems.

Stromberg, J.C., and Tellman, B., 2009, Ecology and conservation of the San Pedro River: Tucson, University of 

Arizona Press, 544 p.

This book synthesized research on the hydrology, geomorphology, and ecology of the San Pedro River and placed 

this research within the watershed’s historical and contemporary socioeconomic context. It discussed prospects for future 

restoration and degradation of the San Pedro on the basis of 

system drivers such as groundwater depletion, urbanization, 

and climate change.

Svejcar, Tony, Angell, R., Bradford, J.A., Dugas, W., 

Emmerich, W., Frank, A.B., Gilmano, T., Haferkamp, 

M., Johnson, D.A., Mayeux, H., Mielnick, P., Morgan, J., 

Saliendra, N.Z., Schuman, G.E., Sims, P.L., and Snyder., 

K., 2008, Carbon fluxes on North American rangelands: 

Rangeland Ecology and Management, v. 61, p. 465–474.

The authors synthesized data on rangeland net ecosystem 

exchange for eight United States grassland sites ranging from 

the Great Plains and northern Rockies to Texas and the desert 

Southwest. They found drought to be a critical influence on 

carbon sequestration, and that the Southwestern sites were 

sources rather than sinks of carbon in most years. Seasonal 

patterns shifted between carbon uptake during wet periods and 

balance or respiratory release of carbon during dry periods.

Torell, L.A., Rimbey, N.R., Ramirez, O.A., and McCollum, 

D.W., 2005, Income earning potential versus consumptive amenities in determining ranchland values: Journal 

of Agricultural and Resource Economics, v. 30, no. 3, 

p. 537–560.

This study estimated the hedonic value of ranches; 

amenities were compared with income-earning potential from 

traditional ranching. The authors found differential value for 

grazing leases on different types of land, in line with grazing 

fees and regulations on stocking rates. They concluded that 

grazing permit fee reform on public lands needs to account for 

the fact that many ranch owners are looking to maximize utility by means of recreational and amenity values, rather than by 

ranch production profit alone.

Venn, T.J., and Quiggin, J., 2007, Accommodating indigenous cultural heritage values in resource assessment—

Cape York Peninsula and the Murray-Darling Basin, 

Australia: Ecological Economics, v. 61, p. 334–344.

The authors discussed the limitations of nonmarket valuation in understanding indigenous preferences for landscape 

and environmental features. They suggested that incorporating 

quantity constraints on resource use that are compatible with 

indigenous rights and beliefs is a means of fairly addressing 

these challenges without imposing an external economic value 

system in the process.

Weber, M.A., and Berrens, R.P., 2006, Value of instream 

recreation in the Sonoran Desert: Journal of Water 

Resources Planning and Management, v. January/February 2006, p. 53–60.

The authors used the zonal-travel cost method to estimate 

the value of backcountry recreation in the Aravaipa Canyon 

Wilderness (the Aravaipa River is a tributary to the San Pedro 

River, with perennial surface flow). They found consumer surplus of $25.06 and $17.31 for two access points which, given 

total annual visitation of 7,800, corresponded to annual values 

of $55,000 and $88,000 for the two access points.

Weber, M.A., and Stewart, S., 2009, Public values for river 

restoration options on the Middle Rio Grande: Restoration Ecology, v. 17, no. 6, p. 762–771.

The authors used choice experiments and contingent valuation to estimate WTP for river restoration of the Middle Rio 

Grande Bosque in Albuquerque, N. Mex., covering 17 miles 

of river and 4,000 acres of riparian habitat. They estimated 

annual WTP on the order of $4.1 million per river mile or 

$1,038 per acre of riparian habitat on the basis of the number 

of households in Albuquerque.

Wheeler, C.W., Archer, S.R., Asner, G.P., and McMurtry, 

C.R., 2007, Climatic/edaphic controls on soil carbon/

nitrogen response to shrub encroachment in desert 

grassland: Ecological Applications, v. 17, no. 7, p. 

1911–1928.

The authors compared soil carbon and nitrogen levels 

at plots with minimal mesquite cover, young mesquite cover, 

and old mesquite cover. Their study site was the Santa Rita 

Experimental Range south of Tucson, Ariz. The authors found 

increased carbon and nitrogen in soils progressing from grasslands to old mesquite patches.
92 Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona

Table F1. Primary economic valuation studies for the San Pedro River watershed, Arizona, and the Southwest.

[See Appendix E for full references. n/a, not available; prep., preparation; SPRNCA, San Pedro Riparian National Conservation Area; T&E, threatened and 

endangered; WTP, willingness to pay]

Study Region Ecosystem service

Value

(dollars/ 

acre-year)

Bark-Hodgins and Colby, 2006 Northeast Tucson, Ariz. Amenity 235

Bark and others, 2009 Northeast Tucson, Ariz. Amenity n/a

Berrens and others, 1996 Middle Rio Grande, N. Mex. In-stream flows for fish; use and nonuse value 150.09

Berrens and others, 1996 Rio Grande, Gila, Pecos, San Juan 

Rivers, N. Mex.

In-stream flows for fish; use and nonuse value 79.67

Berrens and others, 1998 New Mexico Range production 0.71

Berrens and others, 2000 Middle Rio Grande, N. Mex. In-stream flows for fish; use and nonuse value 138.02

Berrens and others, 2000 Rio Grande, Gila, Pecos, San Juan 

Rivers, N. Mex.

In-stream flows for fish; use and nonuse value 64.12

Brookshire and others, 2010 Upper San Pedro, Ariz. Use and nonuse value for stream flow, vegetation 

condition, bird habitat

In prep.

Brookshire and others, 2010 Upper San Pedro, Ariz. Use and nonuse value for stream flow, vegetation 

condition, bird habitat

In prep.

Brookshire and others, 2010 Middle Rio Grande, N. Mex. Use and nonuse value for stream flow, vegetation 

condition, bird habitat

In prep.

Brookshire and others, 2010 Middle Rio Grande, N. Mex. Use and nonuse value for stream flow, vegetation 

condition, bird habitat

In prep.

Colby and Orr, 2005 Upper San Pedro, Ariz. Recreation—bird watching, nonuse value 4.54–7.64

Colby and Smith-Incer, 2005 Kern River, Calif. Recreation—bird watching, nonuse value 503–665

Colby and Smith-Incer, 2005 Kern River, Calif. Recreation—bird watching 1,187–1,618

Colby and Wishart 2002 Tanque Verde Wash, Tucson, Ariz. Amenity 744

Crandall and others, 1992 Hassayampa River, Ariz. Recreation—hiking, bird watching, nonuse value 1,428

Crandall and others, 1992 Hassayampa River, Ariz. Recreation—hiking, bird watching 1,684

Crandall and others, 1992 Hassayampa River, Ariz. Recreation—hiking, bird watching 242

Hand and others, 2008 Arizona and New Mexico Amenity (including recreation) n/a

Kirchoff and others, 1997 SPRNCA and Ramsey Canyon n/a n/a

Kirchoff and others, 1997 Taos Box, N. Mex. Recreation—whitewater rafting n/a

Kirchoff and others, 1997 Lower Gorge, N. Mex. Recreation—whitewater rafting n/a

Orr and Colby, 2002 Upper San Pedro, Ariz. Recreation—bird watching 165

Orr and Colby, 2002 Ramsey Canyon, Ariz. Recreation—bird watching 268

Rimbey and others, 2007 New Mexico and Great Basin Ranch value n/a

Sengupta and Osgood, 2003 Yavapai County, Ariz. Amenity n/a

Torell and others, 2005 New Mexico Amenity; recreation—hunting; beef production n/a

Weber and Berrens, 2006 Aravaipa Canyon, Ariz. Recreation—backcountry, wilderness 30.31

Weber and Stewart, 2009 Middle Rio Grande, N. Mex. Vegetation, fish and wildlife, river processes, 

nonuse value

331.29

Weber and Stewart, 2009 Middle Rio Grande, N. Mex. Vegetation, fish and wildlife, river processes, 

nonuse value

1,108.55

Appendix F. Summary of Primary Economic Valuation Studies for the San Pedro 

River Watershed and the U.S. Southwest
Appendix F 93

Table F1. Primary economic valuation studies for the San Pedro River watershed, Arizona, and the Southwest.—Continued

[See Appendix E for full references. n/a, not available; prep., preparation; SPRNCA, San Pedro Riparian National Conservation Area; T&E, threatened and 

endangered; WTP, willingness to pay]

Study method Beneficiaries Scenario

Hedonic pricing Northeast Tucson homeowners Riparian restoration of 9,432 acres.

Hedonic pricing Northeast Tucson homeowners n/a

Contingent valuation

(dichotomous choice)

New Mexico residents WTP, water rights to preserve in-stream flow for silvery minnow 

along 170 miles of Middle Rio Grande.

Contingent valuation

(dichotomous choice)

New Mexico residents WTP, water rights to preserve in-stream flow for 11 T&E fish species 

on 1,000+ miles of four major New Mexico rivers.

Contingent valuation

(open-ended)

New Mexico ranchers WTP to compensate ranchers for higher Federal grazing lease prices.

Contingent valuation

(dichotomous choice)

New Mexico residents WTP, water rights to preserve in-stream flow for silvery minnow on 

170-mi stretch of Middle Rio Grande.

Contingent valuation

(dichotomous choice)

New Mexico residents WTP, water rights to preserve in-stream flow for 11 T&E fish species 

on 1000+ miles of four major New Mexico rivers.

Contingent valuation

(dichotomous choice)

Arizona residents Various degradation or restoration scenarios.

Choice modeling Arizona residents Various degradation or restoration scenarios.

Contingent valuation

(dichotomous choice)

New Mexico residents Various degradation or restoration scenarios.

Choice modeling New Mexico residents Various degradation or restoration scenarios.

Contingent valuation

(payment card)

Nonresident visitors to SRPNCA and 

Ramsey Canyon

WTP, avoid dewatering and degradation.

Contingent valuation

(payment card)

Visitors to Kern River Preserve, Calif. WTP, avoid loss of streamflow, habitat degradation, loss of key species.

Consumer expenditure

and multiplier

Visitors to Kern River Preserve, Calif. n/a

Hedonic pricing Northeast Tucson homeowners n/a

Contingent valuation

(payment card)

Visitors to Hassayampa Preserve WTP to avoid loss of perennial flow.

Zonal travel cost Visitors to Hassayampa Preserve, Ariz. n/a

Consumer expenditure

and multiplier

Visitors to Hassayampa Preserve, Ariz. n/a

Hedonic pricing

(wage and housing)

Arizona and New Mexico residents n/a

n/a n/a n/a

Consumer expenditure Rafters on Taos Box WTP, one trip at previously experienced streamflow level.

Consumer expenditure Rafters on Lower Gorge WTP, one trip at previously experienced streamflow level.

Consumer expenditure

and multiplier

Nonresident visitors to SPRNCA n/a

Consumer expenditure

and multiplier

Nonresident visitors to Ramsey Canyon n/a

Hedonic pricing New Mexico and Great Basin ranch owners n/a

Hedonic pricing Yavapai County ranchette owners n/a

Hedonic pricing New Mexico ranch owners n/a

Zonal travel cost Visitors to Aravaipa Canyon n/a

Contingent valuation

(payment card)

Albuquerque residents WTP, various restoration scenarios.

Choice modeling Albuquerque residents WTP, various restoration scenarios.
Publishing support provided by:

Denver Publishing Service Center, Denver, Colorado

For more information concerning this publication, contact:

Center Director, USGS Geosciences and 

Environmental Change Science Center

Box 25046, Mail Stop 980

Denver, CO 80225

(303) 236-5345

Or visit the Geosciences and Environmental Change Science Center Web site at:

http://esp.cr.usgs.gov/

This report is available at: http://pubs.usgs.gov/sir/2012/5251
Bagstad and others—Ecosystem Services Valuation to Support Decisionmaking on Public Lands—San Pedro River Watershed, Arizona—Scientific Investigations Report 2012–5251