oEPA
United States
Environmental Protection
Agencv
EPA645/R-20-002 | December 2020 | www.epa.gov/research
Metrics for National and Regional
Assessment of Aquatic, Marine, and
Terrestrial F nal Ecosystem Goods and
Services
Office of Research and Development
Center for Public Health & Environmental Assessment
Pacific Ecological Systems Division
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EPA645/R-20-002
December 2020
by
Paul Ringold, Colin Phifer, and Faye Andrews (editors)
Pacific Ecological Systems Division
Center for Public Health & Environmental Assessment
Paul Ringold, Project Officer
Pacific Ecological Systems Division
Center for Public Health & Environmental Assessment
Corvallis, Oregon 97333
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EPA645/R-20-002
December 2020
Metrics for National and Regional Assessment of Aquatic,
Marine, and Terrestrial Final Ecosystem Goods and Services
Editors: Paul Ringold1, Colin C. Phifer2, Faye V. Andrews3
Contributors: Ted R. Angradi4, Walter Berry5, Timothy J. Canfield6, Andrew Gray7, Christina
Horstmann8, James S. Latimer5, Amanda M. Nahlik2, Dave Peck2, Deborah Santavy9' Leah M.
Sharpe9, Kimberly M. Schuerger10
Contributors are listed in alphabetical order
1 U.S. Environmental Protection Agency (U.S. EPA), Pacific Ecological Systems Division,
Center for Public Health and Environmental Assessment, Corvallis, OR 97333
2 U.S. EPA, Pacific Ecological Systems Division, Center for Public Health and Environmental
Assessment, Corvallis, OR 97333 [Oak Ridge Institute for Science Education, Postdoctoral
Fellow]
3 U.S. EPA, Pacific Ecological Systems Division, Center for Public Health and Environmental
Assessment, Corvallis, OR 97333 [Oak Ridge Associated Universities, Student Services
Contractor]
4 U.S. EPA, Great Lakes Toxicology and Ecology Division, Center for Computational
Toxicology and Ecology, Duluth, MN 55804
5 U.S. EPA, Atlantic Coastal Environmental Sciences Division, Center for Environmental
Measurement and Modeling, Narragansett, RI 02882
6 U.S. EPA, Groundwater Characterization and Remediation Division, Center for
Environmental Solutions and Emergency Response, Ada, OK 74820
7 U.S. Department of Agriculture Forest Service, Resource Monitoring and Assessment
Program, Pacific Northwest Research Station, Corvallis, OR 97333
8 U.S. EPA, Gulf Ecosystem Measurement and Modeling Division, Center for Environmental
Measurement and Modeling, Gulf Breeze, FL 32561 [Oak Ridge Institute for Science
Education Participant,]
9 U.S. EPA, Gulf Ecosystem Measurement and Modeling Division, Center for Environmental
Measurement and Modeling, Gulf Breeze, FL 32561
10 U.S. EPA, Groundwater Characterization and Remediation Division, Center for
Environmental Solutions and Emergency Response, Ada, OK 74820 [Oak Ridge Associated
Universities, Student Services Contractor]
Suggested citation for this report:
U.S. Environmental Protection Agency. 2020. Metrics for national and regional assessment of
aquatic, marine, and terrestrial final ecosystem goods and services. EPA645/R-20-002. U.S.
Environmental Protection Agency, https://www.epa.gov/eco-research/national-ecosvstem-
services-classification-system-nescs-plus.
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FEGS Metrics
Front Matter
Table of Contents
Preface vi
Acknowledgements vii
Acronyms and Abbreviations viii
Glossary ix
Executive Summary 1
1. Introduction 3
1.1 FEGS Framework 4
1.2 Three Principles of the FEGS Framework 6
1.3 Using the FEGS Framework in Decision-making 7
1.4 FEGS Metric Selection Process 10
1.5 Report Obj ectives 11
2. Methods 12
2.1 Step 1: Ecosystem Delineation 14
2.2 Step 2: Beneficiary Specification 14
2.3 Step 3: Attribute Specification 16
2.4 Step 4: Metric Specification 19
2.5 Step 5: Data Sources and Availability at Regional or National Scales 21
2.6 Example Data Visualizations 23
3. Results 24
3.1 Coral Reefs 25
Step 1. Ecosystem Delineation 26
Step 2. Beneficiary Specification 27
Step 3. Attribute Specification 30
Step 4. Metric Specification 30
Step 5. Data Sources and Availability 31
Example Visualizations for FEGS Metrics in Coral Reefs 32
3.2 Estuaries 34
Step 1. Ecosystem Delineation 35
Step 2. Beneficiary Specification 35
Step 3. Attribute Specification 38
Step 4. Metric Specification 38
Step 5. Data Sources and Availability 39
Example Visualizations for FEGS Metrics in Estuaries 40
3.3 Lakes 41
Step 1. Ecosystem Delineation 42
Step 2. Beneficiary Specification 42
Step 3. Attribute Specification 43
Step 4. Metric Specification 43
Step 5. Data Sources and Availability 44
Example Visualizations for FEGS Metrics in Lakes 44
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FEGS Metrics
Front Matter
3.4 Rivers and Streams 47
Step 1. Ecosystem Delineation 47
Step 2. Beneficiary Specification 48
Step 3. Attribute Specification 51
Step 4. Metric Specification 51
Step 5. Data Sources and Availability 52
Example Visualizations for FEGS Metrics in Rivers and Streams 52
3.5 Wetlands 54
Step 1. Ecosystem Delineation 55
Step 2. Beneficiary Specification 55
Step 3. Attribute Specification 56
Step 4. Metric Specification 57
Step 5. Data Sources and Availability 57
Example Visualizations for FEGS Metrics in Wetlands 58
3.6 Agricultural Systems 60
Step 1. Ecosystem Delineation 61
Step 2. Beneficiary Specification 61
Step 3. Attribute Specification 63
Step 4. Metric Specification 63
Step 5. Data Sources and Availability 64
Example Visualizations for FEGS Metrics in Agricultural Systems 64
3.7 Forests 67
Step 1. Ecosystem Delineation 67
Step 2. Beneficiary Specification 68
Step 3. Attribute Specification 69
Step 4. Metric Specification 69
Step 5. Data Sources and Availability 70
Example Visualizations for FEGS Metrics in Forests 70
3.8 Cross-ecosystem Results Synthesis 73
Availability of Spatially Explicit Data 73
Number of Metrics per Beneficiary 74
Representation of Ecosystems for Non-use Beneficiaries 75
Form of FEGS Metrics 75
3.9 Challenges to Providing Data on FEGS 76
4. Discussion 78
4.1 Application and Use of FEGS to Decision-makers 78
4.2 Metric Identification Process and the Classification System 79
4.3 Research Needs 80
5. Conclusion 81
6. Literature Cited 83
Appendix: Detailed FEGS Metrics Table
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Front Matter
List of Tables
Table 1. Ecosystem Classification Used by NESCS Plus 13
Table 2. Beneficiary Classifications from NESCS Plus 15
Table 3. Ecosystem-specific Beneficiaries Considered for this Report by NESCS Plus
Beneficiary Class and Subclass 17
Table 4. Prescribed Attribute Categorization Used to Describe the Physical Components
of the FEGS Metric 18
Table 5. Possible Metrics of Water Clarity 21
Table 6. Available FEGS Metrics for Beneficiaries of Coral Reefs 27
Table 7. Available FEGS Metrics for Beneficiaries of Estuaries 35
Table 8. Available FEGS Metrics for Beneficiaries of Lakes 42
Table 9. Available FEGS Metrics for Beneficiaries of Rivers and Streams 49
Table 10. Available FEGS Metrics for Beneficiaries of Wetlands 55
Table 11. Available FEGS Metrics for Beneficiaries of Agricultural Systems 61
Table 12. Available FEGS Metrics for Beneficiaries of Forests 68
List of Figures
Figure ES. FEGS metrics are the subset of ecosystem features that best link to human
well-being 1
Figure 1. The FEGS Framework and taxonomy of linkages between the
environment/ecosystems and human systems 5
Figure 2. Conceptual model of the central role of FEGS in decision-making analysis by
EPA's Sustainable and Healthy Communities program or other agencies 8
Figure 3. Relationship between NESCS Plus, FEGS Community Scoping Tool, and
FEGS Metrics (this report) 10
Figure 4. Percent live coral cover on the Great Barrier Reef, Australia 32
Figure 5. Secchi disk depth for the Great Barrier Reef, Australia, 1992-2006 33
Figure 6. Winter Flounder and American Lobster (a) captured in an individual trawl at a
single station in Narragansett Bay, and (b) landed and brought to the docks in
Narragansett Bay 40
Figure 7. Regional estimates of Secchi depth by ecoregion (2012 National Lakes
Assessment) 45
Figure 8. Great Lakes Beach Hazard forecast for September 8, 2020 45
Figure 9. Wild rice harvesting license sales by zipcode combining 2005 and 2006 for
Minnesota 46
Figure 10. Stream biotic integrity graphed for major regions of the contiguous United
States 53
Figure 11. Vegetation condition graphed for major regions of the contiguous United
States 59
Figure 12. An example of a FEGS metric for soil productivity for farmers (Soil
Productivity Index) for Midwestern states 65
Figure 13. Estimates of deer density across the United States, an example of a spatial
visualization of the FEGS metric for deer hunters 66
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Front Matter
Figure 14. Area of forestland (and standard error) covered by various plant species in
Washington state, 2006-2015, based on FIA data 71
Figure 15. Total antlered and antlerless elk harvested in Washington State (2001-2013) 71
Figure 16. Population estimates for elk in Washington State based on models that relate
habitat to categories of elk abundance 72
Figure 17. Number of metrics listed for each General Attribute for the 45 beneficiaries
analyzed 74
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Front Matter
Preface
Ecologists and other biophysical scientists use a host of ecological metrics to observe,
understand, assess, and predict ecosystem features. These metrics are defined by sets of
principles well established by biophysical scientists; however, when biophysical scientists seek
to provide information other scientists can use in their analyses, they need to identify and apply
an added set of principles. The use of biophysical outcomes by social scientists, especially in the
quantification of benefits, is one such clear and increasing need (U.S. EPA, 2009). The
publication of "What are ecosystem services? The needfor standardized environmental
accounting units'' by Boyd and Banzhaf (2007) offered the beginnings of a set of principles, well
grounded in economic theory, that appeared to offer a way for biophysical scientists to identify
the subset of ecological metrics that would meet the needs of benefits analysis. The purpose of
this report is to describe how an interdisciplinary team translated the principles delineated by
Boyd and Banzhaf into a set of practices and metrics applicable to a broad set of ecosystems and
the ways in which people benefit from them.
This work focuses on metrics and principles for national and regional scales of analysis. As such,
the metrics and the reports based on them are expected to be more useful for agents acting on
behalf of collections of individuals as they interact with ecosystems, not necessarily individuals
as they make decisions on a day-to-day basis. Having said this, the process and the results that
our ecosystem experts went through, and the metrics identified, should be a useful starting point
for those focusing on community scales of analysis.
This report is one part of a suite of three related tools developed by the U.S. Environmental
Protection Agency (EPA) that use the same Framework:
1. The National Ecosystem Services Classification System (NESCS Plus; Newcomer-
Johnson et al., 2020)—A classification system for ecosystem services that provides a
consistent architecture and taxonomy, as well as the rationale for and a consistent
delineation of the three dimensions of our shared Framework: beneficiaries,
environmental classes, and attributes. It also contains tables of the relationships between
dimensions.
2. Metrics for National and Regional Assessment of Aquatic, Marine, and Terrestrial
Final Ecosystem Goods and Services (FEGS Metrics; this report)—The metrics and
general process for metric selection, which are key elements of the related tools.
3. FEGS Community Scoping Tool (Sharpe, Hernandez, & Jackson, 2020)—Connects
metrics and beneficiaries, specifically at community scales.
It is our hope that this set of three tools, as well as companion works, will improve the capacity
of biophysical scientists to work with social scientists and therefore improve that nation's
capacity to manage its ecosystems and to account for policy changes that affect them.
The conclusions of this report are only as good as the quality of the underlying data upon which
it is based. These data, presented in this report, are based on published literature and EPA
reports, websites, or other sources provided. Importantly, most of the data used in this report are
used to illustrate concepts rather than to provide quantitative conclusions.
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Front Matter
Acknowledgements
This report is the result of a team of social and natural scientists dedicated to finding the
language and ideas that can serve to cross disciplinary boundaries and develop tools for better
management and decision-making of the nation's natural resources. We thank the many
scientists and partners who have been part of this effort and shared their knowledge and
perspective on these topics, without which, we would not be able to complete this project. In
particular, we thank the members of our Steering Committee (James Boyd, Resources for the
Future; Rob Johnston, Clark University; Julie Hewitt, U.S. EPA Office of Water; Joel Corona,
U.S. EPA Office of Water; Jeff Kline, USDA Forest Service; Charles Wahle, National Oceanic
and Atmospheric Administration; and Tim Wade, U.S. EPA, Office of Research and
Development) and the outside reviewers, including Drs. Brendan Fisher (University of
Vermont), Kathleen Segerson (University of Connecticut), and V. Kerry Smith (Arizona State
University), for their time and expertise in reviewing an earlier draft of this report. However, any
errors, oversights or misunderstandings that may be embodied in this report are those of the
authors rather than of these capable reviewers. We also thank RTI International under its contract
with EPA (EP-W-15-005) for its help coordinating the Steering Committee and in the editorial
and printing process of this report. We appreciate the scientific support staff that helped guide
this report through the internal EPA review process and help ensure that it is broadly accessible.
The basis for many of the metrics selected for marine, aquatic and wetland ecosystems were
based upon the National Aquatic Resource Surveys research; without the work of the many
technicians and scientists who are a part of this team, we would lack this rich dataset from which
we were able to draw so much from. We thank our federal partners at the U.S. Department of
Agriculture's Forest Service for their expertise in forest ecology and helping identify metrics for
this ecosystem. We thank the National Oceanic and Atmospheric Administration for loaning us
one its chief scientists to serve on the Steering Committee.
This research was funded by EPA's Office of Research and Development Sustainable and
Healthy Communities Research Program, specifically through the Final Ecosystem Goods and
Services Task (SHC 2.61.2) and the Community-Based Ecosystem Goods and Services Project
(SHC 2.61).
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FEGS Metrics
Front Matter
Acronyms and Abbreviations
AIMS
Australian Institute of Marine Science
EMAP
Environmental Monitoring and Assessment Program
EPA
U.S. Environmental Protection Agency
FDA
U.S. Food and Drug Administration
FEGS
final ecosystem goods and services
FEMA
Federal Emergency Management Agency
FIA
Forest Inventory and Analysis
FWS
U.S. Fish and Wildlife Service
FWC
Fish and Wildlife Conservation GIS geographic information systems
NAPAP
National Acid Precipitation Assessment Program
NARS
National Aquatic Resource Surveys (U.S. EPA)
NESCS Plus National Ecosystem Services Classification System (pronounced Nexus Plus)
NLCD
National Land Cover Database
NO A A
National Oceanic and Atmospheric Administration
NPS
National Park Service
NTU
nephelometric turbidity units
NWCA
National Wetland Condition Assessment
PCU
platinum cobalt units
RIDEM
Rhode Island Department of Environmental Management
StreamCat
Stream Catchment dataset
USD A
U.S. Department of Agriculture
USGS
U.S. Geological Survey
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Glossary
Glossary
This glossary is adapted from a subset of the National Ecosystem Services Classification System (NESCS
Plus) Glossary, December 2020 edition (the Glossary is updated periodically). The complete and most
recent edition of the NESCS Plus glossary may be found at https://www.epa.gov/eco-research/national-
ecosvstem-services-classification-svstem-nescs-plus.
Beneficiary: The interests of individuals, groups
of people, or organizations that drive their direct
use or appreciation of an ecological end-product,
resulting in an impact (positive or negative) on
their welfare. [Note the departure from common
usage, in which a beneficiary is "a person who
receives benefits, " to focus instead on the
person's awareness and interests, relative to
final ecosystem goods and services, rather than
to the persons themselves, because a single
person with multiple interests can benefit from
ecosystems in multiple and distinct ways.]
Example: A farmer relies on their land (space
and soil) for producing crops and uses water
from a nearby stream to irrigate in the summer.
Biophysical: Pertaining to the biological,
chemical, and physical attributes of an
ecosystem or environment.
Class: A main subdivision of a classification
component, located within the top level of the
component's hierarchical structure.
Classification system: 1. An organized (and
often hierarchical) structure that, through well-
defined categories, allows one to group similar
elements together and to separate others. Pre-
determined criteria define what should be
considered similar or different, and these criteria
are driven by the specific purpose for developing
the classification system. 2. A method to group
individual elements or features into collections
similar in type, function, affiliation, behavior,
response, or ontogeny. 3. An organized structure
for identifying and organizing ecosystem
services into a coherent scheme.
Demand: As an economic concept, the amount
of an economic good or service that potential
buyers would be willing and able to purchase at
any given price. The level of demand for a good
or service is also determined by many other
factors, such as the availability and price of
substitute and complementary goods and
services and the income of the potential buyers.
Demand is not the same as economic value, but
it is a key determinant of the economic value of
a good or service. Although most ecosystem
services are not bought and sold in markets—so,
there are no market prices—the economic
demand for an ecosystem service can
nonetheless be thought of as the amount that
people would be willing and able to buy of the
service if they could acquire it through a market
transaction. Context: As an economic concept,
demand can be influenced by, but is not the
same thing as, a need, requirement, or desire.
Like economic values, the demand for economic
or ecosystem goods or services is a reflection of
individuals' preferences for them.
Ecological production functions: Usable
expressions (i.e., models) of the processes by
which ecosystems produce ecological end-
products, often including external influences on
those processes. Context: The definition and
specification of ecological production functions
are used in modeling approaches to quantify
how changes in one part of a natural system
affect changes in another. Example: The
relationship between a plant's uptake of soil
nutrients (as an input) and its rate of biomass
growth (as an output) can be represented by an
ecological production function that can include
one or more factors (e.g., soil nutrients,
precipitation, altitude).
Economic production functions: A
representation (often mathematical) of the input-
output relationship involved in the production of
an economic good or service by
commercial/industrial establishments (i.e.,
firms) or non-commercial entities (e.g.,
households or individuals). Inputs typically
include labor, physical capital (e.g., machinery),
land, other natural resources (e.g., water) and
raw materials, and other material supplies.
Outputs are the goods or services produced by
the process. The function also represents the
technology, skill material supplies. Outputs are
the goods or services produced by the process.
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Glossary
The function also represents the technology,
skill level, and methods that are embedded
within the production process.
Ecosystem attributes: A biological, physical, or
chemical characteristic or feature inherent to an
ecosystem/environment. Context: In economic
valuation studies, ecosystem attributes refer to
the set of ecological features that individually or
as a group contribute to the enjoyment of a
valued experience, such as a recreational or
aesthetic experience (for example, a day of
fishing). Example: Surface water clarity (e.g., as
measured by Secchi disk depth) is an attribute of
water in its natural environment, which can
affect recreational users' enjoyment of the
environment. In particular, it is an example of a
water quality attribute.
Existence value: The enjoyment people may
experience simply by knowing that a resource
exists even if they never expect to use that
resource directly themselves. Context: This is a
component of "nonuse value" from early
literature in environmental economics.
Final ecosystem good: Components of nature,
directly enjoyed, consumed, or used to yield
human well-being. The final ecosystem good
(i.e., ecological end-product) is a biophysical
quality or feature and needs minimal translation
for relevance to human well-being. Furthermore,
a final ecosystem good is the last step in an
ecological production function before the user
interacts with the ecosystem, either by enjoying,
consuming, or using the good, or using it as an
input in the human economy.
Final ecosystem service: The flows produced
by final ecosystem goods and directly used
appreciated or enjoyed by a human beneficiary.
Context: Final ecosystem service flows occur at
the "point of handoff' from natural systems to
human systems. They are an essential concept
for the economic valuation of ecosystem
services because the value of a final ecosystem
service embodies and includes the values of all
intermediate ecosystem services that contribute
to its existence. Example: The fauna present in
forests, such as deer, are an example of an
ecological end-product that provides final
ecosystem service flows to commercial and
recreational hunters who harvest them, as well
as to recreational wildlife viewers who enjoy
them in a non-consumptive way. The forest
ecosystem's production of the forage that
supports the deer populations is an example of
an intermediate ecosystem service that
contributes (as an input) to the final ecosystem
service.
Flow: A variable measured over an interval of
time. Flow measures are typically expressed as a
rate per unit of time—e.g., annual income
(dollars/year), daily nutrient load to surface
water (pounds/day). Context: The distinction
between "stocks" and "flows" is an essential
concept for measuring natural capital (which is a
stock concept) and the contributions of natural
capital to human well-being (which is a flow
concept).
Goods: Tangible items that are created through
a production process and that may be acquired,
used, or consumed by people for use as inputs in
another production process or to satisfy other
needs or wants. Goods can be represented and
measured as "flows," such as the amount sold
and transferred to new owners over the course of
the year, or as "stocks," such as the amount
stored in an inventory at the end of the year.
Context: Two important features that distinguish
goods from services are (1) their tangible nature
and (2) their ability to be treated as stocks in
certain contexts.
Human well-being: A multidimensional
description of the state of people's lives, which
encompasses personal relationships, strong and
inclusive communities, meeting basic human
needs, good health, financial and personal
security, access to education, adequate free time,
connectedness to the natural environment,
rewarding employment, and the ability to
achieve personal goals.
Indicator: 1. An interpretable value or category
describing trends in some measurable aspect,
often used singularly or in combination to
generate an index. 2. A sign or signal that relays
a complex message, potentially from numerous
sources, in a simplified and useful manner. 3. An
interpretable summary value that reflects the
state of, or change in, a system or point of
interest that is being evaluated. Indicators are
derived from measures or metrics that
correspond to components of well-being.
Example indicators are perceived safety,
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Glossary
lifestyle and behavior, and wealth. 4. A
summary measure that provides information on
the state of, or change in, the system that is
being measured. Information based on measured
data used to represent a particular attribute,
characteristic, or property of a system.
Intermediate ecosystem service: Attributes of
ecological structure or process that influence the
quantity and/or quality of ecosystem services but
do not themselves quantify as final ecosystem
goods and services (because they are not directly
enjoyed, consumed, or used). Context: A good
or service can be an intermediate good or service
in one situation and a final good or service in
another situation. Example: Water in a river is
an ecological end-product used in a final
ecosystem service by a kayaker, but the same
river water is an intermediate good or service to
a hiker who appreciates a deer that drinks from
that water.
Metrics and indicators: Direct or indirect
measurements of an ecological end-product or
attributes. If a metric can be consistently and
reliably related to an end-product and a
beneficiary, it can potentially serve as an
indicator of final ecosystem goods or services.
Natural capital: An extension of the economic
concept of physical capital—produced assets
such as buildings, machinery, and equipment
that are used in the production of economic
goods and services—to ecosystem goods and
services. Natural capital is the stock of natural
ecosystems that yields a flow of valuable
ecosystem goods or services into the future.
Non-use values: Human preferences for goods
or services that are not associated with or
derived from direct use or contact with them.
For instance, individuals may care about or
appreciate ecological end-products, even if they
never directly use or see them - i.e., they may
have non-use values for the existence of things
like tropical forests or pristine lakes, even if they
never visit them. Sometimes referred to as
"passive use value," non-use values are
theoretically distinct from "use values,"
although the boundary between use and non-use
values is not always definitive. Different types
of non-use value include existence value, option
value, and bequest value. Context: The
recognition that humans enjoy and benefit from
ecosystems in ways that do not involve direct
use is essential for developing a comprehensive
accounting (e.g., economic valuation) of the
total benefits provided by nature. Example:
Individuals often value the assurance that
threatened and endangered species are being
protected, even if they will never see them in the
wild, simply because they benefit from knowing
that the species exist.
Services: Actions or processes performed by
people or nature that benefit people. Services are
typically intangible and non-storable and, in
contrast to goods, which can be treated as
"stocks" and measured at a specific point in
time, services are "flows" from the service
provider to the service consumer and are
measured over a period of time (e.g., hourly
access to and use of a gym facility). Unlike a
good, which can exist (e.g., as part of an
inventory) without being transferred to a
consumer, the existence of a service requires
that it be received by a human. The wants and
needs of people are met through items (i.e.,
goods) and delivery of assistance (i.e., services).
Economic, environmental, and social services
reflect the three pillars of sustainability.
Stock: A quantity existing at a point in time,
which may have accumulated or been produced
in the past. Units of measurement are typically
expressed in levels - e.g., wealth (dollars),
physical assets (number of machines), and
nutrient concentration (milligrams per liter)—
that are present at a specific point in, or over a
period of, time. Economic goods can be
represented as a stock when they are
accumulated, stored, or stockpiled—e.g., the
stock of produce in a grocery store's inventory
at the beginning of the year. Natural capital is
also a stock concept, representing the level of
wealth (productive natural capacity through
ecosystem characteristics and processes, as well
as the ecosystem goods) embodied within
environments at a point in, or span of, time.
Context: The distinction between "stocks" and
"flows" is an essential concept for measuring
natural capital (which is a stock concept) and the
contributions of natural capital to human well-
being (which is a flow concept).
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Executive Summary
Executive Summary
A central question for any policy analysis is,
What are the effects of this policy on human
well-being? For questions that involve
ecosystem components, these analyses are aided
by the availability of a set of models and metrics
(or data) that link those models together. Often
missing are "linking metrics" and, even more
importantly, the specification of those linking
metrics. Metrics of final ecosystem goods and
services (FEGS) are important because they
represent biophysical units linking ecological
production to social production. They represent
the subset of ecosystem quantities and qualities
that are "handed off' from ecosystem scientists
and models to social system scientists and
models, as illustrated in Figure ES.
Additionally, FEGS metrics are best expressed
in units well understood by people representing
a variety of fields and levels of expertise, so that
theyare therefore useful in communicating
ecosystem services or ecosystem status to those
people. Overall, FEGS are those biophysical
metrics that best facilitate social interpretation of
ecological conditions that directly affect
people's welfare (Boyd et al., 2016).
In addition to FEGS, the linkage from ecological
production to social production includes
intermediate ecosystem goods and services
(ecosystem processes and structures that
contribute to the distribution and abundance of
FEGS and are critical to understanding,
assessing, predicting, providing early warnings,
and ultimately to managing FEGS); and
intermediate or final economic goods and
services (e.g., crop harvests or retail food sales;
these are metrics that depend on FEGS and are
components of social systems).
This report specifies FEGS metrics for use in
national and regional analysis of ecosystems and
the human well-being arising from their use,
appreciation, and enjoyment. National and
regional analysis supports strategic purposes and
provides context and a starting point for local
analysis and decision making. Strategic
purposes include allocating scarce management
attention and making decisions about whether
and how to allocate attention and effort to
specific ecosystems, stressors, or places.
Adaptive Management -4
A Ecological End
Products (FEGS)
A Policy
A Drivers
A Human
Well-being
A Ecosystem
A signifies change
Figure ES. FEGS metrics are the subset of ecosystem features that best link to human well-being.
To identify reliable metrics, we developed a
systematic, five-step process, guided by a
Steering Committee of social scientists, and
applied it to each of the ecosystems considered:
1. Ecosystem Delineation: explain how
biophysical scientists bound ecosystems for
practical purposes.
2. Beneficiary Specification: describe the
beneficiaries to be considered for each
ecosystem. These were adapted from a
classification provided by the companion
National Ecosystem Services Classification
System—NESCS Plus (Newcomer-Johnson
et al., 2020).
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Executive Summary
3. Attribute Specification: identify the
biophysical components of nature (e.g.,
water, fauna) that link the ecosystem service
and the beneficiary's interests. The attributes
were drawn from a hierarchical list provided
in NESCS Plus.
4. Metric Specification: describe the units of
the attribute and specify ideal metrics for
each FEGS.
5. Data Availability: consider the availability
of appropriately scaled data for the ideal
metric and propose alternative metrics when
extensive, spatially explicit data are not
available. These alternative metrics often
represent surrogates or proxies for the ideal
metric; it is important to note the limitations
of surrogate data.
We identified 200 metrics for 45 beneficiaries
across 7 ecosystems (coral reefs; estuaries;
lakes; rivers and streams; wetlands; agricultural
lands; forests). Virtually all types of
beneficiaries directly experienced, or perceived,
multiple FEGS metrics. This implies that any
given benefits analysis may need to consider
tracking multiple metrics. For most FEGS, data
on the ideal FEGS metrics were not available
and thus were often represented by other, often
surrogate, metrics. Even for these "available"
metrics, the spatially explicit extensive
representations useful for assessment and social
analysis are often not available. This poses a
challenge for mapping FEGS, and for economic
studies for which local abundance and scarcity is
vital. Finally, there is a great diversity in the
FEGS metrics identified and in their form.
This work should be interpreted and applied in
the context of two companion efforts: NESCS
Plus (Newcomer-Johnson et al., 2020) and the
FEGS Community Scoping Tool (Sharpe et al.,
2020) for communities to use in identifying
those beneficiaries and metrics relevant to a
given decision-making effort. These three
efforts share a common language and single
FEGS glossary. In addition, several other reports
have been completed or are well underway to
examine individual ecosystems, beneficiaries,
and associated FEGS metrics. These include
• Work on lake water clarity (Angradi,
Ringold, & Hall, 2018)
• A recreational fishery index (Hughes,
Lomnicky, Peck, & Ringold, 2021)
• A review of the state of science of metrics
for existence beneficiaries (Boyd, Johnston,
& Ringold, In prep)
• A metric of wetland plants as used by native
American peoples (Nahlik, Magee, &
Blocksom, In prep).
The purpose of this collection of efforts is to
improve the specification of critical linkages
between ecosystems and human systems. By
improving these linkages, decisions should more
efficiently and reliably incorporate information
about the benefits and costs of policy actions.
This work identified several important future
research needs:
• More primary research on metrics to best
link ecosystems to human well-being and
also effectively communicate ecosystem
status to beneficiaries. Much of the existing
work-focuses on a generic, rather than a
specific, beneficiary. This work suggests
focusing on specific beneficiaries because
they directly experience different attributes
of the environment.
• A focus not only on the biophysical features
that matter to people, but also on the
temporal and spatial units of those features.
• More effort devoted to translating metrics of
FEGS into terms clearly understandable to a
variety of people.
• More efforts to include FEGS metrics in the
design of modeling, monitoring, assessment,
and reporting programs.
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Introduction
1. Introduction
For any policy analysis, a central question that decision-makers want answered is, What are the
effects of this policy on human well-being? Addressing this question for policies that involve
ecosystem components requires diverse scientists to quantitatively link their understanding of
ecological and social systems. However, the "linking metrics" are often missing, particularly the
specification of those linking metrics. The purpose of the research presented in this report is to
improve this linkage between social and biophysical sciences and models by focusing on
components of nature referred to as final ecosystem goods and services (FEGS). This report
describes a framework for linking biophysical and social processes in decision-making and
provides a method useful to identify FEGS for use in environmental assessment, monitoring,
social analysis and planning.
Ecosystem services are ecosystem components and processes that contribute to human well-
being (Millennium Ecosystem Assessment, 2005). The general conceptualization of ecosystem
services was crystalized in 1997 as "the conditions and processes through which ecosystems and
species that make them up sustain and fulfill human life" (Daily, 1997). This definition helped to
recognize the overall connection between environmental qualities and quantities and human
well-being and is useful for conveying the general importance of ecosystems to people. But this
definition is too general to be useful in rigorous linked quantitative analysis appropriate for
decision-making. Efforts to improve and standardize the definition and categorization of
ecosystem services took a big step forward in 2001 when the United Nations' Environmental
Program began a process to systematically organize and account for global ecosystem services
(Costanza et al., 2017; Millennium Ecosystem Assessment, 2005). The result of this international
effort was the 2005 Millennium Ecosystem Assessment proposing a framework classifying
ecosystem services into four broad categories: provisioning (e.g., food), regulatory (e.g., water
purification), cultural (e.g., recreation), and supporting services (e.g., soil) (Millennium
Ecosystem Assessment, 2005).
The Millennium Ecosystem Assessment framework remains influential, but it has some
drawbacks that make it difficult to develop metrics that represent the direct contribution of
ecosystems to human well-being. Metric definition and data to quantify those metrics are a key
part in any analysis process - what is measured and how? The lack of units suitable for
ecosystem services in the context of economic analysis was underscored in theory by the U.S.
Environmental Protection Agency's (EPA's) Science Advisory Board (U.S. EPA, 2009) and in
practice by many analyses, including, for example, analyses of the benefits and costs of the U.S.
acid rain program (Chestnut & Mills, 2005). That analysis found that despite the existence of a
large ecological assessment program (NAPAP, 1992), benefit calculations were problematic due
to a "lack of units of measure to gauge changes in the quality and quantity ecosystems services."
This is perplexing, given the large ecological research and assessment programs dedicated to
examining the effects of acid rain on ecosystems in the United States and the world (NAPAP,
1992, 2010).
The existing frameworks, definitions, and boundaries of ecosystem services were too ill-formed
and inconsistent for integrated analysis (Nahlik, Kentula, Fennessy, & Landers, 2012). The lack
of a causal relationship between changes in ecosystems and ecosystem services and human well-
being make it difficult to conduct real-world environmental analysis (Boyd & Banzhaf, 2007).
To address this shortcoming in metrics of analysis and to operationalize the ecosystem services
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Introduction
concept, EPA's Sustainable and Healthy Communities research program is developing a set of
tools to enable a wide range of users to utilize the FEGS Framework. Central to these efforts are
the standardized metrics from the environment that biophysical scientists should seek to
describe, understand, and predict, and that social scientists can use in benefits analysis. FEGS
are the biophysical metrics that best facilitate social interpretation of ecological conditions that
directly affect people's welfare (Boyd et a/., 2016). These FEGS metrics are the currency of the
FEGS Framework and serve as the units that should be used in interdisciplinary analysis of
ecosystem services and in communicating the status and trends of ecosystems to people.
1.1 FEGS Framework
Photo: Recreational anglers directly interact with the environment in a myriad of ways. They directly enjoy the appeal
of the site and the fish in the water. Measures of these ecosystem attributes comprise metrics of final ecosystem
goods from these ecosystems for this beneficiary. Photo credit: EPA Fiickr.
The FEGS definition we use expands upon on earlier work that described FEGS solely as the
"biophysical components of nature that are directly enjoyed, consumed or used by people" (Boyd
& Banzhaf, 2007). The broader definition reflects interdisciplinary interactions and analysis
between economists and natural biophysical scientists. FEGS metrics are best understood as the
linchpin in a series of linked production functions grounded in well-developed ecological
production functions (Boyd & Krupnick, 2013).
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Introduction
Ecosystem Goods and Services Terminology
In the ecosystem services literature, natural scientists often use "ecosystem services" as a term
encompassing both goods and services. Technically, final ecosystem goods are the tangible biophysical
components of nature that are the direct source of final ecosystem services. Final ecosystem services are
the flows produced by final ecosystem goods and directly used appreciated or enjoyed by a human
beneficiary. For brevity, we use the term final ecosystem goods and services (FEGS) to encompass both
concepts, although most of what we describe are goods rather than services. See the glossary for
definitions of these and other terms.
Figure 1 illustrates the connection between the biophysical environment (on the left) and human
communities and well-being (on the right). Biophysical functions generate ecological end
products (final ecosystem goods) like wild fish in streams or fertile soil in fields, that people can
use directly or appreciate in diverse ways. Some of these go through intermediaries before
affecting human well-being: for example, tuna fish in the ocean (a final ecosystem good) are
caught by commercial anglers (the beneficiary) and processed through an economic system (e.g.,
fish packing plants, truckers, and grocers, and then prepared for consumption in a house) before
affecting human well-being. In contrast, an appealing view affects people directly. It is important
to note that the mere fact that a biophysical quantity or quality is subject to regulation does
notmake that quantity or quality a final ecosystem good or service. A final ecosystem good or
service must be something that a beneficiary directly experiences or perceives, not simply
something regulated or managed (e.g., dissolved oxygen or fish habitat) to manipulate the
distribution or abundance of something else (e.g., fish).
Economic
Production
Household
Production &
Consumption
Human
Wellbeing
Final
Ecosystem
Goods and
Services
, Economic Goods & Services
(Final Ecosystem Goods)
Ecosystems
Ecological
Production
Examples:
Groundwater recharge
Nutrient cycling
Crop pollination
Human Systems
Ecological
End-products
Examples:
Flora
Fauna
Water
Figure 1. The FEGS Framework and taxonomy of linkages between the
environment/ecosystems and human systems.
Source: NESCS Plus (Newcomer-Johnson et al., 2020)
As illustrated in Figure 1, ecological production provides final ecosystem goods. As these goods
are used or enjoyed by people, they provide a service that enters into human systems of
5
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Introduction
production. The specifics of this linkage depend on the beneficiary and the ecosystem. For
example, for a recreational angler on a stream, watershed and stream processes control the
distribution and abundance of fish in the water. The fish are a final ecosystem good; the angler's
use or enjoyment of the fish is a final ecosystem service that enters into household production
(center arrow between the boxes in Figure 1), which influences human well-being; in addition,
the appeal of the site is also a final ecosystem service that directly affects human well-being
(bottom arrow between boxes in Figure 1). A contrasting example is a city drinking water intake
facility on a lake: complex watershed and lake processes influence the quantity and quality of the
water available in the lake. Here, the water itself in the lake is a final ecosystem good. The
municipal intake of water is the ecosystem service that enters into economic production (top
arrow between the boxes in Figure 1) to produce an economic good, fresh water for use by
people and industries. The treated water (the economic good) is sold to households and then
enters into household production and influences human well-being.
1.2 Three Principles of the FEGS Framework
Three important principles of the FEGS Framework help to further distinguish it from other
ecosystem service definitions and make ecosystem service analysis operational (Boyd &
Krupnick, 2013):
1. It focuses on the direct beneficiary of the ecosystem service and the part of nature this
beneficiary directly uses, appreciates or enjoys;
2. It focuses on final ecosystem goods that are clearly understood by beneficiaries and are
directly enjoyed or used by people;
3. It delineates nature into discrete ecosystems with operational boundaries that can be
linked to specific ecosystem services and beneficiaries.
These three principles make the FEGS Framework attractive to EPA's Sustainable and Healthy
Communities program and other Federal, state and local environmental and natural resources
agencies that are seeking ways to use ecosystem services in planning and decision-making
(Olander et al., 2015; Posner, Getz, & Ricketts, 2016; The White House, 2014).
Beneficiaries. The first part of the FEGS Framework focuses on the users of nature or
beneficiaries - a person, industry, or organization that directly uses or interacts with nature.
Beneficiaries are defined as the interests of individuals, groups ofpeople, or organizations that
use, appreciate or enjoy nature and an ecological end product. In the examples earlier, the
commercial anglersand the municipal drinking water facility are the beneficiaries. Understanding
how beneficiaries directly interact with, use, and appreciate nature is key to the FEGS
Framework. There is no single way in which people directly perceive or understand nature;
rather people interact with and appreciate their environments in a variety of ways; seeing the
environment from a beneficiary's perspective is critical for selecting meaningful metrics.
Final Ecosystem Goods. The second part of the FEGS Framework focuses on the components
of nature recognizable and directly used appreciated or enjoyed, the final products of nature. This
approach recognizes that the complex biotic and abiotic ecological interactions of nature have
meaning to people especially as they influence the distribution, abundance, and quality of FEGS.
These often invisible or less apparent cycles of nature are called intermediate ecosystem goods
and services (Boyd et al., 2016; Lamothe & Sutherland, 2018) and are represented by the circle
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Introduction
"Ecological Production" in Figure 1. These critical processes and their relationships to FEGS are
often only understood quantitatively by subject-area or technical experts, which makes it difficult
for others to use or evaluate them consistently and meaningfully. The FEGS approach focuses on
the biophysical goods that are directly experienced or perceived and distinguishes them from
these essential intermediate processes. To be clear, intermediate ecosystem goods and services
are enormously valuable and important to measure, monitor, and manage. For example, wetland
denitrification (an intermediate ecosystem good) may minimize low estuarine oxygen levels
(another intermediate ecosystem good) leading to more abundant fish for a subsistence angler (a
final ecosystem good). In addition, when there are time lags between changes in intermediate and
final ecosystem goods, the intermediate ecosystem goods may provide an early warning for
changes in FEGS. What the FEGS concept does is to help provide an additional rationale, a
social rationale, for an understanding of why these processes is important. We do not suggest
that intermediate ecosystem goods and services have no economic value, rather their economic
value is embedded in the value of the final ecosystem good and the service it provides. This is
the same as in commercial markets where, for example, people make choices and place a value
on different loaves of bread. The flour, yeast, sugar, and water in the bread and all of labor and
systems to create the bread are part of the bread production system. Those components have
value that is reflected in the retail price of the bread and are managed to yield the distribution,
abundance, and quality of bread that people can purchase in the marketplace.
Ecosystems. The third part of the FEGS Framework is to organize and partition the natural
world into distinct ecosystems. Defining and delineating these boundaries make accounting more
tractable, complete, and consistent. Some ecological goods are composites of multiple
ecosystems. For example, building on the recreational angler noted above, the appeal of a
favorite fishing hole may depend on many ecosystems, including not only ocean, but also the
adjacent riparian area and the forests, wetlands, urban areas, and agricultural systems in the
viewshed (or really in the "sensory-shed", since sense of place includes the perception of all the
body's senses). This last point of is an important part of the ecosystem specific delineation of the
FEGS concept: a FEGS is counted and quantified where it is enjoyed or used by the beneficiary,
not necessarily where it is produced or created. So for the recreational angler who catches
salmon in a river, the ocean portion of the salmon's life is where intermediate production takes
place that produces the angler's recreation where that recreation occurs.
The delineation of beneficiaries, ecosystems, and focusing on final ecological goods as the units
of analysis helps to resolve the inconsistencies and double counting that may take place in other
ecosystem service frameworks. The selection of the FEGS metric - the linchpin between
biophysical and social scientists - is the key for successful integration between these different
disciplines. Together, these three principles of the FEGS Framework can be used to improve
analysis, decision-making, and communication when considering changes to policies that may
affect the environment.
1.3 Using the FEGS Framework in Decision-making
EPA's Sustainable and Healthy Communities program is exploring the FEGS Framework as a
means to more fully and consistently represent the environment and the values people place on it
in decision-making. The general goal is to provide comprehensive information on all the ways in
which ecosystems support people so that they can all be properly accounted for in decision-
making. A well-chosen metric can improve the valuation studies economists often conduct or
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Introduction
transfer from other studies when considering cost/benefits of policy changes. The FEGS
Framework faciliates social analysis by economists and increases understanding by the public.
This added clarity and translatability leads to better analysis, and communication (Boyd et al.,
2016).
Figure 2 describes the general decision-making context in which policy analysts operate.
Changes in policy impact a driver on the environment that can lead to changes in ecosystem
patterns and processes. A subset of those changes are changes in FEGS, while many others are
intermediate ecosystem goods or services. Those changes in FEGS impact human well-being.
Finally, adaptive management allows for adjustments to policy changes if they do not have the
desired or expected effect.
A Ecological End
Products (FEGS)
Adaptive Management -4
A Policy
A Drivers
A Human
Well-being
A Ecosystem
A signifies change
Figure 2. Conceptual model of the central role of FEGS in decision-making analysis by EPA's
Sustainable and Healthy Communities program or other agencies.
In this model, a change in policy may impact a driver that effects the environment. FEGS capture the biophysical
processes but can be described and communicated better, which makes them a better choice to include in social
analysis. This figure is similar to Figure 1 in terms of the linkages between FEGS and human well-being but includes
the policy impacts to drivers and the ecosystem.
Using the conceptual model shown in Figure 2, consider a change in acid rain policy. Changes in
the Clean Air Act (policy change) resulted in changes in facility emissions (the driver) that—via
intermediate processes in air, watershed, and aquatic ecosystems—yielded changes in
biophysical outcomes, including chemical water concentrations and fish abundance (ecosystem
changes). Some of these outcomes will be better at facilitating accurate and meaningful social
well-being analysis than others (Boyd et al., 2016). The ones that beneficiaries experience,
perceive, and understand directly are what will be useful FEGS metrics. Changes in these FEGS
lead to changes in human well-being. The absence of models predicting FEGS and information
on FEGS has been a primary obstacle to estimating the ecological benefits of acid rain policy
(Chestnut, Mills, & Cohan, 2006).
The challenge for a national agency like the EPA is how to systematically and consistently
consider the full range of benefits at national and regional scales. Our national and regional scale
charge has two corollaries that relate to the spatial scale or specificity of the suggested metric:
• We seek to be generally correct and consistent; this is in contrast to a local reporting
requirement in which one must be specifically correct but consistency is of little matter.
For example, at the national or regional scale, it might be sufficient to know that some
percentage of forests are deciduous. At the local level one might want to know if a
specific forest contains Ouercus buckleyi Nixon & Dorr, live oak.
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Introduction
• National and regional data require time to assemble and are usually published several
years after the period of time they cover; for example, the U.S. Geological Survey
(USGS) Estimated Use of Water in the United States in 2005 was published in 2009
(Kenny et al., 2009); the EPA National Lakes Assessment 2012 was published in 2016
(U.S. EPA, 2016a); and the National Oceanic and Atmospheric Administration (NOAA)
Fisheries of the United States, 2018 was published in 2020 (NOAA, 2020b), Therefore,
such national or regional data and the reports based on them are not useful for immediate
decisions, such as deciding where to go fishing this weekend, but rather for longer range
or strategic purposes, such as providing insights as to whether the nation should allocate
resources to one problem, issue, or place rather than another. This is consistent with our
purpose: to develop metrics that represent how human well-being and communities
connect with and interact with nature.
As such, our metrics and methods, and the reports based on them, are expected to be more useful
for agents acting on behalf of the beneficiaries than for the beneficiaries directly as they make
decisions on a day-to-day basis. Having said this, the process and the results that our ecosystem
experts went through to define beneficiaries and the metrics that matter to them should be a
useful starting point for those focusing on community scale actions.
This report is one part of a suite of three FEGS-related tools developed by EPA that use the
FEGS Framework at both national and community scales (see Figure 3):
1. The National Ecosystem Services Classification System (NESCS Plus; Newcomer-
Johnson et al., 2020)—A classification system for ecosystem services that provides a
consistent architecture and taxonomy, as well as the rationale for and a consistent
delineation of the three dimensions of our shared Framework: beneficiaries,
environmental classes, and attributes. It also contains tables of the relationships between
dimensions.
2. Metrics for National and Regional Assessment of Aquatic, Marine, and Terrestrial
Final Ecosystem Goods and Services (FEGS Metrics; this report)—The metrics and
general process for metric selection, which are key elements of the related tools.
Information and methods in this report also provide a starting point for community
decisions on which metrics to use in considering their decision.
3. FEGS Community Scoping Tool (Sharpe et al., 2020)—Connects beneficiaries and
metrics, specifically at community scales. This allows community-level decision-makers
to identify the set of prioritized FEGS specific to a community.
Together, these three pieces provide a comprehensive approach to using the FEGS Framework at
national and community scales.
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Introduction
FEGS Community
Scoping Tool
Prioritizes beneficiaries and
attributes (from NESCS Plus)
to facilitate metric selection
FEGS Metrics Report
Provides detailed methods for
linking beneficiaries with
ecosystem-specific metrics
NESCS Plus
Provides FEGS framework organized
by ecosystem, beneficiary, and
environmental attribute
Figure 3. Relationship between NESCS Plus, FEGS Community Scoping Tool,
and FEGS Metrics (this report).
Together these tools allow one to identify ecosystem service beneficiaries, prioritize community values,
and select appropriate metrics for evaluation that reflect community interests.
1.4 FEGS Metric Selection Process
Specification of FEGS metrics is important because these are the specific tangible biophysical
features or qualities that are the objects of management, communication, and social analysis.
Biophysical scientists use an enormous number of metrics to describe, understand, predict, and
assess ecosystems. Many of these metrics, however, may not be directly meaningful to people
absent significant technical translation. FEGS metrics, in contrast, represent ecosystems in units
that people, including policy makers and agencies that represent beneficiaries' interests, can
readily understand. These FEGS metrics can be useful in decision-making and they should be
considered in social analyses, including cost-benefit and trade-off analysis. The result of the
inclusion of FEGS metrics in analysis will be improvements in the connection between
biophysical and social processes, which will in turn provide a more complete and accurate
assessment of policy changes.
We are not the first to address this issue; in fact, social scientists have a well-developed set of
qualitative methods to address such issues (e.g., Champ, 2017; Desvousges & Smith, 1988;
Morgan & Krueger, 1997; Weber & Ringold, 2012), and those methods have been applied for
this purpose (e.g., Avolio et al., 2015; Daniel, 2001; Morton, Adamowicz, Boxall, Phillips, &
White, 1993; Ribe, 1989; Schiller et al., 2001; Weber & Ringold, 2015, 2019). Efforts such as
these are important for linked analyses and for our work. They inform our starting points and can
be used to evaluate our metrics. We hope to stimulate more of this intensive work as a result of
our efforts. However, in some instances, these applications have focused on the general ways in
which people benefit from ecosystems without partitioning by specific beneficiaries that use and
interact with nature in different ways. If beneficiaries differ in what they directly perceive in
ecosystems then this practice would lead to misspecification of FEGs metrics. Because there is
heterogeneity in beneficiary preferences, we believe that tracking preferences by beneficiary is
important (Ringold, Boyd, Landers, & Weber, 2009; Ringold, Nahlik, Boyd, & Bernard, 2011).
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Introduction
In some cases, the needs of the beneficiary are well known; For example, it is well documented
that salt reduces the value of irrigation water (Shannon & Grieve, 1998) or that the presence of
alien mollusks can degrade water intake systems for domestic purposes or for thermoelectric
cooling along a river or lake (Isom, Bowman, Johnson, & Rodgers, 1986; Nakano & Strayer,
2014). However, even in these cases, where the needs of the beneficiaries are well known, the
full set of metrics is less clear: do pathogens matter for irrigators of crops not directly
consumed? How does the potential for scaling and corrosion from source water affect
thermoelectric cooling? For other beneficiaries, needs may be less well known, or may differ
considerably depending on what different beneficiaries directly perceive in ecosystems. In those
cases, there are not consistent methods to propose or select FEGS metrics on a comprehensive
basis. Thus, at the outset of our work, there was no single existing method to identify the set of
metrics for a wide variety of diverse beneficiaries as they interact directly with ecosystems.
For use as FEGS, biophysical metrics must not only meet the needs and requirements of
ecosystem analysis, but also the needs of social scientists. In this vein, Schultz and his colleagues
(Schultz, Johnston, Segerson, & Besedin, 2012) note that biophysical metrics must meet four
criteria: measurability, interpretability, applicability, and comprehensiveness. EPA, elaborating
especially on the biophysical requirements in more detail developed a list of 15 guidelines to
evaluate the suitability of biophysical metrics in a consistent manner for ecological sampling and
assessment (Jackson, Kurtz, & Fisher, 2000). One of the 15 guidelines includes consideration of
linking the metric to management action. In contrast to very specific detail provided on how to
think about the other guidelines, the guidance provided for relevance to management action is
vague. Further, since management action often appropriately focuses on intermediate goods and
services as a means to manage FEGS (e.g., Water Quality Standards; U.S. EPA, 2003b),
guidance to focus on management actions is vital, but not sufficient to support linked analyses or
even to communicate effectively with beneficiaries. Since FEGS serve as the interface between
biophysical and social systems, they represent a way to provide support for these guidelines and
even to extend them.
Consistent with this requirement for joint validity, we designed a process in which biophysical
scientists and social scientists could collaborate and develop a set of linking metrics. Our process
grew out of a series of interactive workshops with the goal of identifying a shared conceptual
foundation to uniformly and consistently define metrics of FEGS. Beginning in 2008, the EPA
FEGS working group organized interdisciplinary workshops with social and natural scientists
and other experts to discuss ecosystems of estuaries, streams and rivers, and wetlands (Ringold et
al., 2009; Ringold, Nahlik, Boyd, & Bernard, 2008). These workshops led to the development of
the boundaries, organizing concepts, and principles of the FEGS concept and its potential
relevance to the EPA in its regulatory and scientific function. Building on the discussion and
success of these initial workshops, principles were refined (Landers & Nahlik, 2013; Ringold et
al., 2009), additional workshops were organized, and additional experts were invited to
participate in the process. This process for consistent metric selection for ecosystem-specific
beneficiaries is described in detail in the Methods section.
1.5 Report Objectives
The objective of this report is to describe not only FEGS metrics generated for a diverse group of
beneficiaries interacting with nature in seven ecosystems across the country, but also the key
steps of the dialog and process of metric generation that structured our process. This report can
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Introduction
be used by other environmental professionals seeking metrics for ecosystem service analysis.
The results of this work are intended to support national and regional decision-makers who
operate on behalf of the beneficiaries' interests. The methods and narrative laid out in this report
provide the rationale and means to select meaningful metrics that can represent nature's
contributions to human well-being.
2. Methods
We designed an approach to use expert knowledge and a structured process to identify metrics of
FEGS for selected ecosystems. We utilized expert knowledge by assembling a team with two
major sets of expertise: (1) biophysical scientists familiar with the principles of metric
specification (Dale & Beyeler, 2001; Jackson et al., 2000; McKenzie, Hyatt, & McDonald, 1992)
and with specific ecosystems; and (2) social scientists familiar with methods of valuation of both
use and non-use existence values. The combined team met three times for workshops in 2016,
2017, and 2018 (Hall, 2017, 2018; Phifer, 2019) to refine their understanding of FEGS, develop
a structured process, and propose a set of metrics as an illustration of the methods. An important
part of the design of our work is to set the expectation that the ecosystem experts would serve as
"champions" for FEGS perspectives in their future work.
Because so much of this work is very ecosystem-specific, we began with the classification of
ecosystems from NESCS Plus (or, in the terminology of that system, "Environment Classes"), as
shown in Table 1. At the highest level, ecosystems are categorized as aquatic or terrestrial.
Conceptually, this delineation is consistent with how state and Federal agencies that interact with
the environment are organized and how these agencies design monitoring programs. Thus, that
dichotomy aligns with environmental sampling programs that already exist: the U.S. Forest
Service has an extensive sampling program for forest resources, NOAA tracks coral reef
resilience and the EPA and USGS track aquatic ecosystems. The ecosystem-based delineation
also corresponds to satellite-based ecosystem classification systems that form the basis of
environmental or natural resources spatial analysis, including the USGS National Land Cover
Database. These classes are then further subdivided into subclasses and still more detailed
subclasses.
We assembled seven ecosystem teams from the full set of ecosystems (or environment classes)
listed in NESCS Plus. These are denoted by an asterisk in Table 1. Three are from the Subclass 1
category: forests, wetlands, and agroecosystems; three more are from the Subclass 2 category:
rivers and streams, lakes, and estuaries; and one, coral reefs, is a subset of the Subclass 2
category near coastal marine. Agroecosystem analysis also added subdivisions to the listed
environmental classes in some of their analyses. These specifications are provided in the
extensive tables in the Appendix under the headings Environmental Class, Environmental Sub-
Class, and Ecosystem.
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Methods
Table 1. Ecosystem Classification Used by NESCS Plus
Environment
Class
Environment
Subclass 1
Environment Subclass II
Rivers and Streams*
Open Water
Lakes and Ponds*
Aquatic
Near Coastal Marine*/Estuarine*
Open Oceans and Seas
Wetlands*
Woody Wetlands
Emergent Herbaceous Wetlands
Deciduous Forest
Forests*
Evergreen Forest
Mixed Forest
Agroecosystems*
Pasture/Hay
Cultivated Crops
Grasslands
Grassland/Herbaceous
Scrubland/Shrubland
Shrub/Scrub
Lichens
Terrestrial
Tundra
Moss
Dwarf Scrub
Sedge/Herbaceous
Ice and snow
Perennial Ice/Snow
Developed Open Space
Urban/suburban
Developed Low Intensity
Developed Medium Intensity
Developed High Intensity
Barren/rock and sand
Barren Land (Rock/Sand/Clay)
* Denotes the seven ecosystems (referred to as environment classes in the NESCS Plus
system) for which metrics were developed in this report. Note that coral reefs are not
explicitly listed, but are a subset of the Near Coastal Marine subclass II.
The structured process for proposing metrics asked each ecosystem team to work through the
following five steps:
Step 1. Ecosystem Delineation: explain how biophysical scientists bound ecosystems for
practical purposes.
Step 2. Beneficiary Specification: describe the beneficiaries to be considered for each
ecosystem.
Step 3. Attribute Specification: identify the biophysical components of nature that links
with the ecosystem service and beneficiary's interests.
Step 4. Metric Specification: describer the units of the attribute and discuss the difference
between ideal and available metrics.
Step 5. Data Availability: consider the availability of appropriately scaled data for the
proposed metric.
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In addition to these steps, the teams also presented visualizations of the candidate metrics and
made some preliminary steps to evaluate their candidate metrics. The steps are described in more
detail in the remainder of this section. Section 3, Results, is organized by ecosystem and
includes the names of the team members, the ecosystem delineation, the beneficiaries, and the
FEGS metrics for each ecosystem-specific beneficiary organized in comprehensive tables. These
beneficiary-specific tables report the metrics for the ecosystem services provided by each
ecosystem considered. These tables are a subset of the columns in the more detailed tables
provided in the Appendix (which is also provided as a sortable and filterable Excel file). The
Appendix tables include a row with the corresponding column designation in the tables provided
in Section 3 or the comment "not shown".
Because of the ecosystem delineation and the (general) reliance on ecological and environmental
sampling programs for metrics, different teams of ecologists and other biophysical experts with
knowledge of each system selected the metrics and beneficiaries for each ecosystem. As a
consequence, there are differences in the metrics selected or in the specific units, reflecting the
different availability of datasets.
2.1 Step 1: Ecosystem Delineation
For each ecosystem considered, the boundaries and scope must be clearly defined and delineated
so that analysts know what features to include or exclude and so that these boundary decisions
are clear for users of the results. In this step, the teams defined issues such as the landward
boundary of streams and lakes, the upstream boundary of estuaries, and the definition of forest
land. Ecosystem teams typically chose boundaries used by ecological sampling programs for
regional or national scales (e.g., the U.S. Department of Agriculture [USD A] Forest Service
Forest Inventory and Analysis [FIA] program; EPA's National Aquatic Resources Surveys
[NARS]). The clear boundaries of the FEGS Framework implementation help to make it
operational and useful for complete ecosystem accounting. At the same time, it is important to
recognize that some beneficiaries may not recognize these ecological boundaries. This is
particularly the case when site appeal is involved. Here, beneficiaries integrate their experience
across all evident ecosystems in making judgments on site appeal, and practical, beneficiary-
centered operational judgements must sometimes be made that may breach the otherwise useful
boundary definitions.
2.2 Step 2: Beneficiary Specification
Here, our teams began with the classification of beneficiaries from NESCS Plus (Newcomer-
Johnson et al., 2020), shown in Table 2. This beneficiary-first approach allows ecosystem
service researchers to first identify what is important and appreciated at a human scale and then
develop appropriate biophysical metrics.
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Table 2. Beneficiary Classifications from NESCS Plus
These are used to systematically identify beneficiaries who directly use, interact with, or directly perceive nature.
Ecosystem teams used this classification to identify ecosystem-specific beneficiaries.
Beneficiary Class and Description
Beneficiary Subclasses
Agricultural
Beneficiaries who use the environment for agricultural or forest
production activities
Livestock Grazers; Agricultural Processors;
Aquaculturists; Farmers; Foresters; Other
Agricultural Beneficiaries
Commercial/Industrial
Beneficiaries who directly use the environment for industrial or
commercial production activities not included in the other
categories
Food Extractors; Timber, Fiber, and Ornamental
Extractors; Industrial Processors; Private Energy
Generators; Pharmaceutical and Food
Supplement Suppliers; Fur/Hide Trappers and
Hunters; Private Drinking Water Plant Operators;
Commercial/Industrial Property Owners
Government, Municipal, Residential
Governmental, military, and residential beneficiaries who make
direct use of the environment in ways not included in the other
categories
Municipal Drinking Water Plant Operators;
Residential Property Owners; Public Sector
Property Owners; Military/Coast Guard; Public
Energy Generators
Transportation
Military and commercial beneficiaries who use the environment
as a medium to transport goods or people
Transporters of Goods; Transporters of People
Subsistence
Beneficiaries who use the environment to support subsistence
activities
Water Subsisters; Food and Medical Subsisters;
Timber, Fiber, and Fur/Hide Subsisters; Building
Material Subsisters; Other Subsisters
Recreational
Beneficiaries who use the environment to support recreational
activities
Experiencers and Viewers; Food Pickers and
Gatherers; Hunters; Anglers; Waders, Swimmers,
and Divers; Boaters; Other Recreational
Inspirational
Beneficiaries who use or appreciate the environment as a
source of inspiration
Spiritual and Ceremonial Participants and
Participants of Celebration; Artists; Other
Inspirational
Learning
Beneficiaries who directly rely on the environment for
educational or scientific research activities
Educators and Students; Researchers
Non-Use
Individuals who benefit from the environment in ways that do
not require or are not associated with direct use of or contact
with a final ecosystem good
People Who Care (Existence); People Who Care
(Option /Bequest)
Humanity
Everyone, regardless of whether they actively recognize or
appreciate the final ecosystem good, because they are
available to everyone and used by everyone to live (e.g., air for
breathing)
All Humans
The teams then chose specific beneficiaries from within the NESCS Plus subclasses; these are
frequently more specific than the lists of beneficiaries available elsewhere (Landers & Nahlik,
2013; Newcomer-Johnson et al., 2020; Ringold et al., 2009; Ringold, Boyd, Landers, & Weber,
2013; Ringold et al., 2011). This additional level of specificity was required by ecosystem teams
to specify attributes and metrics of direct relevance. For example, the NESCS Plus identifies
anglers as a subclass of recreational beneficiaries. Within this subclass, the ecosystem teams for
this effort identified catch-and-eat anglers and catch-and-release anglers. The distinction between
anglers who consume their catch and those who release their catch affects the FEGS metrics that
might be included: the presence of contaminants in the fish flesh in concentrations relevant to
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human health (e.g., mercury concentration) are highly relevant to a catch-and-eat angler, but not
to a catch-and-release angler.
The ecosystem teams purposefully selected a set of wide variety of beneficiaries. The goal was to
select a diverse set of beneficiaries to evaluate the utility of this approach across a broad
spectrum. The beneficiaries include both direct consumptive users (e.g., waterfowl hunter), non-
consumptive users (e.g., lake-front homeowners who enjoy the view), and non-use beneficiaries
(e.g., those holding existence values) to include all types of beneficiaries necessary for a total
economic benefits analysis. The beneficiaries selected were not assumed to be the most
important—we have no way of establishing that, especially in the absence of a policy context—
rather, beneficiaries were selected to demonstrate the potential of the FEGS Framework for
EPA's Sustainable and Healthy Communities program and other organizations.
A difficult, but critically important, beneficiary subclass is the existence beneficiary, who
appreciates the existence of a system with the expectation that they will never directly use or
experience the system. In some circumstances, the benefits of ecosystem change attributable to
this beneficiary can be quite large (Johnston, 2018). In addition, specification of biophysical
units for this beneficiary are among the most difficult to define (Turner, Georgiou, & Fisher,
2008). Therefore, all ecosystem teams (except for the agricultural systems team) were asked to
propose biophysical metrics for this beneficiary that represented a measure of the ecosystem's
overall health or integrity. Their choices here were a subject of much discussion between the
ecosystem experts and the Steering Committee. Those discussions have prompted further
analysis, including a thorough state-of-the-science review (Boyd et al., In prep)
The beneficiaries selected for each ecosystem are shown in Table 3.
2.3 Step 3: Attribute Specification
Having specified an ecosystem and a beneficiary, the next step to metric identification was to ask
as a heuristic question, what matters to this beneficiary? The answers led ecosystem experts to
specify the general features of the environment that matter directly to each beneficiary. We
termed these general features "attributes" of nature. This general specification then allowed the
ecosystem teams to think more specifically about an appropriate metric. For example, swimmers
in a lake care about water conditions—Is it safe to swim in? Is it clear or murky? What is the
temperature in summer? These general attributes of the lake water (safety, clarity, temperature)
matter to the specific beneficiary (swimmers) and so are important to measure and describe.
To simplify this step and make consistent attribute selections, we developed what eventually
became a standardized, hierarchical set of attribute terms, shown in Table 4. Attribute categories
are structural elements of NESCS Plus (2020) and are described in more detail there and in our
shared glossary. These categories cover the basic components of all ecosystem (e.g., water, flora,
fauna). The attribute subcategories further subdivide the attribute categories. When identifying
metrics for their beneficiaries, ecosystem teams first identified the relevant attribute categories
and subcategories. Within these prescribed categories and subcategories, ecosystem teams
selected a more specific attribute; these were not prescribed and reflected the particulars of the
specific beneficiary.
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Table 3. Ecosystem-specific Beneficiaries Considered for this Report by NESCS Plus Beneficiary Class and Subclass
Key: — no specific beneficiary chosen. SUPs = stand-up paddle boarders.
Beneficiary
Class
Beneficiary
Subclass
Coral Reefs
Estuaries
Lakes
Rivers
Wetlands
Agriculture
Forests
Agricultural
Aquaculturists
Coral nurseries
Shellfish growers
—
—
—
—
—
Farmers
—
—
—
—
Cranberry farmers
Crop farmers
—
Foresters
—
—
—
—
—
—
Timberland owners/
timber growers
Commercial/
Industrial
Commercial Anglers
—
Commercial
anglers
—
—
—
—
—
Pharmaceutical and
Food Supplement
Suppliers
Extractors /
bio-prospectors
Extractors /
bio-prospectors
Private Energy
Generators
—
—
—
Thermoelectric
cooling
—
—
—
Timber, Fiber, and
Ornamental Extractors
Reef ornamental
extractors
—
—
—
—
—
Timber extractors
Government,
Municipal,
Residential
Residential Property
Owners
Coastal property
owners
Coastal property
owners
Lakeshore
property owners
River front
property owner
Farmland property
owners
Homeowner with
some trees living
next to forested
area
Transporters
—
Barge or ferry
—
—
—
—
—
Learning
Agricultural Landscape
—
—
—
—
—
Educators/
researchers
—
Non-use
People Who Care
Existence values
Existence values
Existence values
Existence values
Existence values
—
Existence values
Recreational
Anglers (recreational)
Catch & release,
catch & eat
Catch & release,
catch & eat
Catch & release,
catch & eat
Catch & release,
catch & eat
—
—
—
Boaters
Kayakers, SUPs,
& boaters
Kayakers, SUPs,
and boaters
Power boaters
—
Kayakers, SUPs,
and boaters
—
—
Food Pickers &
Gatherers
—
—
—
—
—
—
Recreational
huckleberry pickers
Hunters
—
—
—
—
Waterfowl hunters
Deer, waterfowl, &
small game hunters
—
Swimmers, Waders,
Divers
Scuba divers and
snorkelers
—
Swimmers
Swimmers
—
—
—
Subsistence
Food and Medicinal
Subsisters
Anglers
(subsistence)
Anglers
(subsistence)
Wild rice
harvesters
Anglers
(subsistence)
Anglers
(subsistence)
Native American
medicine
subsisters
Elk hunters
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Table 4. Prescribed Attribute Categorization Used to Describe the Physical Components of the
FEGS Metric
Attribute Category
Attribute Subcategory
Atmosphere
Air quality
Wind strength/speed
Precipitation
Sunlight
Temperature
Soil/Substrate
Soil quantity
Soil quality
Substrate quality
Substrate quantity
Water
Water quality
Water quantity
Water movement
Fauna
Fauna community
Edible fauna
Medicinal fauna
Keystone fauna
Charismatic fauna
Rare fauna
Pollinating fauna
Pest predator/depredator fauna
Commercially important fauna
Spiritually/culturally important fauna
Flora
Flora community
Edible flora
Medicinal flora
Keystone flora
Charismatic flora
Rare flora
Commercially important flora
Spiritually/culturally important flora
Fungi
Fungal community
Edible fungi
Medicinal fungi
Rare fungi
Commercially important fungi
Spiritually/culturally important fungi
Other Natural Components
Fuel quality/quantity
Fiber material quantity/quality
Mineral/chemical quantity/quality
Presence of other natural materials for artistic use or consumption
(e.g., shells, acorns, honey)
Composite (and Extreme Events)
Site Appeal
Ecological condition
Open Space
Extreme Events
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Attribute categories, as structural elements of the classification system, are mutually exclusive.
For example, either something is fauna or it is not—it cannot be both fauna and water. There
were two exceptions to this, the Extreme Events category and the Composite category. Extreme
Event attributes encompass such aspects of the environment that serve to increase or decrease the
likelihood that beneficiaries will experience extreme events such as fire and flooding. Composite
attributes encompass aspects of the environment that are the result of multiple individual
attributes working together, such as the aesthetics of a landscape. Both of these categories are the
result of multiple attributes working together across those mutually exclusive categories. A
beneficiary, however, experiences them in their totality, which is why they are included as such
in the attribute categorization.
Attribute subcategories reflect how a beneficiary interacts with the attribute category (related to
the use itself). These are typically not mutually exclusive. For example, Pacific salmon are
edible, commercially important, and spiritually important to Northwest tribes, and thus could be
included in three subcategories of fauna: edible fauna, commercially important fauna, and
spiritually/culturally important fauna. Different beneficiaries will be concerned with one aspect
over another.
In addition to providing consistency across the ecosystem teams' processes and metric tables, the
attributes also provide a connection point across FEGS tools. The attribute categories are used in
NESCS Plus and both the categories and subcategories are used in the FEGS Community
Scoping Tool.
The attributes selected for each beneficiary are identified for each ecosystem and are listed as in
the tables in Section 3, Results, and the Appendix.
2.4 Step 4: Metric Specification
After identifying the general biophysical attributes that matter to each beneficiary, ecosystem
experts then focused on identifying metrics that would be of direct relevance to a beneficiary.
Metrics are more specific than general attributes; they are the biophysical parts of nature that
natural scientists model, measure, and monitor directly, often as part of environmental quality
programs. In contrast to the previous steps, where ecosystem specialists had a defined list of
beneficiaries and attributes, there is no comparable compact list of metrics to choose from.
Rather, ecosystem specialists often have a long list of potential metrics. For example, consider a
purely ecological metric: in the process of developing a multimetric index of biotic integrity for
macroinvertebrates, ecologists start with hundreds of candidate metrics just for one biotic
assemblage (Stoddard et al., 2008). In aquatic ecosystems, this list of hundreds of metrics is
complemented by lists that are equally long for landscape metrics, riparian structure, physical
habitat, chemistry, and other assemblages. The challenge for ecosystem specialists is then to
select the subset of metrics that are directly meaningful to beneficiaries.
For a few beneficiaries, ecosystem specialists identified a single metric that matters; for most
beneficiaries, several metrics were suggested. We left the decision about the number of metrics
to use for each beneficiary up to the best professional judgement of each ecosystem expert,
though their selection was subject to feedback from their peers and oversight from the Steering
Committee. The notion that people might directly experience or perceive multiple metrics of an
ecosystem is consistent with market consumer decisions. From the simplest purchase (e.g., socks
or bandanas) to the most complex (a house or a car), consumers make decisions on the basis of
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multiple attributes of the good or service that directly matter to them. Most purchases involve
weighing multiple metrics and selecting one consumer item that matches the consumer interests;
similarly, many ecosystem services are best represented by multiple metrics.
When selecting metrics, we made a distinction between ideal metrics and currently available
metrics. Ideal metrics are ones most consistent with FEGS concepts. They reside in the right
location in the set of linked production functions for the relevant beneficiary; they can be
measured in continuous form; and in concept, they meet the other requirement for metrics (e.g.,
Jackson et al., 2000). We found that data on the ideal metrics were often not available. To
provide guidance to users, ecosystem specialists also identified available metrics—metrics
related to the ideal metric but for which data were available, especially at national and regional
scales. These available metrics are shown in the Results section of this report. We also list the
ideal metrics in the Appendix.
Available data are often surrogates for the ideal metrics. An extensive literature summarizes
issues with the use of surrogacy (e.g., Hunter et al., 2016). In our Framework, surrogates are
often either intermediate ecosystem goods (i.e., to the left of final ecosystem goods in the
lefthand box in Figure 1) or economic goods or other social measures (i.e., the righthand box in
Figure 1). Two examples illustrate the limitations, and therefore our reservations, regarding the
use of surrogates for FEGs:
• Allison, Lubchenco, & Carr (1998) showed that habitat, in the form of marine reserves, is
necessary but insufficient as a tool to manage valued ecosystem components, such as
some commercial fish populations. Similarly, Lindenmayer and Likens (2011) suggest
that management of indicators or flagship species may be necessary but are insufficient to
maintain biodiversity and that "in some circumstances, the alternative of direct
measurement of particular entities of environmental or conservation interest will be the
best option".
• Maunder et al. (2006) point out the limitations of socio-economic measures, such as fish
landings, or catch per unit effort as a surrogate for fish abundance. They note that catch
as a function of a "unit of effort" varies over time and space, as well as with technology
and skill, environmental factors, target organism size and taxon, and management
practices, and therefore, the direct use of catch per unit effort estimates of fish abundance
may lead to erroneous management decisions.
While such surrogate measures may be the best available data set, they must be used with
recognition that the management goal is not the surrogate, but something else. It should also
prompt the collection of data that are a more reliable representation of the FEGS.
Another issue ecosystem teams had to address is how much biophysical or social translation
should be embedded in metrics. A metric with more translation may be more meaningful but
may raise other issues. For example, water clarity is one factor that matters directly to
homeowners residing by the water: the home is more valuable when the water is clearer (Gibbs,
Halstead, Boyle, & Huang, 2002; Moore, Doubek, Xu, & Cardinale, 2020; Papenfus, 2019; Poor,
Boyle, Taylor, & Bouchard, 2001). This attribute—water clarity—can be measured in many
ways and reflects multiple properties of water (e.g., Hutchinson, 1957; West, Nolan, & Scott,
2015). Brezonik, for example showed that measurements of organic color and turbidity were
excellent predictors of Secchi disk depth (Brezonik, 1978). The question for us is, how do we
represent water clarity? Table 5 summarizes the issues for this attribute and the tradeoffs in
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specifying this quality at any of three levels. All three of these metrics are metrics of water
clarity. The metric that is most easily understood—the good, fair, poor categorization—might
communicate most effectively, but economists on our Steering Committee told us that they
prefer to use a continuous measure in their valuation studies. Similarly, policy makers have told
us that they would want to analyze the benefits of changes that might occur within a category,
e.g., a 10% improvement in water quality, even if the changes resulted in no change in the
category of the water quality classification. We can provide a technical translation to quantify the
Secchi disk depth from more basic measures. However, the categorization of water clarity into
good, fair, and poor categories requires a social translation. Thus, our goal was to identify
metrics that would be most directly relevant to beneficiaries, but with a minimum of social
translation embedded. Fortunately, even when a metric of the right attribute may not be directly
understandable by a beneficiary, it may still be useful in policy analysis if it is a description of
the right attribute.
Table 5. Possible Metrics of Water Clarity
Metric(s)
Measurement
Units
Advantages
Disadvantages
Turbidity or
Color
FTU or NTU
Pt or pcu
Can be predicted directly by
quantitative models and thus can
be included in linked quantitative
management models
Requires the most technical explanation
to make relevance clear to many
beneficiaries
Secchi disk
depth
Meters or Feet
Can be predicted directly by
quantitative models and thus can
be included in linked quantitative
management models
Requires minimal technical explanation to
make relevance clear to many
beneficiaries
Good, Fair,
or Poor
Categories
Categories communicate
effectively. When category status
can be predicted from biophysical
measures, can be used in linked
management models
Requires the least technical explanation
to make relevance clear to many
beneficiaries, but classification must
reflect beneficiary values. Translation
from biophysical measures to categories
may vary overtime and space. Doesn't
allow for evaluation of policies when
changes may occur within a category.
This example is for water clarity, but the same advantages and disadvantages exist for other sets
of metrics, for example, pathogen concentrations vs. compliance with regulatory standards for
pathogens; lists of macroinvertebrate taxa vs. Multimetric Index of Biotic Integrity (Stoddard et
al., 2008) or ratios of observed taxa to expected taxa (Hawkins, Norris, Hogue, & Feminella,
2000; Moss, Furse, Wright, & Armitage, 1987); or good, fair, or poor categorization relative to
some ecological baseline.
2.5 Step 5: Data Sources and Availability at Regional or National Scales
When considering the availability of data to quantify or represent the metrics, ecosystem teams
recognized that local data would often exist (e.g., a report on the fishing at a local tackle shop or
a description of the landcover of local farms). For strictly local applications, these data are the
most appropriate to use even if they are not consistent with data from other locations. However,
when locations are compared or when regional and national analyses are at issue, more extensive
data are necessary.
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Extensive data for large regions can come from several different sources. Our tables list the
sources identified by each ecosystem team, but in general, these fall into four types of sources:
1. Direct observation of features of interest in a sampling design that allows for
extrapolation to a region of interest. Such data exist for aquatic ecosystems in EPA's
NARS program (U.S. EPA, 2020) (NARS include the National Coastal Condition
Assessment, U.S. EPA, 2015; the National Lakes Assessment, U.S. EPA, 2016a; the
National Rivers and Streams Assessment, U.S. EPA, 2016b; and the National Wetland
Condition Assessment, U.S. EPA, 2016c) and for forests in the USD A Forest Service
FIA program (Olsen et al., 1999; Oswalt, Smith, Miles, & Pugh, 2019; Stevens Jr &
Olsen, 2004).
2. Compilations of large amounts of existing data. For example, the EPA Water Quality
Portal (https://www.epa.gov/waterdata/water-qualitv-data-wqx) and the USGS National
Water Information System (https://waterdata.usgs.gov/nwis). These are essential
repositories for many analyses and are tempting for use in regional analysis. However,
given their lack of a design basis, these sources should not be considered to be a useful
source for regional analysis. Such compilations of existing data have been shown to be an
inefficient way to make regional estimates and may lead to conclusions completely
opposite from conclusions one would draw with data designed for this purpose (Paulsen,
Hughes, & Larsen, 1998; Peterson, Urquhart, & Welch, 1999). Further, users of such data
will also want to ensure that they understand whether the levels of consistency of data
collection and analysis in the data they may extract from such sources matches the needs
for consistency in their application.
3. Remote sensing data. To the extent that remote sensing can provide estimates of FEGS,
it is an invaluable source. In some cases, for example, with the FIA, remote sensing
determines the boundaries within which field observations are implemented.
4. Spatial interpolation models. These combine extensive data with field observations in
sampling programs to produce estimates for the population of resources in a region or
even for estimates of the status of a metric at all locations of a resource (e.g. Fox, Hill,
Leibowitz, Olsen, & Weber, 2016; Hill et al., 2017). This type of source is useful where
complete coverage, rather than population estimates, is required.
These four categories are described in terms relevant to national and regional analysis, and focus
on Federal sources of data. In many instances, ecosystem experts also identified state sources of
data (e.g., state data on fish in lakes). These can be invaluable sources of information on FEGS
for many purposes, but because of different definitions and methods, they may not aggregate
well quantitatively to a national or regional picture.
In contrast to the first type of source (designed direct observation), which produces population
estimates of ecosystem resources, the third and fourth source types (remote sensing and spatial
interpolation models) can provide spatially explicit and extensive representations of ecosystem
resources. Spatially explicit representations are important for economic analyses where
understanding the local abundance and scarcity of resources is critical to valuation and mapping,
which are in turn important for communication and planning. Therefore, ecosystem experts were
asked to specify whether the available metrics could be represented in such a manner. Answers
to this question are provided in the extensive table in the Appendix under the headings
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"Currently described over large areas via remote sensing?" and "Existing capacity to model over
large extents?"
In addition to specifying the data source for the available metric, ecosystem experts also
described the spatial and temporal scale of the data used. By spatial scale, we mean the spatial
extent of the data. Temporal dimensions cover the frequency or the temporal extent of the data.
We did not generally address a complementary and important question; specifically, what are the
spatial dimensions of the ecosystem features directly used, appreciated, or enjoyed by a
beneficiary? However, it is reasonable to expect that the temporal and spatial perspective of the
FEGS metric can matter to beneficiaries. The biophysical metrics reported in the metric tables in
the Results section are generally provided without a temporal or spatial unit specification. They
do not reflect spatial or temporal variation in FEGS metrics or how beneficiaries' interest and
interactions with nature may change. Ideally, metrics should be based on data that reflects when
and where the FEGS benefits are received, not necessarily as annual averages or as a sample
during an ecologically important index period.
An example of the "when" can demonstrate the importance of the timing of the FEGS metric. A
swimmer at a Lake Michigan beach, for example, cares more about the water temperature in July
than in January (when they are unlikely to be swimming). Likewise, a Midwestern farmer may
care less about a winter flood of floodplain farmland than a flood at planting or harvest time,
which could endanger the crop. These examples illustrate the need to specify the temporal
dimensions of ecosystem services from the perspective of the beneficiary.
These examples relate to the units of individual observations, but of equal importance is the
reporting extent. For some uses, the status of an individual stream location is relevant. For other
decisions, the relevant scale is the status of individual resources over a region. This report lists
sources of information that may be available for large regions or for the nation, which is the scale
at which national policies are formed. Those national policies may, for example, include
decisions about the allocation of funding to specific resources (e.g., aquatic ecosystems as
opposed to highway safety), specific stressors (e.g., riparian habitat as opposed to nutrients) or to
specific locations. The allocation of resources over a larger extent is likely to have strategic value
in the allocation of resources made by a different group of people. For example, an individual
home or property owner might choose to act due to the status of an individual resource. In
contrast, a governmental authority might make decisions on the status of resources over a
regional or national scale.
In addition to these categories of data, there is an abundance of data on human activity that is
closely dependent on ecosystem status. This includes the National Survey of Hunting and
Fishing, various surveys of recreational fishing catch and effort (https://www.fisheries.noaa.gov/
recreational-fishing-data/tvpes-recreational-fishing-survevs). and the National Census of
Agriculture (https://www.nass.usda.gov/AgCensus/index.php). These sources are generally a
source of surrogate metrics, e.g., fish landings (a measure of human activity directly dependent
upon ecosystem abundance) rather than fish in the water (a FEGS for some beneficiaries),
although they may contain some data on FEGS in some instances.
2.6 Example Data Visualizations
Each ecosystem subsection in the Results features one or two data visualizations of a FEGS
metric for a particular beneficiary. These visuals are examples of the metrics that may be used to
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express the interest of the particular beneficiary. Metrics can be continuous or categorial in
nature, but the usage of these forms differs.
• Continuous Metrics. These are quantitative metrics (including count metrics) that may
be displayed directly or binned into ranges first. Metrics with continuous units are best
for social science or economic analysis and are common in the economic literature. For
example, in a revealed preference example, Netusil, Kincaid, & Chang (2014) showed
that house sales prices vary as function of E. coli concentrations, dissolved oxygen,
temperature, total suspended solids, and pH in two streams near Portland, OR. The
magnitude of the effect varies as a function of distance from the streams. They also
showed a seasonal effect, where dry season (May to October) E. coli levels had a more
significant and negative effect on house prices than wet season levels.
• Categorical Metrics. These are qualitative metrics (e.g., poor, fair, good, excellent),
although they may be based on underlying quantitative data. These are more useful for
communication. Biophysical scientists can represent continuous data as categories or
grades. These categories reflect ecological conditions, especially as delineated in
Stoddard et al. (2006); regulatory standards; or an array of other reasons to communicate
and represent data in compelling ways.
The lesson that we drew from this is that the form of the metric used should vary depending on
the specific needs of the user. Thus, what we list as FEGS metrics in our tables is one metric that
represents something a beneficiary directly uses, appreciates, or enjoys, but for any particular
application, the analyst may choose to use the metric in a different form.
3. Results
This section presents the biophysical metrics for each of the seven ecosystem types considered in
this report (Sections 3.1 to 3.7), as well as a cross-ecosystem synthesis (Section 3.8) and a
discussion of data challenges (Section 3.9). The seven ecosystems are:1
• Section 3.1: Coral Reefs
• Section 3.2: Estuaries
• Section 3.3: Lakes
• Section 3.4: Rivers and Streams
• Section 3.5: Wetlands
• Section 3.6: Agricultural Systems
• Section 3.7: Forests.
For each ecosystem, a brief summary gives context for the system. A table lists the beneficiaries,
attributes, metrics, and data sources chosen for the ecosystem; these table are a subset of the
columns in the more detailed tables provided in the Appendix. The text that follows walks
through the steps outlined in Section 2 for one or two beneficiaries and attributes; the intent is to
illustrate the process, not to describe every metric. Each ecosystem section also includes one or
two example FEGS metric visualizations related to the metrics discussed in the text; these are not
the only possible visualizations, but examples to illustrate the approach. The subcaptions note
whether the metric visualized is continuous or categorical.
1 The names of the ecosystem experts who contributed to each section are listed at the beginning of each subsection
along with a brief affiliation; see the title page for more detailed affiliations.
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3.1 Coral Reefs
Deborah Santavy and Christina Horstmann
U.S. EPA Gulf Ecosystem Measurement and Modeling Division
Photo: Recreational scuba divers enjoy many of the benefits coral reef ecosystem goods and services provide.
Photo credit: Christina Horstmann.
Coral reefs are underwater ecosystems comprised of large structures build by reef-building corals
(Spalding, Ravilious, & Green, 2001). Thousands of live coral polyps build calcite reefs that are
inhabited by at least 25% of the world's marine species, although they occupy less than 0.1% of
the area of the world's oceans (Spalding et al., 2001). People frequently visit coral reefs to
experience their beauty, extensive biodiversity, and the vast refugia providing habitat for many
species of fauna and flora. Many more people enjoy reefs vicariously by viewing the colorful
marine organisms via television and social media. Since the invention of scuba, recreational
divers and reef visitors have traveled to experience paradise underwater, generating an estimated
$36 billion globally in economic activity, and $2.4 billion in the United States alone (Spalding et
al., 2017), thereby sustaining local, state, and Territorial economies. Other important recreational
services enjoyed by visitors are snorkeling, kayaking, boating, and recreational fishing.
Coral reef ecosystems provide a multitude of benefits to reef visitors (e.g., divers and snorkelers)
and residents of adjacent areas that contribute to their well-being, and these are not limited to
recreation. Other important ecosystem goods and services provided by reefs include coastline
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protection from ocean storms and floods, subsistence fishing, sense of place, and cultural way of
life for local and indigenous peoples. Visitors and residents alike benefit from tourism
opportunities, food products, aquarium fish, jewelry and curios, personal use products, and
unique pharmaceutical drugs. Coral reef organisms have proven to be important sources for the
development of bioactive products used to treat illnesses and other health problems. Protection of
these benefits and the ecosystem that provides them is an important objective for coral reef
managers. Currently, coral reef ecosystems are threatened by rapidly increasing coastal human
populations; climate changes such as increased sea temperatures and ocean acidification; and the
addition of detrimental substances dumped into watersheds and coastal waters.
Step 1. Ecosystem Delineation
Many reefs regarded to be in the same ecosystem are not self-contained, but they can be
separated by adjacent ecosystems such as mangroves and seagrasses. This fact makes coral reef
boundaries difficult to delineate compared to, for example, lakes or streams, which are defined
by their land-waterbody interface. Coral reefs are open marine systems and very irregular in their
distribution. Much like forests and mountain ranges, there can be physical and geochemical
barriers that prohibit species flow or crossover between adjacent coral reef ecosystems. Some
examples of barriers specific to coral reefs are water depth (ocean trenches, deep channels),
currents (regional and oceanic), and temperatures (tropical) (Walker, 2012). The outer edges of
the coral reef architecture define the physical boundaries, and generally they do not move due to
the sessile nature of reef-building corals. Thus, interpretation of coral reef ecosystem boundaries
becomes more difficult when considering reef mobile species, especially fish. The fish, and even
planktonic larvae, can swim to other reefs considered as the same coral reef ecosystem, but they
must past through other ecosystems to get to the next coral reef patch. Coral reefs are limited in
their distribution, and they require warm, oligotrophic waters. These limitations restrict the
distribution of coral reefs to the tropical oceans. Unlike lakes, rivers, and streams, the coral
animal deposits, as its skeleton, the underlying calcium-based reef structure of a coral reef, and
larva have specialized preferences that dictate where they will settle.
Boundary delineations for coral reefs can be complicated and uncertain unless considerable
effort has gone into using sophisticated mapping techniques to define reef edges and determine
where live coral reefs are located (NOAA, 2017). NOAA's U.S. coral reef maps were used to
delineate tropical coral reefs in the United States and its territories. The NOAA coral reef benthic
maps have limitations because they are based primarily on seafloor topography and have
constrained ability to predict where live coral reefs are located. This approach lacks the ability to
decipher the difference between dead geomorphic reef structures and living coral reef
environments. A second generation of NOAA maps have improved delineation of reef
boundaries after allowing time for considerable ground-truthing by research divers, remotely
operated vehicles, and underwater cameras. Currently, these second-generation maps provide the
best resource by providing coarse resolution where reefs are located, but they continue to be
improved by commercial satellite imagery.
Even though there is a dilemma for determining coral reef edges and boundaries with adjacent
ecosystems such as mangroves, seagrass beds and the open ocean, the goods and services
provided are usually limited to the reef area. An exception is coastal property owners, one
beneficiary group that does not utilize the reef area directly. There are many intermediate
ecosystem goods and services involved in how coral reefs provide coastal protection, such as
wave attenuation provided by reef height and coral morphology. Most of the time, the coral reef
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boundary and adjacent ecosystems are far from the shoreline, at distances ranging from meters to
kilometers.
Step 2. Beneficiary Specification
The coral reef team selected 11 beneficiaries from the NESCS Plus classes that directly benefit
or interact with coral reef ecosystems; these represent a diverse and broad spectrum of
beneficiaries that interact with most FEGS that coral reefs provide (Table 6).
Table 6. Available FEGS Metrics for Beneficiaries of Coral Reefs
1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Aquaculturists
Coral Nurseries
Water
Water quality
Turbidity: FTU and NTU ppm.
Visibility: m. Satellite
chlorophyll a: relative
concentrations. Light
penetration: Kd, PAR
NOAA: satellite,
monitoring by
nursery owners
No
No
Coliforms, enterococci,
vibrios (CFUs). Microbial
toxins, heavy metals and
chemicals: |jmol/L)
Local beach water
quality, NOAA,
monitoring by
nursery owners
No
Yes
Temperature
NOAA
Yes
Yes
Flora
Flora community
Abundance
Observational
surveys by nursery
owners
No
No
Soil/Substrate
Substrate quantity
Percent uncovered
Monitoring by
nursery owners
No
No
Substrate quality
Reef type
Monitoring by
nursery owners
No
No
Pharmaceutical and Food Supplement Suppliers
Bioprospectors
Fauna
Commercially
important fauna
Diversity, Richness, and
Abundance
Published Literature,
EPA, NOAA, State
No
No
Edible fauna
Published Literature,
EPA, NOAA, State
No
No
Medicinal fauna
Published Literature,
EPA, NOAA, State
No
No
Flora
Commercially
important flora
Diversity, Richness, and
Abundance
Published Literature
No
No
Edible flora
Published Literature
No
No
Medicinal flora
Published Literature
No
No
Extractors
Fauna
Medicinal fauna
Abundance, size, species
Published Literature,
EPA, NOAA, State
No
No
Commercially
Important fauna
Abundance, size, species
Published Literature,
EPA, NOAA, State
No
No
Flora
Medicinal flora
Abundance, size, species
Published Literature
No
No
Commercially
Important flora
Abundance, size, species
Published Literature
No
No
Soil/Substrate
Substrate quantity
Habitat type
Published Literature,
benthic habitat maps
No
No
Substrate quality
Habitat type
Published Literature,
benthic habitat maps
Yes
Yes
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Timber, Fiber and Ornamental
Extractors
Ornamental Extractors
Fauna
Commercially
important fauna
Commercially important live
aquarium (species, size,
abundance)
Published Literature,
EPA, NOAA, State
No
No
Organisms used for products
(species, size, abundance)
Published Literature,
EPA, NOAA, State
No
No
Flora
Commercially
Important flora
Commercially important
aquarium (species, size,
abundance)
NOAA, State, NPS
No
No
Organisms used for products
(species, size, abundance)
NOAA, State, NPS
No
No
Residential
Property Owners
Coastal Property
Owners
Composite
Extreme events
Probability of Flooding
NOAA SLOSH
model, FEMA flood
risk maps,
EnviroAtlas
No
Yes
Site appeal
Water clarity
Survey data and
satellite
No
Yes
People Who Care
Existence values
Water
Water Quality
Common water quality tests
Local beach water
quality, NOAA
mussel watch
No
No
Fauna
Fauna community
Diversity, Richness, and
Abundance
Published Literature,
EPA, NOAA, State
No
No
Flora
Flora community
Diversity, Richness, and
Abundance
NASA Satellite/
Online Posting,
NOAA, State
No
No
Composite
Ecological condition
—
—
—
—
Soil/Substrate
Substrate Structure
Reef type, rugosity
NOAA, NASA, Coast
Guard, local shops
No
No
Composite
Site appeal
Field crew opinion, Secchi
depth, algal abundance
Word of mouth, local
bait and tackle
shops, local radio
and TV fish reports
No
No
Boaters
Kayakers, SUPs,
and Boaters
Fauna
Charismatic fauna
Species, size, abundance,
diversity
U.S. FWS, NOAA,
State fisheries
departments (FWC)
No
Yes
Composite
Site appeal
Field crew opinion
Word of mouth, local
bait and tackle
shops, local radio
and TV fish reports
No
No
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Water
Water quality
Diver recorded visibility
Online posting; diver
recorded visibility;
NOAA Satellite
No
No
Common water quality tests
Local beach water
quality, NOAA
mussel watch
No
No
(J)
Charismatic fauna
Presence/absence of
charismatic fish
EPA, NOAA, State
Species
specific
Yes
CD
>
Q
~u
d
03
to"
a>
E
E
a>
CD
O
C
CO
¦a
C
03
(/)
Fauna
Fauna community
Fish biomass, size,
abundance, diversity,
richness, species name,
feeding guilds, species
description
EPA, NOAA
No
Yes
"S
CO
c/>"
a;
"a
CD
>
Q
03
_Q
=3
Coral species name,
morphotype, abundance, size
(cm), health, rugosity
EPA, NOAA
No
Yes
CO
Charismatic flora
Algal abundance, species
name, size, density, % cover
Published literature
No
Yes
Flora
Flora community
Invasive species
presence/absence, density,
taxa, extent, presence
NASA Satellite/
Online Posting,
NOAA, State
Yes
Yes
Soil/Substrate
Substrate quality
Reef structure (reef type,
rugosity)
EPA, NOAA
No
No
Composite
Site appeal
Local Reports
Online Posting
No
No
Composite
Site appeal
Local reports
Online Posting
No
No
? CD
m w
? 03
CD
Fauna
Fauna community
Hazardous species
Presence/absence
Beach Flags, Online
Posting
No
Yes
03 oc
o
Soil/Substrate
Substrate quality
Local reports
Online Posting
No
No
C
O
Fauna
Charismatic fauna
Presence/absence
State, Federal
No
Yes
cc
a>
o
CD
(/)
"cc
Fauna
Fauna community
Cone, of pathogens/ toxins/
Contaminants/ parasites in
fish
FDA, USDA
No
Yes
_a>
CD
C
<
"6
£=
CO
Fauna
Fauna community
Hazardous species
Presence/absence
Beach Flags, Online
Posting
No
Yes
"cc
o
Soil/Substrate
Reef Structure
Local reports
Online Posting
No
No
Fauna
Taxa
Presence/absence
State, Federal
Yes
Yes
Composite
Site appeal
Local reports
Online Posting
No
No
Edible Fauna
Edible fauna
Presence/absence
State, Federal
Yes
Yes
CO
'aT
Fauna
Fauna community
Hazardous species
Presence/absence
Beach Flags, Online
Posting
No
Yes
-Q
=3
CO
CD
c cn
Soil/Substrate
Substrate quality
Local reports
Online Posting
No
No
¦a
o
o
Ll_
< -§
w.
Fauna
Fauna community
Cone, of pathogens/ toxins/
Contaminants/ parasites in
fish
FDA, USDA
No
Yes
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Step 3. Attribute Specification
For coral reefs, we selected scuba divers and snorkelers (subsequently referred to as just divers)
as the beneficiary of interest to demonstrate the FEGS framework and apply standardized
language from Table 4 when selecting attributes to describe the biophysical components of
FEGS metrics. For this beneficiary, the primary attribute is the water itself. The sub-attributes for
water are the water quality, quantity, and movement. These attributes influence how a diver
interacts with and experiences the underwater coral reef environment. One key specific attribute
is water clarity (under the sub-attribute water quality), which is of great interest to divers and
snorkelers. Water clarity is vital for the divers to see and experience coral reefs and is linked to
the ability to experience the coral reefs and associated fish communities. Unfortunately, water
quality for coral reefs can often be quite variable depending on the location, season, time of day,
ocean conditions, and weather. There are few long-term databases at regional or national scales
for water clarity of coral reefs, because it is so variable over large spatial scales.
Divers care directly about many other specific attributes for water quality, for example the
presence of harmful contaminants and high pathogen concentrations (see Table 4 for more
specific attributes analyzed). Exposure to harmful materials or microorganisms is potentially
dangerous since divers and snorkelers are immersed in the water and may also ingest seawater
accidentally. Many states, territories, counties, and municipalities regularly monitor and report
this information as part of water quality reporting requirements for treated wastewater and U.S.
EPA programs under the 2000 BEACH Act (Beaches Environmental Assessment and Coastal
Health Act, Public Law 106-284). Results that identify whether there are health or safety issues
with contact or immersion in a particular waterbody are reported to the public by signage, online,
and in newspapers. The water quality sub-attributes are of direct interest to all divers and
snorkelers, not reserved to just coral reef ecosystems.
The important and unique qualities valued by divers on coral reefs are FEGS metrics
representing the presence, abundance, size, and species diversity of the fauna communities.
These sub-attributes for coral and fish communities are what attract divers, who hope to see
many different colors, textures, and movements on a healthy reef. Other important faunal metrics
are the presence and abundance of charismatic species, such as sharks, turtles, dolphins, and
large fish. Likewise, the presence of flora community can be important, like charismatic species
of bright pink crustose coralline algae as an example of a specific flora attribute. These are
usually recorded when seen and reported to others in the area, but their locations usually cannot
be predicted in real time and space. Many recreational divers are thrilled with an encounter with
one of these species; therefore, availability of these metrics would certainly be preferred by
many coral reef recreators when making choices where to dive. Due to the rarity of charismatic
species encounters, the main sub-attributes that divers look for are fauna and flora communities.
Divers also experience the environment beyond the species themselves. The reef structure is
considered a soil/substrate attribute and categorized as the sub-attribute of substrate quality
evaluated by the metric rugosity. Finally, a composite attribute includes the sub-attribute of site
appeal, which is the experience and interaction with the underwater viewscape, using all senses
(sight, hearing, touch).
Step 4. Metric Specification
For water clarity, the ideal metric is Secchi disk depth because it is easily understood and
obtained, reliable, inexpensive, and widely used. Water clarity could also be translated from
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satellite imagery into Secchi disk depth (Kloiber, Brezonik, & Bauer, 2002).The water clarity
metric most commonly referenced in the literature is underwater visibility recorded by individual
divers. These estimates are often guided by experience and not usually recorded or made
available to other divers. Thus, they are not measured consistently across coral reef locations and
can be less reliable and more subjective. Since this is the attribute that divers care the most
about, making average monthly visibility reports available to beneficiaries would be especially
desirable for divers choosing dive vacation destinations. Other potential metrics for water clarity
include chlorophyll a measurements and total suspended solids determined by water sampling
and satellite imagery. As the ability to model clarity from water quality parameters improves, it
will be increasingly derived from satellite data, such as chlorophyll a, and made available to the
diving public (Kloiber et al., 2002).
For reef structure, desirable geological structures (e.g., high relief, large drop offs, cavernous
tunnels) can be very location specific and are always a lure for experienced divers and
snorkelers. Other sources for potentially preferred dive sites might be obtained from underwater
benthic habitat maps determined by side scan sonar and LIDAR (light detection and ranging)
imagery. Usually, spatially explicit data at regional and state levels are not obtained over long-
term or regular temporal scales. Closing this gap would require significant changes in monitoring
and sampling efforts or a greater attempt to combine localized datasets to report FEGS on
regional or national scales.
Metrics deemed most important to the beneficiaries should be monitored consistently over time
and at many locations by researchers or environmental departments. Currently, most monitoring
programs are designed and used by biophysical scientists evaluating coral reef condition in an
ecological context. When more recreational divers communicate what traits they value most,
there might be increased efforts to obtain these biophysical data, translate them into reliable
FEGS, and communicate them in places where they are easily obtained by the beneficiaries and
the public. These actions would be driven by increased public or beneficiary demand.
Step 5. Data Sources and Availability
In Table Al of the Appendix, currently available data and the idealized dataset for many of the
FEGS metrics for coral reefs are presented. The data sources provided are ones useful for a
national or regional audience and for the analysts and policy makers working at that scale. For all
beneficiaries, we assume that local data useful to individual beneficiaries making individual
decisions may be available. For this ecosystem, we delineate some of those local data sources
here and their intersection with national and regional datasets.
Because of the patchy nature of reefs, most of spatial data are very localized and site specific,
making it difficult to scale up to greater temporal and spatial scales without a large effort for data
organization and FEGS metric communication to the public. After examining numerous datasets,
several trends were identified. When a dataset covers a large spatial region, often the data are
limited temporally. This might be resolved by incorporating coral reef monitoring and metrics
into a national assessment and data program like NARS. Unfortunately, coral reefs are limited to
mostly U.S. Territories that are often lower priority when considering budgets and where limited
resources will be spent. The monitoring of coral reefs is usually very expensive and time
consuming, and many measurements are made using scuba.
The databases we have found that contain the most extensive reef coverage for reefs found in the
United States and Territories is accessible online through NOAA's National Coral Reef
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Monitoring Program. These data are suitable for calculating FEGS metrics to be used in analyses
that can be appropriately scaled both spatially and temporally to apply the management decision
context (NO A A, 2017; NOAA and U.S. Coral Reef Task Force, 2014). The program was
designed to report status and trends of coral reef condition and their associated communities.
Many of these data have not yet been sufficiently or adequately translated into metrics directly
understandable by beneficiaries (especially as discussed in Section 2.4, Metric Specification, and
Section 3.9, Challenges to Providing Data on FEGS), as the implementation of an ecosystem
goods and servi ces perspective is relatively novel for overworked coral reef resource managers.
This foundational work might initiate interest in utilizing some of the FEGS biophysical metrics
to be further translated into more meaningful communications to divers. Future work could
utilize these datasets for analyzing metrics to determine useful FEGS indicators that are widely
available for multiple beneficiaries using reef ecosystem goods and services.
Example Visualizations for FEGS Metrics in Coral Reefs
The two examples of data visualization for important coral reef metrics are from the Great
Barrier Reef in Australia. Figure 4 illustrates how a metric such as coral cover can be shown on
a large spatial scale. This metric is important to scuba diver beneficiaries. Figure 5 represents
our suggested metric for water clarity (Secchi disk depth).
15° S
20° S
25° S
Figure 4. Percent live coral cover on the Great Barrier Reef, Australia.
Percent live coral cover is a metric for many coral reef beneficiaries, e.g., Scuba Divers and Snorkelers as shown in
Table 6. This is classified as a continuous metric. Green represents lower coral cover and red represents higher.
Source: (AIMS, 2015).
LTMP_Manta-Live_Cor3l-
Cover_per_cent
©2019 eAtlasr Google
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1450 E 150°E
Secchi-Secchi-m
©2019 eAtlas, Google
Figure 5. Secchi disk depth for the Great Barrier Reef, Australia, 1992-2006,
Secchi disk depth is a measure of water clarity. This is classified as a continuous metric. Here, green represents
lower water clarity and red represents higher (AIMS, 2009).
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3.2 Estuaries
Walter Berry and James S. Latimer, U.S. EPA Atlantic Coast Environmental Science Division
Photo: This spot on a small estuary in Rhode Island is popular with boaters and recreational anglers, but is especially
popular with kayakers and standup paddleboarders. Photo credit: W. Berry.
Estuaries are where rivers meet the sea. They have long been hubs for commerce, providing
sheltered harbors with access to inland areas. Many of the nation's largest cities have grown up
around these harbors. Estuaries produce many benefits (or ecosystem services) for people:
recreational opportunities; food; storm and flood protection; natural beauty; and important
cultural significance to the region. They also support touri sm, transportation, and other economic
activities, as they have for centuries, and provide support to a broad range of scientific and
educational activities.
Coastal counties of the United States are home to over 126 mill ion people, or 40 percent of the
nation's total population (NOAA, 2019a [Fast Facts]). Not only do estuaries provide ecosystem
services to the people live close to or physically use them, they also help to define the character
of a whole region. Examples include the importance of the quahog fishers in Narragansett Bay to
the identity of Rhode Island residents or the Chesapeake Bay watermen to the residents of
Delaware, Maryland, Virginia, and the District of Columbia.
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Step 1. Ecosystem Delineation
An estuary is a partially enclosed body of water with one or more rivers or streams flowing into
it upstream and a connection to the open sea downstream. In a modern estuary, the upstream
boundary of the estuary is often a dam, above which the water is fresh. Below the dam, the water
is brackish, increasing in salinity until it meets the sea. Estuaries in the United States vary in size
from tiny creeks flowing into the ocean to the Chesapeake Bay (approximately 4,480 square
miles (Chesapeake Bay Program, 2020).
Step 2. Beneficiary Specification
The Estuary team selected nine beneficiaries from the NESCS Plus classes that directly benefit
or profit from estuarine ecosystems (Table 7). These represent a diverse and broad spectrum of
beneficiaries that interact with most FEGS that estuaries provide.
Table 7. Available FEGS Metrics for Beneficiaries of Estuaries
1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Aquaculturists
Shellfish Growers
Water
Water quality
Turbidity: FTU & NTU, ppm.
Visibility: m. Light
penetration: Kd, PAR
NOAA: satellite,
monitoring by
shellfish growers
No
No
Coliforms, enterococci, vibrio
(CFUs). Microbial toxins,
heavy metals & chemicals
Local beach water
quality, NOAA,
monitoring by
nursery owners
No
No
Shellfish closures
Local beach water
quality, NOAA,
monitoring by
nursery owners
No
No
Salinity, Temperature, pH,
DO
State, University,
NARS
Sea
surface
temp
No
Water
Water Movement
Tides, wind speed &
direction
Data from
meteorological
reporting stations &
marine buoys,
NOAA
No
Yes
Fauna
Commercially
important fauna
Abundance, species
Observational
surveys by shellfish
growers
No
No
Flora
Commercially
important flora
HAB (outbreak frequency)
Observational
surveys by shellfish
growers
No
No
Chlorophyll aChlorophyll a
Observational
surveys by nursery
owners
No
No
Soil/Substrate
Substrate quantity
Permitted area
Municipal records
No
No
Fauna
Commercially
important fauna
Shellfish closures due to
disease organisms
Monitoring by
shellfish growers,
State postings
No
No
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Commercial Anglers
Commercial Anglers
Water
Water movement
Tide, surge, wind speed &
direction
NOAA, Weather
channel
Yes
Yes
Wave height, speed &
direction
NOAA, Weather
channel
Yes
Yes
Atmosphere
Wind strength/speed
Wave height, speed &
direction
NOAA, Weather
channel
No
Yes
Fauna
Fauna community
Presence/absence
State, Federal
Yes
Yes
Cone. Of Pathogens/ Toxins/
Contaminants/ Parasites
FDA, USDA, State
No
Yes
Hazardous Species
Presence/absence
Beach Flags, Online
Posting
No
Yes
Soil/Substrate
Substrate quality
Local reports
Online Posting
No
No
Water
Water movement
Tide, surge, wind speed &
direction
NOAA, Weather
channel
Yes
Yes
Pharmaceutical and Food Supplement
Suppliers
Extractors/ Bioprospectors
Fauna
Medicinal Fauna
Abundance, size, species
Published Literature,
EPA, NOAA, State
No
No
Commercially
Important Fauna
Abundance, size, species
Published Literature,
EPA, NOAA, State
No
No
Fauna community
Diversity, richness of
extractable source
Published Literature,
EPA, NOAA, State
No
No
Flora
Medicinal Flora
Species Abundance, size,
species
Published Literature
No
No
Commercially
Important Flora
Species Abundance, size,
species
Published Literature
No
No
Flora community
Species Diversity, richness
of extractable source
Published Literature
No
No
Residential
Property Owners
Coastal Property
Owners
Composite
Extreme events
Probability of flooding
FEMA Maps and
EnviroAtlas
No
Yes
Site Appeal
—
—
No
No
Transporters
Barge or ferry
Water
Water movement
Local/ Regional currents
Data from
meteorological
reporting stations &
marine buoys,
NOAA
Yes
Yes
Water movements
Water intensity
Data from
meteorological
reporting stations &
marine buoys,
NOAA
Yes
Yes
Water quality
Nautical Hazards
NOAA charts
No
Yes
Atmosphere
Wind strength/speed
Wind intensity
Data from
meteorological
reporting stations &
marine buoys,
NOAA
No
Yes
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
People Who Care
Existence
Composite
Site appeal
Field crew opinion
Word of mouth, local
bait & tackle shops,
local radio & TV fish
reports
No
No
Ecological Condition
—
—
—
—
Water
Water quality
Common water quality tests
Local beach water
quality, NOAA
mussel watch
No
No
Fauna
Fauna community
Diversity, Richness &
Abundance
Published Literature,
USEPA, NOAA,
State
No
No
Flora
Flora community
Diversity, Richness &
Abundance
NASA Satellite/
Online Posting,
NOAA, State
No
No
Soil/Substrate
Substrate quality
Shoreline Type
NOAA, NASA, Coast
Guard, local shops
No
No
Boaters
Kayakers, SUPs,
and Boaters
Fauna
Charismatic Fauna
Species, size, abundance,
diversity
U.S. FWS, NOAA,
State fisheries
departments (FWC)
No
Yes
Composite
Site appeal
Field crew opinion
Word of mouth, local
bait & tackle shops,
local radio & TV fish
reports
No
No
Anglers (Recreational)
Catch-and-Release
Fauna
Charismatic fauna
Presence/absence
State, Federal
No
Yes
Fauna community
Hazardous species
Presence/absence
Beach Flags, Online
Posting
No
Yes
Composite
Site appeal
Local reports
Online Posting
No
No
Catch-and-Eat
Fauna
Fauna community
Presence/absence
State, Federal
No
No
Cone, of pathogens/ toxins/
contaminants/ parasites
FDA, USDA, State
No
Yes
Hazardous species
Presence/ absence
Beach Flags, Online
Posting
No
Yes
Composite
Site Appeal
Local reports
Online Posting
No
No
Food
subsisters
Anglers
(Subsistence)
Fauna
Fauna community
Presence/absence
State, Federal
No
No
Cone, of pathogens/ toxins/
contaminants/ parasites
FDA, USDA, State
No
Yes
Hazardous species
Presence/absence
Beach Flags, Online
Posting
No
Yes
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Step 3. Attribute Specification
For estuarine systems, we selected anglers as the broad beneficiary group to demonstrate the
FEGS Framework methodology for attribute selection. Within this category, we include
commercial, recreational (catch-and-release and catch-and-eat), and subsistence anglers. These
attributes are drawn from the standardized list in Table 4 and are used to describe the biophysical
attributes of FEGS metrics.
For this beneficiary group, the primary attribute is the fish community, which is first listed as the
fauna in the attribute category. Different types of anglers care about specific types of fish
communities; these important distinctions are described in the subattribute column as edible
fauna, keystone fauna, charismatic fauna, or commercially important fauna. The degree to which
the attribute is more important to the specific beneficiary likely relates to the degree of
dependence the angler has on the fish—for example, are the fish the primary source of
sustenance or revenue, or a weekend recreational activity? Commercial, subsistence, and
recreational catch-and-eat anglers care about whether the type of fish are edible and safe to eat
(i.e., free of tissue contamination); catch-and-release anglers likely do not. Anglers who are not
dependent on the fish biomass for food or profit also typically consider the whole experience of
fishing—the sights, sounds, and smells and so are more likely to care about site appeal (is the
fishing location pleasant to the senses?) than commercial or subsistence anglers. This important
sense of place and personal experience is accounted for in the composite attribute category with
site appeal as the specific subattribute. Commercial fishers also care about tides and waves
(subattributes of water categorized as water movements) and related atmosphere attributes of
wind speed and strength, which can also affect wave intensity and direction.
Step 4. Metric Specification
This metrics discussion focuses on the interests of recreational catch-and-eat and subsistence
anglers. These anglers care about the specific fish species, abundance, and size, which are FEGS
metrics described below. These considerations are generally similar for both those harvesting
finfish and those harvesting shellfish.
Anglers need information on abundance, species, and size to be very specific for it to be useful.
Fishing success can vary tremendously with location, season, time of day, or the tide. This sort of
specific information used to be available only from local sources like bait shops and newspapers
but is now more available on the internet due to newsletters and listservs (e.g., from the Rhode
Island Saltwater Anglers Association) and apps (e.g., Fishbrain). Information on contamination
of fish is available on a local level, although it will usually be in the form of fish advisories (e.g.,
"Don't eat the fish if you are a nursing mother or a small child."). Information on the appeal of
fishing sites is likely to be only available from places like bait shops.
Most of the fisheries data available for estuaries are collected by and for fishery biologists and
managers. Managers are not as interested in local data as anglers are. They typically collect data
on larger scales—statewide or beyond. These data are more in line with the scales of the
management questions they work with, such as
"Will we exceed our state quota for a particular species?"
"How is the stock doing (is it increasing or decreasing)?"
"Should we tighten or loosen restrictions on a particular fishery?"
"What is the economic benefit of recreational fishing to my state?"
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Some data are available on the actual abundance of individual species in estuaries, but for the
most part, the available data relates to juveniles caught in seines or bottom fish caught in large
bottom trawls, which is of little use to anglers and may only be available by special request (e.g.;
Rhode Island Department of Environmental Management [RIDEM] juvenile flounder data; Anna
Gerber Williams [RIDEM], personal communication). These data are, however, useful for
looking at long-term trends in various fisheries and predicting what the stocks will do in the
future. By contrast, a lot of data are available on the numbers and weights of fish harvested in
estuaries. These data are usually reported on a scale useful for management, but not useful for
most recreational anglers, and do not represent a FEGS because of the effort component in catch
data. Further complicating matters from an estuarine perspective is that estuarine fisheries are
often lumped with fisheries data from areas farther offshore.
Thus, the currently collected data are of a type more useful to fisheries managers than individual
anglers, and the continued monitoring of even those data may be imperiled by budget cuts. The
rapidly changing climate means that long-term data sets are more important than ever to provide
context on observed changes in fish abundance and species composition, but these may not be
predictive of what will happen in the future. Recreational anglers would benefit from more site-
specific information. This may be easier for shore anglers, because access is limited in many
areas, although some fishing charts are available for those fishing from a boat. To be really
useful, the information must be season- and species-specific. For example, a given dock or
bridge may be a great spot for squid or young-of-the-year bluefish at certain times of the year but
be virtually useless for fishing at other times of the year.
Information about tissue contamination is generally available in the form of fish advisories for
those species that are harvestable. Often the information is site specific, with postings that a
certain area is closed to fishing or shellfishing. Shellfishing advisories are often conditional, with
certain areas being closed after rainfall.
Site-specific information on site appeal is not generally available for estuaries. It may also be
that this attribute is not as important as it is in some other ecosystems (e.g., lakes and streams)
because estuaries are generally more open and developed than other ecosystems.
Some of the recreational catch data that could make an available metric are already collected
(NOAA, 2019b [Recreational Fishing Data]). However, catch data are only surrogates for FEGS.
The FEGS is the abundance of fish in the water. Fish landings depend on the abundance of fish
in the water, but are also a measure of human activity along with site access, technology, and
skill (Maunder et al., 2006).
Step 5. Data Sources and Availability
Much of the available fisheries data are collected on a regional scale and are designed to be
scaled up to a national scale, like NOAA's Marine Recreational Information Program (NOAA,
2020a [About the Marine Recreational Informational Program]). Other data are collected on a
state scale, like RIDEM's Narragansett Bay Juvenile Finfish Seine Survey (RIDEM, 2019), and
scaling these data up to regional or national scales may be more difficult because of the different
methodologies used from place to place. As stated above, much of these data (e.g., NOAA's
Marine Recreational Fishing Information Program; NOAA, 2020a) are catch data, which are
only surrogates for FEGS; only some of the available data are actual "numbers of fish in the
water" data (e.g., Narragansett Bay Juvenile Finfish Seine Survey; RIDEM, 2019), which
directly measure the FEGS.
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Most of the metrics selected for this project were drawn from the National Coastal Condition
Assessment (U.S. EPA, 2015), a project that periodically surveys the nation's estuaries. The
sampling for this assessment is based on more than 1,100 independent samples from five regions
of the country, representing the variation in estuary condition. Each site is sampled once,
between June and September. This sampling is repeated every five years; the most recent
publicly available dataset is the 2010 sampling data; results from the 2015 survey are expected in
2020. Results are generally pooled into annual means and seasonal averages are not available,
which may impact some beneficiaries, like swimmers, who likely care more about summertime
water temperature highs and lows.
Example Visualizations for FEGS Metrics in Estuaries
For both recreational and commercial anglers, the key FEGS is "Faunal Community", actual fish
in the water. Figure 6 shows two datasets for Winter Flounder and American Lobster in
Naragansett Bay. Figure 6a is an example of data representative of the actual FEGS (abundance
of American Lobster and Winter Flounder), because it was taken using a standard trawl, from a
single site in Narragansett Bay. These data might not be as useful to a fishery manager but might
be more useful to someone looking to target fish in Narragansett Bay.
However, as is discussed above, most of the available data on faunal abundance of fish in
estuaries that would be of interest to recreational and estuarine anglers is actually catch data,
which are only surrogates for fish abundance, because they are confounded by level of effort.
Most of these are also collected on a large scale, appropriate for regional fish stock assessment.
Figure 6b, taken from NOAA landings, is an example of this sort of data. It shows the poundage
of American Lobster and Winter Flounder caught in Rhode Island from 1950-2012.
o - amrsBmoffi
1960 1970
1980 1990
Year
2000 2010
¦o
0
"O
T3
C
D
O
Q.
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3.3 Lakes
Ted R. Angixidi, U.S. EPA Great Lakes Toxicology and Ecology Division
Photo: Lakes provide many important ecosystem services to a variety of beneficiaries. For recreational boaters,
enjoyment of the water and the setting are important contributions from nature. Photo credit: DOI.
Freshwater lakes, reservoirs, and ponds provide many benefits to a variety of users including
recreational users such as swimmers, anglers, property owners, farmers, and subsistence food
gatherers. Benefits include serving as a source of water for consumptive uses (e.g., irrigation,
drinking water, or for cooling), non-consumptive uses such as contact recreation, and non-use
existential benefits. Recreational fishing can be either a consumptive use when caught fish are
eaten or a non-consumptive use when catch and release is practiced. In many inland and arid
areas of the country, lakes and reservoirs are a primary location for outdoor recreation.
There are about 41 million acres of lakes in the U.S. and tens of thousands of lakes in the lower
48 states (U.S. EPA, 2003a). The exact number is surprisingly difficult to describe without
extensive qualifications. For example, how does one count the smallest ponds or distinguish
temporary water bodies from a lake? In addressing questions such as this, the National Aquatic
Resource Surveys (NARS) surveys defined a target population of 159,652 lakes. The NARS
surveys (e.g., the National Lakes Assessment [NLA] of 2012; U.S. EPA, 2016a) provide an
abundance of data on lakes. In some areas, lakes and ponds provide irreplaceable benefits for
specific users. Examples include wild rice in lakes from the upper Midwest and livestock stock
ponds (tanks) in the arid west. Reservoirs provide drinking water for many communities
including for large cities such as New York City, New York and Boston, Massachusetts
(https://wwwl.nvc.gov/site/dep/water/drinking-water.page.
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https://www.mass.gov/locations/quabbin-reservoir). Many millions of people depend upon
reservoirs and their watersheds as sources of safe drinking water
Step 1. Ecosystem Delineation
How the lake ecosystem is delineated depends on the beneficiary. For anglers, swimmers, and
boaters, the wetted perimeter is a reliable boundary for the part of the system that provides
benefits. For property owners and non-use beneficiaries, lake benefits may depend on composite
attributes of the non-aquatic but closely associated habitats of the lake setting, such as scenic
views that including riparian and terrestrial vegetation. For some beneficiaries, the appropriate
delineation may overlap with another ecosystem. For example, the lake habitat from which wild
rice harvesters derive a benefit may in other contexts be considered wetlands.
Step 2. Beneficiary Specification
The Lakes team selected eight beneficiaries from the NESCS Plus classes that directly benefit or
interact with lakes (Table 8). These beneficiaries include consumptive and non-consumptive
users of lakes.
Table 8. Available FEGS Metrics for Beneficiaries of Lakes
1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Residential Property Owners
Lakeshore Property Owners
Composite
Site Appeal
Appealingness score (1-5
scale)
EPA National
Lakes Assessment
No
No
Flora
Flora Community
Presence/absence of
nuisance species, total
macrophyte abundance (0-4
scale)
EPA National
Lakes Assessment
No
No
People Who Care
Existence values
Fauna
Fauna Community
Macroinvertebrate MM I score
(0-100), or condition class
(good, fair, poor)
EPA National
Lakes Assessment
No
No
Composite
Site appeal
Pristineness (1-5 scale)
EPA National
Lakes Assessment
No
No
Boaters
Power Boaters
Water
Water quality
(clarity)
Secchi depth (m)
EPA National
Lakes Assessment
Yes
No
Flora
Commercially
important flora
(nuisance species
presence)
Coverage (%)
State Websites
Maybe
No
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Waders, Swimmers, and Divers
Swimmers
Water
Water movement
(waves and
currents)
Water currents - Beach
hazard warnings (red, yellow,
green)
State and local
websites
Yes
No
Water quality
(composite)
Swimmability (good, fair, not
swimmable)
EPA National
Lakes Assessment
Yes
No
Water quality
(clarity)
Secchi depth (m)
EPA National
Lakes Assessment
Yes
No
Water quality
(pathogens)
Cyanobacteria concentrations
(cells/mL)
EPA National
Coastal Condition
Assessment
Yes
Yes
Microcystin concentrations
(ng/L)
EPA National
Coastal Condition
Assessment
Yes
Yes
Anglers (Recreational)
Catch-and-
release
Fauna
Culturally important
fauna (abundance)
Presence/absence, lake
survey, creel survey
State Websites
No
No
Catch-and-
eat
Fauna
Edible fauna (tissue
contamination)
Mercury, PCBs, PAHs,
concentration (ppb)
EPA National Lake
Fish Tissue Study
No
Maybe
Fish consumption advisories
by water body (y/n)
State Websites
No
No
Food Subsisters
Wild Rice
Harvesters
Flora
Culturally important
flora (wild rice)
Recognized wild rice lake
(y/n)
State websites
No
No
Wild rice area (ha)
Landsat 7
Yes
No
Anglers
ISubsistence)
Fauna
Edible fauna
(abundance)
Recruited biomass (kg/ha)
State Websites
No
No
Step 3. Attribute Specification
We selected recreational anglers, specifically, both a catch-and-release and a catch-and-eat
angler, as the beneficiary type of interest to illustrate the FEGS Framework methodology. Using
this beneficiary, we then selected attributes from the standardized list in Table 4. Both anglers
care primarily about the fish community, which is first represented in the attribute table as fauna.
The main distinction for these two anglers is whether the fish is caught for sport or for eating.
For catch-and-release anglers, who are excited for sport fishing and game fish, the attribute
subcategory of interest is therefore charismatic fauna. Catch-and-eat anglers, by contrast, care
that the fish are safe to eat, so the attribute subcategory of interest is therefore edible fauna. From
these standardized attributes, specific metrics were chosen as the FEGS metric.
Step 4. Metric Specification
Continuing to use the angler as the beneficiary to demonstrate the FEGS Framework, we then
selected the best available and ideal FEGS metric. This method and way of thinking was then
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repeated for each beneficiary. For this beneficiary, the fish community itself is the best metric.
These data are likely available from state fish and wildlife agencies, which track these data
carefully. Catch-and-eat anglers also care about the safety of eating the fish, notably the presence
and concentration of mercury and other chemical or biological contaminants.
Step 5. Data Sources and Availability
No national fish community dataset exists that captures what recreational anglers value; such
data are usually available at a state or regional levelm and there are practical barriers to the
creation of regional or national metrics of fish FEGS and the angling benefits therefrom.
Foremost among these challenges is the lack of consistency of the data across states. For
example, all the states bordering Minnesota have some public information that is comparable to
that available from that state's LakeFinder application, which may be accessed at
https://mapsl.dnr.state.mn.us/lakefinder/mobileA but the information varies in completeness and
format. It may be possible to compile and standardized the underlying data at a regional or
national scale, but that would require a significant effort.
Adding a characterization of fish populations would improve the National Lake Assessment,
however, it is unlikely that this will happen. Fish sampling would be expensive, difficult to
standardize across lake types, and would likely not be representative of fish species and
population characteristics most relevant to recreational anglers. More useful would be an effort
to compile available state and tribal fisheries information into ecoregional or national databases
from which indicators could be developed.
Example Visualizations for FEGS Metrics in Lakes
Easy to collect metrics for the water clarity attribute of lakes, in particular Secchi depth, may be
suitable national- or regional-scale indicators for the assessment of benefits provided by lakes for
multiple beneficiaries, like boaters and swimmers. Recent research using data from the National
Lakes Assessments (Angradi et al., 2018) showed that the water clarity attribute of lakes varied
among regions (Figure 7). By using thresholds derived from replicate subjective perceptions of
benefit quality, the percent of the lake resource providing each level of benefit could be
estimated (Figure 7, inset). Applying these thresholds to future biophysical assessments could
provide insight to changes over time in the quality of the lake benefit for contact water users
(e.g., swimmers, divers). For the specific attribute of lake currents, which are relevant for the
safe enjoyment of the resource by swimmers, there are local or larger scale sources of
information on beach hazards. The National Weather Service provide a daily forecast of swim
risk for the beaches of the Great Lakes (https://www.weather.gov/greatlakes/beachhazards); an
example is shown in Figure 8. Data compiled from these daily forecasts could be used to
estimate change in the swimming benefit over time in response to climate change or other
drivers.
A final example visualization not related to anglers is provided in Figure 9, which shows wild
rice harvesting licenses sold. The actual FEGS is wild rice area, but those data are not readily
available. The sales of licenses is a surrogate that reflects a related human activity.
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Mountains
Mountains 95% CI
Plains
Plains 95% CI
Exceptional
Marginal
10 20 30 40 50
Percent of lakes (±95% CI)
0 2 4 6 8 10 12
Secchi depth (m)
Figure 7. Regional estimates of Secchi depth by ecoregion (2012 National Lakes Assessment).
Main plot shows the cumulative distribution function for each ecoregion. Values on the vertical axis are the percent of
lakes with less than or equal the Secchi depth value on the horizontal axis. This is a continuous metric. The inset plot
shows the percent of lakes in each swimming benefit quality class based on thresholds derived by Angradi et al.
(2018), a categorical metric.
Thunder
Sudbury
Ottawa
Wisconsin
linrieapolis
Barrle
Peterborough
Toronto
Utica
Madison
Lansing
Rockford
Scranton
lleadvHIe
Pennsylvania:.
Fort Wayne
Pittsburgh
Hatrisburg ' Leaflet
Beach Forecasts | Incident Database & Stats j| Beach Safety Great Lakes Water Quality | Additional Information
Swim Risk
(Hover for definitions)
Q"| Low
P] Moderate
~ High
~ Beach Hazards Statement in Effect
! © OpenStreetMapy
Figure 8. Great Lakes Beach Hazard forecast for September 8, 2020.
Swim risk is based on predicted measured wave height and current strength; this is a categorical metric. This
resource also provides information on harmful algae blooms in the Great Lakes (Great Lakes Water Quality tab),
which is a metric for the attribute of pathogens in water.
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Legend
2006 Licenses
~~ 1-3
| 4 - 10
| 11-22
| 23 - 38
I 39 - 77
0 35 70 140 210 280
H H I 1 I I Kilometers
Figure 9. Wild rice harvesting license sales by zipcode combining 2005 and 2006 for Minnesota.
Source: Wild rice area is a measure of a FEGS for wild rice harvesters. In contrast, this is a map of license sales, a
human activity, which serves as a surrogate for a FEGS. This metric is a categorical variable mapped by zipcode for
a region. Source: (Drewes & Silbernagel, 2004)
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3.4 Rivers and Streams
David Peck, U.S. EPA Pacific Ecological Systems Division
Photo: Rivers and streams are important natural capital. The health of the river can impact beneficiary-specific
ecosystem services, like recreational anglers or swimmers. Photo credit: EPA Flickr site.
Rivers and streams are vital ecosystems in the United States, which has over 3.5 million miles of
rivers and streams with varying uses and conditions (https://www.rivers.gov/waterfacts.php).
These ecosystems provide a habitat for fish and other aquatic life. Associated habitats provide
necessary food and shelter for many non-aquatic species. Rivers and streams connect major
water resources from melting snow high in mountains to estuaries meeting the ocean. Rivers and
streams are important for human life by providing essential drinking water, while their
commercial uses are geared towards hydropower, irrigation, navigation, industry, and waste
removal, with upwards of 750,000 miles of rivers are behind dams to produce hydroelectricity
and other commercial goods and services (https://www.rivers.gov/waterfacts.php). The economic
and cultural benefits from the recreation and enjoyment of flowing water ecosystems further
justify the protection of these ecosystems.
Step 1. Ecosystem Delineation
Rivers and streams are bodies of water which flow from higher to lower elevations. Some rivers
and streams flow partially or entirely below the ground. Some are a tiny trickle, and others are
more than a mile wide. Rivers and streams can be classified by their size based on the upstream
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drainage area (i.e., the area where precipitation runs off into streams, rivers, or lakes). For
example a stream with a drainage area between 10 and 100 km2 (39 and 390 mi2) is classified as
a small river (Wang et al., 2011). Alternatively, size and accessibility can be categorized based
on "stream order," which is based on how smaller streams join to create larger streams and
eventually rivers. Headwater streams (i.e., with no upstream tributaries) are categorized as first
order streams; when two first-order streams come together, they create a second-order stream,
and so on. Wadable streams are typically first through third order (but in more arid regions,
higher order streams may also be wadeable). Rivers are typically sixth-order or greater. Due to
the changes in stream order from streams to rivers, there are often clear differences between the
biological communities that inhabit each.
Throughout history, many of the rivers and streams in the United States have been channelized
or impounded behind dams. These modifications are done to facilitate navigation, reduce flood
risk, create power, or allow development of the adjacent land. Rivers are also subject to seasonal
changes based on climate and weather. Natural events and cycles of erosion take their toll on
rivers, changing the landscapes of many of the rivers we see today. Some of the largest rivers in
the United States are critical to the economy, transportation of goods, natural beauty, and energy
production from hydroelectric dams. The Mississippi River is the largest river in the United
States based on discharge, with a drainage area of almost 1.2 million square miles. Other notable
rivers such as the Missouri, Delaware, Columbia, and Colorado Rivers stretch thousands of miles
throughout the United States. Their cultural contributions are not to be underestimated, as many
of the rivers were once extensively used as transportation corridors for moving goods and
connecting civilizations.
Streams are typically wadable but not navigable. Most streams are tributaries to rivers. Many
small streams are also seasonal, flowing only during wet periods or following storms. About
90% of the perennial (constant flowing) streams and rivers in the United States are non-
navigable. Urban streams receive much of their flow from runoff from impervious surfaces (e.g.,
pavement).
Step 2. Beneficiary Specification
The rivers and streams team selected five beneficiaries from the NESCS Plus classes that directly
benefit or profit from rivers and streams (Table 9). These beneficiaries were selected based on
their relevance and the availability of data.
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Table 9. Available FEGS Metrics for Beneficiaries of Rivers and Streams
1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
CO
o
CD
Water quality
Temperature (Long-term
measurements of water
temperature; Predicted
annual and seasonal water
temperatures)
NARS; StreamCat
No
No
ro
a>
c
a>
CD
O)
a;
c
CD
CD
"o
O
o
o
4=
o
CD
CD
O
E
Water
pH; Calcium hardness;
Alkalinity; Water temperature;
Total dissolved solids;
Chloride; Sulfate; Alkalinity;
Water temperature
NARS
No
No
CC
.>
CD
_£=
1—
Water quantity
Mean Annual Flow
NARS; StreamCat
No
No
Q_
Fauna
Fauna community
Density/abundance of
fouling/nuisance/invasive
organisms (e.g., Asian clam,
zebra mussels)
NARS
No
No
Contaminant concentrations
(mg/L)
NARS
No
No
CO
CD
C
o
CD
Q_
O
0)
C
O
Q_
O
Water
Water quality
Concentrations of harmful
bacteria (e.g., enterococci, E.
coli); Presence and/or
concentration of
cyanobacteria in water
(cells/mL)
NARS
No
No
~c
CD
"a
"c0
CD
or
£=
O
a>
>
or
Composite
Extreme events
Annual risk of flooding GIS
layer with elevations and
floodplain delineations; FEMA
maps, risks of flooding
NARS; EnviroAtlas
No
Yes
Site Appeal
Observations of presence or
extent of surface films or
odors in water
NARS
No
No
People Who
Care
Existence
values
Fauna
Fauna community
Taxonomic count data with
autecological and tolerance
assignments / 0/E scores /
species richness of
macroinvertebrate community
NARS
No
Yes
CO
0)
>
Chemical contaminant
concentrations in water
(mg/L)
NARS
No
No
Q
~U
c
03
co"
CD
CO
0)
E
Water
Water quality
Biological contaminant
Cyanobacteria concentrations
in water (cells/mL)
NARS
No
No
E
E
"S
CO
E
CO
Water clarity (Turbidity
(NTU); color (PCU)
NARS
No
No
co"
Water temperature (F/C)
NARS
No
No
~U
i
Water quantity
Water depth measurements
(m or cm); either cross-
sectional or thalweg
NARS
No
No
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Chemicals contaminant
concentrations in water
NARS; EMAP
No
No
0)
(/>
03
CD
Water
Water quality
Biological contaminant
(Enterococci / cyanobacteria /
microcystin and/or
cylindrospermopsin
concentrations in water
NARS
No
No
~U
C
03
Water clarity Turbidity (NTU);
color (PCU)
NARS
No
No
_o
"cc
O
Fauna
Fauna community
Presence, richness, and
abundance of desirable fish
species/Number of "trophy"
game fish or other
charismatic species
NARS
No
No
03
C
O
"cc
O)
b
CD
£
Composite
Site appeal
Measurement data on overall
rating for site appeal
NARS
No
No
Chemical contaminant
concentrations in water
NARS; EMAP
No
No
_CD
CD
C
<
Water
Water quality
Biological contaminants
Enterococci / Cyanobacteria /
Microcystin and/or
cylindrospermopsin
concentrations in water
NARS
No
No
03
CD
~U
C
03
.£=
Collection data with edible
fish species and relative
abundance
NARS
No
No
03
O
Fauna
Edible fauna
Relative abundance (or
biomass) of edible fish
species
NARS
No
No
Presence and severity of fish
anomalies, contaminate
levels in fish tissue (ng/g wet
wt), potential risk from
consuming
NARS,
State/Federal fish
consumption
advisory
databases
No
No
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Chemical contaminant
concentrations in water
NARS; EMAP
No
No
cn
CD
O
Water
Water quality
Biological contaminants
Enterococci / Cyanobacteria /
Microcystin and/or
cylindrospermopsin
concentrations in water
NARS
No
No
_a>
"oo
"cn
_Q
=3
CO
_a>
"oo
"cn
_Q
=3
C/^
Collection data with edible
fish species and relative
abundance
NARS
No
No
o
o
Ll_
_a>
CD
C
<
Fauna
Edible fauna
Relative abundance (or
biomass) of edible fish
species
NARS
No
No
Presence and severity of fish
anomalies, contaminate
levels in fish tissue (ng/g wet
wt), potential risk from
consuming
NARS,
State/Federal fish
consumption
advisory
databases
No
No
Step 3. Attribute Specification
For rivers and streams, we selected thermoelectric cooling energy plants as the beneficiary
example. These power producers draw water from rivers and streams to cool equipment.
Consequently, for this beneficiary, the water itself is the primary attribute of interest.
Specifically, two subattributes are important to a cooling plant operator: the amount of water
available (water quantity subattribute) and water temperature (water quality sub attribute). For
example, colder water temperature has higher capacity to cool equipment. A second attribute
valued by the cooling plant beneficiary is water free of fouling invasive species. This attribute is
represented by the fauna attribute category and specified as the fauna community subcategory.
Together, these attributes influence plant operations, including profitability and performance.
Step 4. Metric Specification
The candidate metrics were chosen to represent biophysical metrics that are amenable to regional
and national-scale assessments, although many of these could be adapted for use at more local
scales. The ideal metric for thermoelectric power generators would be an index of the risk of
ambient river water damage to piping because of poor water quality and/or from biofouling
organisms. This index would be a specified composite of water chemistry metrics that would
indicate if a power generator would be able to use the water from the specific river or stream.
Available data to understand the water chemistry from state and regional sources includes water
pH, hardness, alkalinity, water temperature, total dissolved solids, chloride, and sulfate; relevant
biological data would focus on presence/density of biofouling organisms. The data that can be
used as metrics to make a power generator water quality index are available and would need
topic experts to create the ideal index to provide an indication of risk.
There are some barriers to the creation of such an index for regional and national metrics. The
first would be the inherent differences between the water qualities around the United States and
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the needs of thermoelectric power generators. The different pH, salinity, dissolved oxygen, and
other water chemistry measurements in combination could give a large matrix of complicated
issues when trying to identify non-corrosive waters. It may be more feasible to try to get a
regional index to similar waterbodies, as expert evaluation is needed to move forward in
understanding the potential need and flexibility of an index.
Step 5. Data Sources and Availability
The water chemistry data are available to create a spatially explicit representation of a water
quality index used for corrosivity of water in thermoelectric power generator decision making.
However, expert and local advice on this measure is needed to create a robust and useful index.
The idea behind making a spatially explicit representation would allow for a visualization of the
areas of lowest risk to power generation facilities. The survey design for the NARS assessments
allows one to estimate the length of streams and rivers in a defined state based on the particular
indicator (e.g., the length of rivers that pose low risk of corrosion or scaling), and these estimates
can be produced at various scales or for specific components of the river and stream length (e.g.,
for rivers larger than 5th order, or for a geographic region).
A general constraint of using the NARS data for metrics associated with rivers and streams is the
synoptic component of the survey design and the density of sampling sites. Data are collected
once from each site during a defined index period (generally the period where flow is stable).
Temporally, there could be issues with understanding the entirety of a season in an area from the
few NARS data point collections. Metrics or indicators that rely on more intensive sampling at a
site are not amenable to assessment (e.g., increased sediment and fertilizer load in the rivers by
snow melt and runoff during rainy/wet seasons). While the density of the NARS sampling sites is
low, there are approaches that can be used to develop spatial models to predict condition for a
specific metric or indicator for river and stream length that is not sampled (e.g., Thornbrugh et
al., 2018).
Example Visualizations for FEGS Metrics in Rivers and Streams
The survey design for the National Rivers and Streams Assessment component of NARS allows
for inferences to be made from the set of sampled sites to a much larger population (in the same
manner as public opinion polls). The population in this case is defined as the total length of all
streams and rivers that had flowing water in them when they were sampled. Assessment
questions that are appropriate for the National Rivers and Streams Assessment are of the general
form: "What is the total length of stream that is in acceptable (or unacceptable) condition to a
specific beneficiary based on a given FEGS metric?"
For a benficiary interested in river and stream water an an acceptable source of cooling water,
the metric might represent a combination of water quantity, water quality (in terms of
temperature and corrosion or scaling potential), and the potential for biofouling, and the
assessment question could be phrased as: "What is the length of the river and stream network
that has sufficient water quantity, acceptable temperature to provide adequate cooling, and a low
risk of scaling, corrosion, or biofouling?" As this metric has not been developed yet, we provide
a visualization example for another potential beneficiary, but the general presentation would be
similar for a metric associated with the availability and quality of cooling water, or any other
metric with a categorical representation.
A simpler assessment question is possible for a non-use beneficiary. The example metric is an
index of biological condition of the benthic invertebrate community that is found on the bottom
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of rivers and streams. The structure (in terms of what species of organisms are present) and the
composition (how individuals are allocated among the species present) of this community are
affected by vari ous types of human disturbance. The assessment question in this example is:
"What is the length of the river and stream network that has benthic invertebrate communities
that are similar to what is expected in rivers and streams that have the lowest intensity of human
disturbance?" This metric is a biophysical quantity that is a reasonable representation of the
concept of biotic integrity that matters directly to an existence beneficiary (e.g., Johnston,
Segerson, Schultz, Besedin, & Ramachandran, 2011).
Figure 10 is an example graphic that addresses this assessment question, and can be modified to
address the general form of the question. The left panel presents stream/river length estimates in
terms of the percent of total length, while the right panel presents the actual estimated stream
lengths. The bars represent various classes of condition. In this example, "Good" means the
index values are similar to values expected in least-disturbed sites. "Poor" means the index
values are not similar to the values observed in least-disturbed sites, and "Fair" means values are
somewhat similar to values observed in least-disturbed sites. Results are shown for different
regions of the country, but one can define other spatial domains, such as river basin, stream size
classes, or ownership. If a cooling water metric were available, one could produce a similar-
looking graphic where "Good" would represent conditions that are associated with adequate
water quantity, quality, and low risk of biofouling, and "Poor" would represent conditions that
are not conducive as a source of cooling water.
National Biological Condition
WSA Mega Regions*
SI West
I 1 Plains and Lowlands
Eastern Highlands
Plains and Lowlands
Condition Categories
Good
I I Fair
Poor
i—J Not Assessed
Figure 10. Stream biotic integrity graphed for major regions of the contiguous United States.
Biotic integrity is a measure of a FEGS for nonuse beneficiaries. This is a categorical representation of stream
reaches aggregated to major region; this same sort of representation could be applied to the cooling water example
with appropriate data. Source: (U.S. EPA, 2016b [National Rivers and Streams Assessment]).
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3.5 Wetlands
Amanda M. Nahlik, U.S. EPA Pacific Ecological Systems Division
Photo: Wetlands are a diverse and everchanging ecosystem that vary across the nation. The cypress swamp pictured
above is more typical of the Gulf Coast than where it is actually located - in southern Illinois, the northern most range
for this type of wetland ecosystem. Photo credit: (National Wetland Condition Assessment; U.S. EPA, 2016c).
Wetlands are the parts of the landscape that are transition zones from land to water (for at least
some of the year). Wetlands can occur along rivers, streams, and lakes or along natural
depressions and seeps. Three important features define wetlands: (1) water and wet-adapted plant
life; (2) soil conditions that feature evidence of prolonged saturation; and (3) presence of water at
or near the soil surface to support the first two features (National Wetland Condition
Assessment; U.S. EPA, 2016c). This vitally important ecosystem occupies 5-8% of the Earth's
land surface (U.S. EPA, 2016c) and provide important benefits to natural and human
communities. These ecosystems provide important intermediate ecosystem services, including
flood prevention, water filtration, and wildlife habitat, in addition to the final ecosystem services
presented here that are enjoyed by beneficiaries.
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Step 1. Ecosystem Delineation
Defining the boundaries of wetlands is an ongoing issue in the aquatic sciences as a whole. One
definition of wetlands often used as the standard for setting wetland boundaries is as follows:
" Wetlands are lands transitional between terrestrial and aquatic systems where
the water table is usually at or near the surface or the land is covered by shallow
water. For purposes of this classification wetlands must have one or more of the
following three attributes: (1) at least periodically, the land supports
predominantly hydrophytes; (2) the substrate is predominantly undrained hydric
soil; and (3) the substrate is non-soil and is saturated with water or covered by
shallow water at some time during the growing season of each year. " (Cowardin,
Carter, Golet, & LaRoe, 1979)
This definition, while specific to wetlands, also innately includes aquatic systems like perennial
streams and shallow estuaries. Farmed wetlands may be included in this definition if they were
converted to agricultural production prior to 1985 and still meet the specific hydrologic criteria
of a jurisdictional wetland; however, prior converted wetlands, which are wetlands converted to
agricultural land prior to 1985 but no longer meet the criteria of a jurisdictional wetland, are
excluded.
Step 2. Beneficiary Specification
The Wetland team selected five beneficiaries from the NESCS Plus classes (Table 10). These
beneficiaries were evaluated based on the potential for available data, particularly from the
National Wetland Condition Assessment.
Table 10. Available FEGS Metrics for Beneficiaries of Wetlands
1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Farmers
Cranberry
Farmers
Water
Water quantity
—
—
Yes (ideal
metric)
Yes (ideal
metric)
Soil/Substrate
Soil quality
Soil pH, soil type, percent
sand
2011 NWCA
No
No
People Who Care
Existence values
Composite
Ecological
condition
Vegetation multimetric
index or condition class
2011 NWCA
No
Perhaps
(using
WetCat)
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Boaters
Kayakers, SUPs, and boaters
Water
Water quantity
—
—
Yes (ideal
metric)
No
Surface water depth
2011 NWCA
No
No
Water quality
Levels of harmful bacteria;
levels of chemical
contamination
2011 NWCA; EPA
sources
No
No
Composite
Site appeal
—
—
No
No
Hunters
Waterfowl hunters
Fauna
Edible fauna
Waterfowl abundance
(State-based catch rates
(proxy))
State-based
wildlife agency;
USFWS
No
No
Biological and chemical
contaminates in meat
(Levels of harmful bacteria;
levels of chemical
contamination)
2011 NWCA; EPA
sources
No
No
Composite
Site appeal
—
—
No
No
Food and Medicine
Subsisters
Native American
Medicine Subsisters
Flora
Medicinal flora
Plant species
composition,plant species
richness, plant mean
relative cover
2011 NWCA;
Native American
Ethnobotany
Database
No
No
Water
Water quality
Levels of harmful bacteria;
levels of chemical
contamination in water and
plants
2011 NWCA; EPA
sources
No
No
Step 3. Attribute Specification
For wetland systems, we selected cranberry farmers as the beneficiary example to illustrate the
FEGS Framework. We selected general attributes from the hierarchical standardized categories
listed in Table 4. For this beneficiary, we were parsimonious in selecting main attributes that
matter to the farmer. The selected attributes, described in the following paragraphs, influence the
geographic location and method by which cranberries may be grown and harvested in addition to
the resulting quantity of cranberries that may be grown and harvested.
Flooding is conducted in wetland-soil cranberry bogs for pest management (without the use of
chemicals), frost protection, irrigation, and wet harvesting. On the other hand, water is often
shunted from cranberry bogs in the late winter to promote plant budding (Sandler &
DeMoranville, 2008). Without a proximal water source that the farmer can withdraw water from
or deposit water into, cranberries cannot be farmed, and water quantity, measured by the
availability of water during the growing and harvest seasons, is important to cranberry farmers
because cranberry vines are sensitive to drought. Therefore, a cranberry farmer cares about water
first, specifically water quantity, which is listed in the subattribute column.
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Soil type, the amount of sand in the soil, and soil pH of a cranberry bog are all critical
components for successful cranberry growth. Thus, in addition to water quantity, the cranberry
farmer cares about soil quality, which is a subattribute of the soil attribute category.
Step 4. Metric Specification
Metric specification was conducted for five beneficiaries: farmers; people who care (existence
values); boaters; hunters; and food and medicine subsisters. A similar approach to developing
metrics was conducted for all beneficiaries by (1) developing hypotheses regarding the attributes
the beneficiary interacts with, utilizes, or cares about from the environment (based on secondary
research, i.e., literature searches) and (2) determining whether data to support the posited
attributes exist. In the following examples, we will focus on farmers, hunters, and people who
care (existence values) to discuss this process.
Example 1: Famers. For the cranberry farmer, soil metrics can be developed that adequately
capture the physical attributes that the beneficiary cares about from the environment;
specifcially, soil type, pH, and percent sand. Most of the data needed to represent these metrics
exist in either spatial (i.e., geographic information system [GIS]) datasets or from the 2011
NWCA data (U.S. EPA, 2016c). There may even be supplemental data available for soil metrics
from the National Resource Conservation Service (SSURGO [Soil Survey Geographic
Database]; NRCS, 2020) if the NWCA data does not have sufficient coverage. Conversely, for
water quantity, lack of data is a major reason metrics could be developed for this beneficiary;
indeed, for most FEGS (or attributes) and most beneficiaries, data do not exist to do anything
beyond proposing a hypothetical, ideal metric.
Example 2: Hunters. Waterfowl hunting is a popular activity in wetlands, and entire
organizations, such as Ducks Unlimited, exist to protect wetlands and waterfowl populations for
hunting. The metrics that were developed adequately capture the attributes that waterfowl
hunters care about from the environment. However, unlike the soil metrics developed for the
cranberry farmer, which both exist and are ideal, the metrics for populations of animals, like
waterfowl, do not exist in ideal form for reporting on a spatial scale that would benefit hunters.
Migratory waterfowl population surveys are conducted for some species and in some states by
the U.S. Fish and Wildlife Service (e.g., U.S. Fish and Wildlife Service, 2016) and provide status
reports on waterfowl populations. However, this information does not provide hunters with
information of where to go to hunt specific types of waterfowl. National-scale wetland surveys,
such as the NWCA, could provide the spatial information that beneficiaries need, but are limited
to a one-day field visit. Furthermore, adding data collection that requires extensive surveying
time, such as wildlife surveys, cannot be supported by the NWCA survey.
Example 3: People Who Care (Existence Values). While there may be many different metrics for
existence values (e.g., number of threatened, rare, and endangered species; number of
charismatic species; percent area of rare or critical habitat type), we hypothesized that ecological
condition broadly captures the attribute of wetlands that people care about. In the NWCA, a
vegetation multimetric index (VMMI) was used to indicate ecological condition (Magee,
Blocksom, & Fennessy, 2019; U.S. EPA, 2016d). The metrics used in the VMMI describe
characteristics of the collective vegetation community - not individual taxa.
Step 5. Data Sources and Availability
Wetlands tend to be studied at local and watershed scales, with only a few states having
conducted state-wide wetland surveys (e.g., Ohio, Minnesota). Until 2011, national-scale
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wetland datasets were limited to mapping efforts to report on extent of wetland area; thus there
are limited options for publicly available wetland datasets. However, in 2011, the first National
Wetland Condition Assessment (NWCA) was conducted, one of the aquatic resources assessed
as part of the NARS. One of the challenges of using the NARS data is that the surveys (and data)
are designed for national or regional reporting. As such, NARS data are not as helpful for place-
specific metric development. So, for example, NARS data does not help determine where on the
wetland landscape cranberry farmers may best utilize FEGS. The NARS data can only tell how
much area across regions or the nation have particular characteristics. Furthermore, combining
the coarse NARS data with finer resolution spatial data will be challenging when it comes to
indicator development.
Mapping wetlands is challenging, given their spatial and temporal variability and the fact that
some wetland types, such as forested wetlands, are nearly impossible to capture without ground-
truthing. Maps of high-resolution, consistently delineated wetland boundaries on a national
spatial scale do not exist. Wetlands have been identified as a land cover class using satellite data
at a 30m-pixel resolution for the National Land Cover Database (NLCD), although when it
comes to distinguishing wetland boundaries from other ecosystems, the algorithms used to do so
from this satellite imagery provide very different results than other sources, such as the National
Wetland Inventory. In contrast, the U.S. Fish and Wildlife Service's (FWS) Status and Trends
project has established approximately 5,000 permanent, 4-square-mile plots to identify wetlands
using aerial imagery so that changes in wetland area may be monitored over time. While the
aerial imagery for these plots is ground-truthed, these plots do not provide coast-to-coast
coverage of the United States and are nonexistent in some areas because plots are allocated in
proportion to the amount of wetland acreage expected to occur in each state (U.S. FWS, 2017).
Wetlands sites surveyed as part of the 2011 NWCA, from which data were used for this exercise,
were selected based on a design frame that used the FWS Status and Trends plots (U.S. EPA,
2016c [National Wetland Condition Assessment]).
Example Visualizations for FEGS Metrics in Wetlands
The example metric shown is for the non-use beneficiary, and is the VMMI described above.
While every sampled wetland site is assigned a VMMI score between 0 and 100, that continuous
information is not easily interpreted. Instead, "good", "fair", and "poor" thresholds were applied
by region and wetland type to describe ecological condition of wetlands across the United States.
These are shown in Figure 11.
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Vegetation Multimetric Index (VMMI)
Area (ha)
12,139,920 ±726,986
4,928,944 ± 640,296
8,084,386 ± 692,292
,257,727 ± 559,346
2,679,350 ±450,107
3,564,766 ± 442,004
g° "11111 'I III IIIII11111 III 111111IIII11111111111 ' 48-3%
h , iggjd
349 '32 1 %
Condition
Good
~ Fair
Poor
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiim
21.4%
28.5%
50.1%
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3.6 Agricultural Systems
Timothy Ccinfield and Kimberly Schuerger
U.S. EPA Groundwater Characterization and Remediation Division
Photo: This picture of an old barn is quintessential Americana and this viewscape is often prized in our culture and
arts. Agriculture landscapes like this one combine elements of human capital and labor and contributions from nature,
which are challenging to untangle from the FEGS perspective because the line between nature's contribution and
human inputs is hard to determine in some cases. Photo credit: EPA Flickr site.
Agriculture makes up one of the largest land-uses in the United States. Grasslands and pasture
used for livestock grazing account for 29% of the total U.S. land, and croplands account for 17%
(Bigelow, 2017; Bigelow & Borchers, 2017). In total, 1,047 billion acres (46%) of the nation's
area is used for agricultural production (Bigelow & Borchers, 2017). Together, agriculture, food,
and related industries added more than one trillion dollars to the U.S. Gross Domestic Product in
2017, more than 5% of the nation's total output (USD A, 2020a).
Agriculture systems are an important part of the nation's cultural landscape, cultural history, and
remain an important part of the country's cultural heritage. The challenge for this ecosystem
within the FEGS framework is that embedded within the agricultural landscape are products of
both nature and human labor and capital. Farmland, as depicted in the photo at the beginning of
this section, includes natural elements - the soil, climate, wild animals - but also the
romanticized old barn, and likely fertilizer on the grasslands. The barn and the whole vista are
deeply valued, but the distinction between the natural and built environment is difficult to
separate.
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Step 1. Ecosystem Delineation
The NESCS Plus defines agroecosystems as those lands that include orchards, vineyards, row
crops, tree farms (e.g., Christmas tree farms or short-rotation woody plantations), and
pasture/rangelands for livestock (Newcomer-Johnson et al., 2020). Within this framework, we
considered many types of farmers and farmlands when we hypothesized the metrics for
beneficiaries. There is no single dataset or spatial sampling frame that captures agricultural
systems, though USGS Landsat data does classify agriculture as one of its land covers (NLCD;
Multi-Resolution Land Characteristics Consortium, 2001).
Step 2. Beneficiary Specification
The Agricultural Systems team selected seven beneficiaries from the NESCS Plus classes
(Table 11). Farmers were separated into those whose crop is dependent upon pollination (e.g.,
apples) and those who plant wind-pollinated crops (e.g., row crops, like corn).
Table 11. Available FEGS Metrics for Beneficiaries of Agricultural Systems
1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Farmers
Non-pollinator
Dependent Crop Farmer
Soil/Substrate
Soil quality
Generic Productivity Index
USDA&MSU
Yes
Yes
Pollinator-dependent
Crop Farmer
Fauna
Pollinating fauna
Population of wild bees
National Websites
Yes
Yes
Residential
Property Owners
Farmland property
owners
Water
Water quality
Contamination of water
State Websites
No
No
Water quantity
Groundwater surveys
State Websites
No
No
Composite
Site appeal
Natural Amenities Index
USDA
Yes
Yes
Extreme events
Risk of flooding (Flood Maps
for 100 year flood risk)
FEMA
Yes
Yes
Atmosphere
Air quality
Air monitoring data
State Websites
No
No
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Deer density status and
trends monitoring
State and National
Websites
No
No
Summary of Illinois Deer
Seasons
State and National
Websites
No
No
CO
a>
Range-wide status of Mule
Deer and Black Tailed Deer.
State and National
Websites
No
No
c
=3
X
Fauna
Edible fauna
Deer density United States,
2008
State and National
Websites
No
No
CD
Q
Deer Harvest Reports by
Season
State Websites
No
No
Chronic Wasting Disease
Surveillance
State Websites
No
No
Levels of chemical
contaminants in population
National Websites
Yes
Yes
Population reports for
abundancy information for
hunters.
State Websites
No
No
CC
C
o
Sex ratios of harvested
waterfowl to determine if
male or female is preferred.
National Websites
No
No
b
CD
CO
io
C
=3
CO
is
~ci
=3
X
Fauna
Edible fauna
Migratory bird hunting
activity and harvest during
the 2014-15 hunting
seasons.
National Websites
No
Yes
_a>
"cc
g
Preferred season dates from
hunter survey and activity
based off of time of year.
Local Websites
No
Yes
Waterfowl harvest reports by
season (surrogate)
National, state
Websites
Yes
Yes
Levels of chemical or
biological contaminants in
population
National Websites
Yes
Yes
Reported cases of affected
waterfowl
National Websites
Yes
Yes
CO
a>
Small game population
State and National
Websites
No
No
d
=3
X
CD
E
03
CD
"ro
Fauna
Edible fauna
Harvest Data
State and National
Websites
No
No
Presence/Albescence of
Pathogens and Parasites
National Websites
Yes
Yes
E
CO
Distribution of Small Game
Habitat
State Websites
Yes
Yes
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Agricultural Landscape (Learning)
Educators/
Researchers
Water
Water quality
Nutrient levels in surrounding
streams and groundwater
National Websites
Yes
Yes
Water quality readings in the
surrounding area (regional)
National Websites
Yes
Yes
Water quality current
conditions
National Websites
Yes
Yes
Soil/Substrate
Soil quality
Soil contaminants
National Websites
Yes
Yes
Soil quality surveys
National Websites
Yes
Yes
Fauna
Pollinators
Wild bee abundance
(Koh et al., 2016)
Yes
Yes
Spiritually/culturally
important fauna
Monarch butterfly monitoring
National Websites
No
Yes
Fauna community
Invasive Species Presence
National Websites
No
Yes
Step 3. Attribute Specification
For farmers, soil is the primary attribute that influences all other decisions a farmer may make.
Soil quality is a listed subattribute of the soil category. Carbon and nutrient rich soil may provide
most, if not all, of the foundational base for farming activities. Farmers that grow pollinator
dependent crops (i.e. apples) also care about wild pollinators, which we represent as pollinating
fauna sub-attribute of the more general fauna attribute column.
In addition to the soil, water is the life blood of farming. So farmland owners very much care
about water attributes of quantity and quality. Water for irrigation, from a well, pond, or river, is
essential for many crops and water salinity can impact soils and irrigation equipment, an
example of a water quality attribute. Many farmers also enjoy the aesthetics and sense of place of
farms, a composite attribute for site appeal sub-attribute. A potential negative composite attribute
is extreme weather events, like flooding or droughts, which farmers often contend with from year
to year.
Step 4. Metric Specification
Unlike other ecosystems considered in this report, agricultural ecosystems do not have a single,
primary data source or nationwide sampling effort that describe ecosystem status, like NARS for
aquatic ecosystems or FIA for forests (Gray, Brandeis, Shaw, McWilliams, & Miles, 2012;
Oswalt, Smith, Miles, & Pugh, 2014; Oswalt et al., 2019). There are databases such as the
Census of Agriculture (USDA, 2020b) but that database "looks at land use and ownership,
operator characteristics, production practices, income and expenditures" so it contains a wealth
of important data but it mainly describes human activity that depends on ecosystems - thus it
contains surrogate data rather than FEGS data or metrics. Agricultural systems are highly local,
and most available data are at the state or county level, often lead by a land-grant university or
local NRCS office. Consequently, the ability to scale FEGS metrics from local to regional and
national scales is limited. The USGS' Landsat land classification system includes agriculture as
one its land use and land cover categories (NLCD; Multi-Resolution Land Characteristics
Consortium, 2001), though this is coarsely categorized.
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Step 5. Data Sources and Availability
The biggest barrier or constraint is that most of the rendered data is not kept in the public
domain. While raw data may be collected by state agencies, the use of these data seem to be
restricted to those beneficiary group organizations that put time and money into rendering the
data, and thus these data are predominantly available through a subscription service where there
is an associated access cost. This makes it difficult to develop a publicly accessible FEGS listing
at the local to state scale, let alone a national scale. Some organizations, such as the Quality Deer
Management Association, put out an annual report that details quite a bit of information. While
this report is searchable online, it contains a fee-based subscription component that provides
access to additional information.
Since most of these data are collected at the local and state level, there tends to be a lack of
standardization on data collection, including how collected, recorded, and reported. This lack of
standardization creates a logistical issue when trying to pull these data together from different
states, including ensuring data are all measuring and representing the same thing. Ideally, by
harmonizing and standardizing the way these data are collected at the local and state scale, these
metrics could be easily applied at multiple scales depending on how the data are aggregated to
address scale-dependent questions.
Example Visualizations for FEGS Metrics in Agricultural Systems
Much of the data that are gathered is rendered in tabular format that allows full presentation of
the numbers but makes analysis of those numbers time consuming, since these data must be
transcribed into a spreadsheet before analysis can begin. Having a standard format that the data
are collected and recorded into a spreadsheet that could be combined with other data from
different states or regions would help facilitate the development of a regional or national
comparison. While it is important to have spreadsheet numbers to make quantitative assessments
for whatever metric or parameter is being assessed, it is equally important to develop visuals of
these data rendered into a readily understandable format for the beneficiary to use with ease.
Data visualization is essential to transfer beneficiary useful information quickly and efficiently to
the end user. We present two figures as examples of visually translating FEGS metrics into
figures to improve social translation: (1) Soil Productivity Index (Figure 12); this map is an
example of a method to estimate soil quality for farmers; and (2) an example of a regional
estimate of deer density (Figure 13), the FEGS metric of choice for hunters. The use of GIS
capabilities are helping to render this information more readily as graphics, so that data such as
soil productivity or deer densities can be produced with relative ease. It will be essential to
provide these visual representations to facilitate information transfer to those resource managers
acting on behalf of the beneficiaries rapidly and accurately.
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Legend
Soil Productivity Index
_]o CDs [CI
¦' CZ
¦ ? Id]' _
3 HI
Figure 12. An example of a FEGS metric for soil productivity for farmers (Soil Productivity Index)
for Midwestern states.
The higher the soil productivity index, the more likely the soil will support greater crop harvest. This is a
representation of a categorical metric mapped at the county level for a major region of the United States. Source:
(Schaetzl, Krist Jr, & Miller, 2012)
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Low
Moderately low
Moderately high
High
Figure 13. Estimates of deer density across the United States, an example of a spatial
visualization of the FEGS metric for deer hunters.
Top: 1982 deer densities; Bottom: 2001-2005 deer densities in the contiguous United States. These are maps of
classes of deer density mapped at the county level for the contiguous United States, a categorical metric. Deer
density is a measure of a FEGS for Deer Hunters. Source: (Hanberry & Hanberry, 2020)
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3.7 Forests
Andrew Gray, USDA Forest Service
Photo: Forests are an important ecosystem in the United States and provide multiple ecosystem services. Standing
trees that may be used for building are an example of a FEGS metric for the timber manager beneficiary. Photo
credit: USDA Forest Service Flickr site.
For centuries, forests have provided a variety of renewable wood products to the nation, and land
that was converted to agriculture. Forests play a key role in providing dependable clean water,
which provides drinking water as well as supporting fresh-water aquatic ecosystems and
fisheries. Forests also modulate climate and can store (or release) substantial amounts of carbon
to or from the atmosphere. Forested lands also provide a range of non-timber products, including
edible, medicinal, and decorative plants and fungi; support wildlife populations important to
subsistence hunting and recreation; and occupy landscapes valued for their recreational
opportunities and aesthetic qualities. Some types of forest conditions, usually those with open
canopies and large trees, have been imbued with spiritual value as well. Forests can also threaten
human health and well-being by harboring dangerous animals and diseases or by carrying
wildfire into populated areas.
Step 1. Ecosystem Delineation
Forest ecosystems are generally described as areas that have some minimum amount of
occupancy by trees. Definitions vaiy in terms of what qualifies as a tree and what should be
defined as the minimum area and threshold for tree abundance. The international definition used
by most countries reporting to the United Nations' Global Forest Resource Assessment is land
areas of 0.5 ha or more with at least 10% cover of trees taller than 5 m, or where trees are able to
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reach these thresholds, and primarily under forest land use (Keenan et al., 2015). This includes
forested wetlands (e.g., swamps and mangroves), but excludes areas primarily under agricultural
or urban land use. This is the definition used by the FIA program, which is mandated to report on
the status and trends of forests in the nation (Gray et al., 2012). Forest ecosystems cover 310
million hectares in the United States, or 34% of the land area of the 50 states (Oswalt et al.,
2014).
Step 2. Beneficiary Specification
The Forest team selected six beneficiaries from the NESCS Plus classes that reflect a variety of
types of FEGS with a range of available data and complexity (Table 12).
Table 12. Available FEGS Metrics for Beneficiaries of Forests
1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Foresters
Timberland
owner/timber grower
Soil/Substrate
Soil Quality
Site class: estimated
potential wood production for
"normal" stand (m3/ha/yr)
FIA
Yes
Yes
Flora
Commercially
important taxa
Quantity of merchantable
volume (m3 or board-ft/ha)
FIA
Yes
Yes
Quality of merchantable
volume
FIA
No
No
Timber, Fiber,
and Ornamental
Extractors
Timber extractor
Flora
Commercially
important taxa
Volume - quantity of
merchantable volume (m3 or
board-ft/ha)
FIA
Yes
Yes
Quality of merchantable
volume
FIA
No
No
Residential Property
Owners
Home owner with
some trees living
next to forested area
Fauna
Pest fauna
Species lists of regional
wildlife
State wildlife
agencies
No
No
Composite
Extreme events
Forest fire - Modeled fire risk
given climatic fire regime
LANDFIRE program
Yes
Yes
Site appeal
—
—
No
No
People Who Care
- Existence
Existence values
Composite
Ecological
condition
Terrestrial condition
assessment scores (1 -5)
Cleland etal. (2017)
Yes
Yes
Food Pickers and
Gatherers (Recreational)
Recreational huckleberry
picker
Flora
Edible flora
Cover of huckleberry species
FIA
No
No
Cover of huckleberry species
in different forest conditions
FIA
No
No
Composite
Site appeal
No
No
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1
2
3
4
5
6
7
8
Beneficiary
Subclass
Specific
Beneficiary
Attribute
Category
Attribute
Subcategory
Available
FEGS Metric
Suggested
Source
Remotely
sensed?
Model
available?
Food and
Medicinal
Subsisters
Elk hunter
Fauna
Edible fauna
Estimates of game
populations for selected
areas
State game
department
population estimates
No
No
Occasional estimates of
game populations for
selected areas
State game
department
population estimates
No
No
Step 3. Attribute Specification
Of the six forest beneficiaries, we selected recreational berry pickers to illustrate the FEGS
methodology for attribute specification. We used the standardized hierarchical categories in
Table 4 to describe the FEGS metrics attributes. For berry pickers, they care about the flora
community, specifically the edible or commercially important floral species that are categorized
as subattributes of the flora community. Berry pickers also value the whole experience of berry
picking, the sights, sounds, smells, and tastes of the forest environment and its flora. This
attribute is described as a composite category in Table 4. The overall "gestalt" of the berry
picking is categorized as site appeal subattribute.
Step 4. Metric Specification
For the forester and commercial extractor beneficiaries, the FEGS metrics are concrete and there
are well-developed national monitoring systems available, primarily through the FIA program
(Gillespie, 1999). For the food gatherer and food subsister categories, the FEGS are concrete, but
the available information to estimate them is incomplete and fragmentary. For the residential
property owner and non-use categories, the FEGS are somewhat nebulous and the type and
amount of information available to estimate them is unclear. Some of the categories have several
FEGS which cross ecosystem boundaries. For example, fauna used for subsistence, for example
deer and elk, often rely on water, forest, and range ecosystems for their survival.
The ideal metric for the FEGS of huckleberries for recreational pickers is the abundance and
quality (taste) of huckleberries available in a particular area of interest. The available metric is
the cover of each species. This metric could be improved by collecting both FIA protocols
(extensive and intensive) on a subset of plots to estimate the amount of cover missed by using
the 3% cover minimum threshold on the standard plots. The metric could also be improved for
some species by collecting inventory or monitoring data on non-forest alpine or subalpine
ecosystems. While it would be impossible to visit every field plot when it is most likely to have
fruit, it might be possible to quantify fruit abundance when present in order to build habitat
production models to apply to the overall dataset and estimate fruit production (i.e., develop an
ecological production function). Quantifying fruit quality might be difficult given variation in
beneficiaries' tastes, but simple classifications of fruit might be feasible (e.g., plump, dry, seedy).
The example of huckleberries in forested environments is broadly analogous to a wide range of
FEGS related to forest plants. The FIA sampling approach currently can do an adequate job of
estimating the cover of species, but this is not usually the attribute of interest. For example,
commercial pickers of floral greens focus on plants that are in good condition (e.g, vigorous,
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fresh, without damage or other blemishes). It is possible that additional measurements or other
research can be applied to FIA measures of plant cover and height to provide modeled estimates
of FEGS abundance. Similarly, a better understanding of what is missed with the 3% cover
minimum would be useful. Species where individual plants tend to have a large growth habit
(e.g., many shrubs) are more likely to meet the 3% cover threshold than species with small
individuals (e.g., many forbs) or those that tend to be found in a dispersed rather than a clumped
pattern. The FIA methodology is unlikely to be modified in the near future, but the program is
responsive to user needs and tries to accommodate ancillary studies.
Step 5. Data Sources and Availability
Understory plant species data are currently collected by FIA only in the western states of the
United States (including Alaska and Hawaii). Prior to 2000, these data were collected on only
particular ownerships and states, and in some cases the quality may have been lower than
currently is the case (e.g., species identified only to genus). Since 2000, the data are more
consistent and comprehensive, with one tenth of the plots being measured each year in a spatially
balanced design with the intention of resampling plots on a 10-year interval. The detailed all-
species data were collected on a subset of plots in some years, with the most complete sampling
occurring in the north-eastern states of the U.S. from approximately 2001-2009. These data
could be useful for estimates at broader spatial domains for the northeast and could inform
estimates of the abundance of plants missed by the standard data collection on western plots.
One challenge of the FEGS perspective for forest ecosystems regarding forests is that many of
the ecosystem services that forests provide are intermediate services, such as water purification,
carbon sequestration, and wildlife habitat. For these intermediate services, it is important to be
clear and link them directly to the FEGS biophysical metric—water quality and quantity,
standing trees and wildlife populations, to illustrate the services listed above.
Example Visualizations for FEGS Metrics in Forests
Currently, there are no comprehensive data for huckleberry plants in non-forest alpine or tundra,
although some National Forests do apply the FIA measurements on non-forest vegetation types.
Figure 14 provides a visualization of such data for a narrower scope, the State of Washington.
For game and recreational species, whether consumptive (white-tail deer hunting) or non-
consumptive (bird watchers), the ideal metric would be estimates of the wild population.
However, population estimates are usually lacking and estimates for game species are commonly
derived from hunter-reported catch rates (Figures 15 and 16).
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oval-leaf blueberry
thinleaf huckleberry
Snowberry
red elderberry
salmonberry
thimbleberry
Oregon grape
oceanspray
salal
Cascara
snowbrush
kinnikinriick
Serviceberry
vine maple
¦H
0 200,000 400,000 600,000 800,000 1,000,000 1,200,000
Figure 14. Area of forestland (and standard error) covered by various plant species in Washington
state, 2006-2015, based on FIA data.
These species produce FEGS for different non-timber forest-product harvesters, like berry pickers. This is a surrogate
for the FEGS metric, cover of huckleberry species, for recreational huckleberry pickers (as well as for other food
pickers and gatherers). It is a represented as a continuous variable for a region of the United States.
T3
to
CD
>
0)
(D
-Q
6,000
5,000
4,000
3,000
E 2,000
3
1,000
Antlered elk
Antlerless elk
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Figure 15. Total antlered and antlerless elk harvested in Washington State (2001-2013).
Source: (WDFW, 2015). This is a surrogate for the FEGS for an elk hunter or for a food subsister. It is a surrogate
because a FEGS metric would be the number of elk in the wild, not the number captured. It is a represented as a
continuous variable for a region of the United States.
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Washington estimated elk population numbers
Land cover
WA_GMUs_2018
population
riNA
¦ 0-100
Figure 16. Population estimates for elk in Washington State based on models that relate habitat to
categories of elk abundance.
The challenge is these maps do not represent the FEGS metric - the elk - directly, and they demonstrate one of the
challenges of mobile organisms that do not obey ecosystem delineation. The representation is of a categorical
variable for each of a number of game management units for the state of WA Source: (Multi-Resolution Land
Characteristics Consortium, 2001)
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3.8 Cross-ecosystem Results Synthesis
We evaluated 45 beneficiaries for seven ecosystems (see Table 3), including both direct
consumptive users, non-consumptive direct users, and existence values for non-use beneficiaries.
Among the seven General Beneficiary Classes (see Table 3), ecosystem teams selected
recreational beneficiaries the most frequently (14 beneficiaries) and Learning beneficiaries the
least (1 beneficiary). The teams selected beneficiaries from the remaining beneficiary classes
(government, municipal, and residential; agricultural; commercial/industrial; non-use; and
subsistence) about equally (5-7 beneficiaries for each).
Among the eight General Attributes (see Table 4), the ecosystem teams identified three the most
frequently: fauna, water, and composite/extreme events. Within these attributes, the specific
attributes most frequently identified were edible fauna, water quality, and site appeal
respectively. This is not a list of general importance; for example, although diverse wild fungi
are important for subsistence and commercial beneficiaries in regions of the United States and
throughout the globe (Boa, 2004), no ecosystem team identified fungi as an attribute. Rather, this
is a reflection of the beneficiaries selected by the ecosystem teams and their understanding of
beneficiary preferences.
Ecosystem teams identified 200 metrics for these beneficiaries and attributes; typically 4 to 5
metrics per beneficiary. These are not 200 distinct metrics; for example, "Site Appeal" is
identified as an ideal metric three times and "Water Clarity" is identified as an ideal metric four
times.
The remainder of this section focuses on four features of our results: the availability of spatially
explicit data; the "appropriate" number of metrics per beneficiary; representation of ecosystems
for non-use (existence) beneficiaries; and the form(s) of a FEGS metric.
Availability of Spatially Explicit Data
Figure 17 shows the number of metrics identified for the seven General Attributes selected
(fungi are omitted as no teams selected fungi as an attribute) and how often the metrics were
available on an extensive and spatially explicit basis. Figure 17 demonstrates that FEGS cannot
simply be mapped. Of the 200 metrics identified, we found that many (about two-thirds) could
not be represented in a spatially explicit manner (e.g., in maps or used in quantitative spatially
explicit analyses such as economic analyses where the specification of local scarcity and
abundance is important). This finding is reinforced by Tashie and Ringold (2019), who found
that there is a paucity of spatially explicit data on FEGS that could be used to map FEGS overall.
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o
CuO
QJ
+->
ro
U
QJ
+->
_Q
Fauna
Water
45%
Composite &
Extreme Events
Soil/Substrate 29%
12%
88
%
32%
68%
Other Natural
Components
Atmosphere
I
Spatially Explicit
l Not Spatially Explicit
10 20 30 40 50
Number of Metrics
60
70
80
Figure 17. Number of metrics listed for each General Attribute for the 45 beneficiaries analyzed.
Note that some metrics were identified more than once.
Number of Metrics per Beneficiary
The ecosystem experts we convened at our workshops had a range of perspectives on how many
metrics are necessary or appropriate for each beneficiary, and we discussed this question at
length. Some felt that a beneficiary could only directly experience a single attribute or quality
represented by a single metric, and that additional metrics would reflect a different way that
people benefit from ecosystems. Others argued that multiple attributes and metrics are important
in the way people directly experience or perceive ecosystems. An example helps to illuminate
the point: consider a recreational angler; do they directly experience only something about the
fish? Or do they also benefit from other aspects of the experience? To the ecosystem experts who
support the one beneficiary, one metric perspective, the extent to which the angler also directly
experiences and makes decisions on fishing location on the basis of the appeal of a location
would make the angler function as two beneficiaries: an angler and a viewer. However, most of
the ecosystem teams supported the multi-attribute (and multi-metric) view of a beneficiary (i.e.,
the angler is one beneficiary with two metrics relating to two attributes, the fish and the view).
This perspective is consistent with mainstream consumer theory (e.g., Lancaster, 1966) and with
literature on beneficiary decision making. For example, Hunt (2005) and Morton et al. (1993)
show for recreational fishing and hunting, respectively, that those beneficiaries make decisions
on the basis of multiple factors, including the target organism and the appeal of the site. In the
end, we took this latter view, selecting 4-5 metrics on average per beneficiary. This result
suggests that communication of ecosystem status and benefits analysis should include multiple
metrics.
This question of the number of metrics per beneficiary can be one of deep philosophical interest.
A meaningful answer to the question is aided by a specific application rather than the general
national and regional charge that we have. For example, if you wanted to examine the benefits of
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a policy that would change landcover or land use, then metrics of site appeal could well be of
great importance for many beneficiaries, including recreational anglers and hunters. If you
wanted to examine a policy that would affect lake acidity, other metrics would be important,
including fish abundance for recreational anglers, and other attributes might be less relevant.
Finally, if you wanted to describe a resource that might be enjoyed or appreciated by a
recreational angler, you might want to combine metrics of recreational fish abundance and the
appeal of fishing sites (Ringold et al., 2013).
Representation of Ecosystems for Non-use Beneficiaries
All ecosystem teams except the one for agricultural systems identified a possible metric for non-
use existence value beneficiaries. In contrast to the other ecosystems considered, agricultural
systems are human systems embedded in natural systems, making it hard to identify an existence
or non-use value that reflects ecosystem (and not human) activity.
Generally, at the suggestion of the Steering Committee, we selected existence metrics that either
(1) represented the status of the biota with respect to an undisturbed reference condition, or (2)
focused on an assemblage of organisms for which there is minimal use value, to avoid confusion
with direct use metrics. For example, in rivers, the ecosystem team selected a multimetric index
of biotic integrity for invertebrates in the water, which is a measure of the biota with respect to
least disturbed conditions (Stoddard et al., 2006).2A similar index could be constructed for fish
or even birds (Bryce, 2006), but both assemblages are often associated with direct use and this
can confound the specification of a non-use value.
Form of FEGS Metrics
For metrics of non-use value and other metrics, discussions with members of our Steering
Committee also identified issues with respect to the form of the FEGS metric. They suggested
that for economic analysis, it is better for biophysical scientists to provide continuous data not
paired with a social translation (e.g., specification of good, fair, poor categories). In contrast,
many reports that focus on communicating the results of assessments of ecosystems using
indices of biotic integrity report the index in a classified form (e.g., good, fair, poor; most
disturbed, moderately disturbed, least disturbed). These include all NARS reports and many
others (e.g., Jimenez-Valencia, Kaufmann, Sattamini, Mugnai, & Baptista, 2014; Karr, 1991;
Llanso, V0lstad, Dauer, & Dew, 2009; Noble, Cowx, Goffaux, & Kestemont, 2007). This
suggests that the form of the index for communication may be different than for economic
analysis.
We conclude that the appropriate form depends on the specific application and user. Evidence of
these multiple forms is apparent in the ecosystem teams' metrics tables. Most of the entries listed
as ideal metrics are traditional biophysical measures (e.g., temperature, turbidity, contaminant
concentration), but a few, especially associated with site appeal or risk of disease or extreme
events, embody a greater level of social translation and suggest a metric in a classified form.
2 After our work had been completed, a series of focus groups compared ratios of observed taxa to expected taxa
(O/E indices) with multimetric indices to evaluate how well they resonate with people. The study concluded that
the O/E indices performed better than the multimetric ones (Hill et al., 2020). Had we known of this finding as
we developed this report, we would have used O/E indices instead.
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3.9 Challenges to Providing Data on FEGS
In this section, we present a synthetic discussion of the challenges in specifying FEGS and
providing FEGS data. We identified five core challenges.
1. We often do not collect attribute data that matters directly for FEGS beneficiaries.
For example, one key attribute not included in the wetlands survey is any measure of site
appeal, an attribute listed as being important to two recreational beneficiaries.
Comparable NARS surveys include a quantification of site appeal based on field crew
judgements. It would be a simple step that could be implemented with little additional
field crew burden to transfer the site appeal measure to the wetlands survey. However,
field crew quantification of site appeal is not a measure that can be easily used in a fully
linked set of models, because it has no quantified link to a biophysical feature that would
change in response to a change in policy. From this perspective, aesthetic measures
constructed from landscape features (Booth et al., 2017; Daniel, 2001; Frank, Fiirst,
Koschke, Witt, & Makeschin, 2013; Gobster & Westphal, 2004; Gregory & Davis, 1993;
Howley, 2011; Ribe, 1989, 2009) are much more desirable.
2. In cases where data was collected ostensibly relevant to a FEGS metric, attributes
are not collected that directly matter to a beneficiary. Using the NARS as an example,
barriers to closing these gaps range from minor (e.g., fish size) to insurmountable (e.g., a
full set of contaminants in fish and in the water). In fact, NARS surveys have added
additional information on fish size, which was absent in precursors (Stoddard et al.,
2005). Data on fish size has supported the development of a fishery index (Hughes et al.,
2021). In contrast, with tens of thousands of inorganic and organic contaminants that
could pose a risk to beneficiaries either through water contact or fish consumption,
priorities must be set (Hughes & Peck, 2008) to provide a reasonable representation. In
another example, the U.S. Forest Service collects understory vegetation estimates based
on area, including berry-producing plants. But a berry picker is most interested in the
quantity of berries that are produced from the forest vegetation, not the area of
production, an example of having the right attribute but wrong metric.
3. Temporal or spatial characteristics of the data do not provide information at the
scales a beneficiary or managers acting on behalf of a beneficiary would find useful.
Even when we are able to collect the right attributes in the right form, this barrier or gap
reflects three different facets of time and space: (1) the extent; (2) location, density, or
frequency of the data; and (3) the size of the unit being observed.
The first facet, the extent of our consideration, is the national and regional scale. The
absence of nationally consistent data is frequently evident in our analyses. For example,
41 of the metrics in the Appendix are listed as being of only local extent, another 5 are
listed as being only of state extent, and another 38 are listed as local observations that are
aggregated to regions, though with variable methodology that may not support consistent
national and regional reporting.
The second facet is the location of the data. Metrics of flooding are directly relevant to
many beneficiaries. However, until very recently, many riverside locations subject to
flooding were not identified. FEMA has produced flood maps for 61% of the contiguous
United States, providing coverage for about a third of the stream miles in the United
States. Areas not mapped tend to have limited development but include large areas in
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agricultural production. Reflecting the need for more extensive coverage, a recent
modeling effort provided maps of 100 year floods for the contiguous United States
(Woznicki, Baynes, Panlasigui, Mehaffey, & Neale, 2019). Similar modeling efforts
(e.g., Fox et al., 2016; Hill et al., 2017) are providing information for biological features
that are directly used, enjoyed, or appreciated by beneficiaries (e.g., for existence
beneficiaries in aquatic lakes and rivers). These spatial interpolation methods may be of
use for other metrics observed in probability surveys for which valuation and other
studies require data for locations that not sampled. The way flood probabilities are
expressed—typically as the probability of flooding over an extended period of time (e.g.,
100 years) or as the annual probability of flooding—also illustrates the need to pay
attention to the temporal characteristics of the way in which beneficiaries experience
ecosystems. For building owners, a flood at any time of the year can be devastating—thus
annual probabilities are meaningful. In contrast, for farmers, while floods at some times
of the year are devastating, floods at other times of the year may be beneficial (Dahlke,
Brown, Orloff, Putnam, & O'Geen, 2018), thus probabilities of flooding for different
seasons of the year may be a more meaningful representation.
The third facet is the unit of observation. National economic data may describe, for
example, the average household income; national public health data might describe the
percentage of adults that are obese. Here the household or adult individuals are the units
of observation. Biophysical scientists have similar units, acres of forest or wetland or
miles of streams. They have well developed methods for determining how to sample
these units for specific purposes. The question this leaves is what is the spatial unit that
directly matters to a beneficiary? Do beneficiaries directly experience the number,
shoreline length, area, or volume of lakes? This is an important issue that affects the way
in which a resource is presented and analyzed. To the extent that resources respond to
stressors as a function of their size, this can affect the way in which the magnitude of
response to a stressor is understood. For example, the percentage of acidic lakes (acid
neutralizing capacity <0) in the northeastern United States in 1984 was 2.5 times larger
when expressed in terms of the number of lakes than when expressed in terms of the area
of lakes (Linthurst et al., 1986). Repeated discussions in our workshops have led us to
conclude that the question of the spatial dimensions of ecological resources directly
experienced by beneficiaries is a topic that would benefit from more research.
4. There is a translation gap among scientists and between scientists and decision-
makers and the public. In some ways, this is the most fundamental gap in providing
FEGS. The need to translate information is a fundamental one (e.g., Nicholson et al.,
2009; Schiller et al., 2001) and is evident and illustrated in all the indicators developed in
this report, especially with regard to their form. The level of translation required for a
metric to serve best as a FEGS metric is also an issue that needs attention. In some
instances, the capacity to provide a translation is well developed. Regulatory analyses can
provide translations of biophysical quantities into units that may be more meaningful to
people. For example, EPA analyses of ozone translate technical units (e.g., the fourth-
highest daily maximum 8-hour concentration averaged across three consecutive years in
parts per million) into levels considered to be safe for sensitive individuals with an
adequate margin of safety. The 1996 Safe Drinking Water Act Amendments require EPA
to review National Primary Drinking Water Standards once every six years and set levels
protective of human health in drinking water. Here, agency standards translate technical
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units into levels that protect human health and that water systems can achieve using the
best available technology (https://www.epa.gov/dwreginfo/drinking-water-regulations).
To be clear, it is not that a regulation makes something a FEGS metric, but that a
regulatory process may provide the translation from something very technical to
something directly understandable. While such translations are important, additional
effort needs to be devoted to how best to describe what we presume to be the right
technical metric in a form that matters directly to people.
5. Sometimes we simply are not able to hypothesize the metrics that matter directly to
a beneficiary with any real level of comfort. The most difficult case here is for
existence values. We have hypothesized three sets of biophysical measures for existence
values (1) the existence of charismatic or iconic species and places; (2) a set of measures
of ecological condition, for example, multimetric indices of biotic integrity or
Observed/Expected indices often used in aquatic systems (Hawkins et al., 2000; Karr,
1981; Moss et al., 1987; Stoddard et al., 2008), particularly when the assemblage is one
that has minimal use value, such as for periphyton or for macroinvertebrates; and (3) all
the metrics for use values. This is an area that would benefit from additional research,
especially since existence values may be large (Boyd, 2018; Hewitt, 2018; Johnston,
2018). A state of science review is in development that should help to frame this issue
(Boyd et al., In prep).
4. Discussion
4.1 Application and Use of FEGS to Decision-makers
FEGS have several uses for Federal and state agencies and for decision-makers in general. First,
the focus on starting with beneficiaries and the provision of a full list of beneficiaries (in Table 2
here and in NESCS Plus) should help decision-makers ensure that their analyses consider the full
range of benefits at the design and subsequent stages of a project. Second, FEGS metrics should
be candidates for inclusion in agency reporting programs to the general public, as well as to
groups of specific beneficiaries and to their representatives and those responsible for managing
resources. Third, FEGS metrics should be candidates for inclusion in monitoring programs;
equally they should be considered to be the features that ecological models seek to predict. As
these tools and data become more abundant, the capacity to estimate benefits arising from
candidate changes in policy will be improved (Sinha, Ringold, Van Houtven, & Krupnick, 2018).
Most importantly, Federal and state agencies should seek to refine our candidate metrics and
generate a set that are most directly meaningful to people.
Many of the concepts behind FEGS are prominently in use in decision-making. For example,
EPA's strategy for water quality standards and criteria quite clearly notes the need to manipulate
a range of intermediate features (e.g., dissolved oxygen and contaminants) for the sake of
specific beneficial uses, such as the abundance of edible fish (U.S. EPA, 2003b [WQS]).
Similarly, analyses of the social cost of carbon illustrate one way in which the value of a series
of final goods and services (agricultural production, human health, property damages from floods
and others) are linked via quantitative models to sequestered carbon (an intermediate good)
(Interagency Working Group on Social Cost of Carbon, 2010). That linkage is then used to
identify the value of increments of sequestered carbon. Outside the EPA, other government
agencies have begun to incorporate ecosystem services into their planning process, most notably
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the USDA Forest Service (Scarlett & Boyd, 2015). The Forest Service was directed to include
ecosystem services into their decision-making and planning (U.S. Forest Service, 2012),
although considerable discretion was allowed for the appropriateness and levels of investment.
As a result, the Forest Service often reports only provisioning goods and services like timber in
its assessment (Ruhl & Salzman, 2020).
However, while there are abundant examples of the use of FEGS in which intermediate features
are managed for the sake of a final good, there are also perplexing cases in which the final
ecological good or service appears not to be given adequate attention. For example, some
analysts suggest that invasive plants, such as the giant reed (Arundo donax), with great capacity
to disrupt ecosystems, be used to sequester carbon (Dukes & Mooney, 2004; Richards, 2002;
Ringold, Magee, & Peck, 2008; U.S. EPA, 2013). This is perplexing because one goal of
sequestering carbon is to minimize the disruption of ecosystems, but that could instead be
exacerbated by the use of invasive plants.
Another use of FEGS metrics is to help governments estimate stocks of natural capital for natural
capital accounting practices, which have piqued the interest of many governments at different
levels and across the globe. The United Nations Environmental Program has proposed a natural
capital accounting framework, but the United States has not developed its own accounts. In
natural capital accounting, a nation's natural resources and the ecosystem services that flow from
these, as raw material - assets - are used to support the economy and understand natural
resources and ecosystem health over time. The goal is to translate ecosystem metrics into
standard units that could be reported and shared broadly much like gross domestic product is
calculated (Boyd et al., 2018). FEGS could be useful in this effort by offering clear, concrete
metrics from the nation's ecosystems that could be broadly understood. In addition, because
FEGS are defined at a consistent point in the series of linked production, they are ideal for
accounting as they help to avoid double counting, such as counting the value of crabs in addition
to the value of the habitat necessary but not sufficient to produce them (e.g., Hein, van Koppen,
de Groot, & van Ierland, 2006; Keeler et al., 2012; Lele, Springate-Baginski, Lakerveld, Deb, &
Dash, 2013; Toman, 1998); e.g. This is a frequently stated concern in studies in which the value
of an intermediate ecosystem good or service is counted in addition the value of other
intermediate or final ecosystem goods and service. Many of the FEGS metrics shared in this
report are based on existing nationwide ecological sampling protocols, providing a potential
foundation for elements of natural capital accounting.
4.2 Metric Identification Process and the Classification System
In our process of identifying metrics, we identified some issues with the use of the classification
system in NESCS Plus and its predecessors). Some of those issues were noted in the Methods
section. An examination of our results expands on or illustrates of those challenges. We have
noted that beneficiaries typically directly use, appreciate, or enjoy ecosystem features quantified
by multiple metrics. In addition, those metrics may be representations of multiple ecosystems (or
environmental classes in NESCS Plus terms). The most prominent example of this is site appeal,
one of the more frequent attributes identified by the ecosystem experts. This means that there is a
one-to-many relationship between beneficiaries and environmental classes, and that will likely
need to be addressed in social analysis, mapping of FEGS, and national accounting systems.
Another issue encountered is the level of resolution of the structural elements of the
classification system. For example, the NESCS Plus beneficiary "Food and Medical Subsisters"
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is represented by three different specific beneficiaries in our report: fishers, wild rice harvesters
and Native American medicinal plants harvesters. A different set of metrics is directly important
to each. Similarly, the classification system does not identify some highly valued ecosystems.
For example, the environmental subclass Estuaries and Near Shore Marine includes estuaries
such as the Puget Sound, the Chesapeake Bay, or Galveston Bay and their host of benefits and
associated metrics. It is also the environmental subclass that includes coral reefs, which even for
similar beneficiaries have a very different set of metrics. To be fair, any classification system
will have this limitation, but these examples and our experience point out that NESCS Plus is a
higher level classification system whose users may want to identify finer levels of resolution
depending on their needs.
4.3 Research Needs
Throughout this project, we used the FEGS Framework to delineate and help select metrics for
ecosystem services. Additional efforts are needed to continue to expand upon the ideas presented
here. From our process, we identified three areas that warrant further research:
Areas where operationalizing the FEGS concept requires some additional analysis. (See
Section 3.9, Challenges to Providing Data on FEGS). In certain instances, the FEGS Framework,
with its shared definitions, would benefit from additional research. Essentially, where does the
FEGS definition and its use of ecosystem-specific beneficiaries work and under what
circumstances does it become too rigid to be useful in real-world applications? We encountered
these limitations when applying the Framework to beneficiaries and soliciting feedback from one
another and our Steering Committee experts, and identified four topics needing additional and
concentrated attention: (1) human health linkages with FEGS; (2) freshwater visibility and its
relationship with property values; (3) hunters or anglers and highly mobile game species
(anadromous fish, deer, birds) that cross ecosystem boundaries; and (4) property owners and
flooding risks. These four crosscutting ideas are important starting points for further discussion.
1. The need to expand beyond the seven ecosystem-specific beneficiaries we present
here. One ecosystem type of special interest is urban and suburban systems, which we
have not addressed in this report. These areas are densely populated but the challenge is
identifying the contribution of nature in heavily modified landscapes. Like agricultural
systems, the line between natural and built is nuanced in cities. FEGS in urban areas need
to be evaluated to define boundaries between what is provided by nature and what
benefits require significant human input and capital. For example, is a tree planted in a
sidewalk planter by a city crew providing an appealing site and shade, sustained with
trucked-in soil, fertilizer, and drip irrigation, a quantity whose enumeration would reflect
a measure of ecosystem activity? Questions such as this are important because on a daily
basis, the contact people have with nature is where they live and 84% of the U.S.
population lives in urban areas (United Nations, 2019). Bringing the FEGS perspective
into cities and urban planning will require consideration of the boundaries between the
natural and built environment, and a clear sense of the direct beneficiaries. While it may
provoke interesting philosophical discussions, it is likely that the boundaries will be
drawn by clearly stated operational decisions and rules.
2. The need to critically evaluate the proposed metrics in partnership with social
scientists. We consider the metrics detailed in this report to be "first-generation," and
they need further testing and evaluation to ensure that they best represent the linkage
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between changes in ecosystems and changes in human well-being. There are four general
means by which such testing and evaluation can be done: (1) face validity; this was the
primary method we used to evaluate our metrics; (2) reference to beneficiary-specific
literature, especially reviews such as Hunt (2005) on recreational fishing, or other
primary beneficiary-specific literature, such as Phillips et al (1993) on recreational
hunting; Shannon and Grieve (1998) on conductivity and irrigation; or Isom (1986) on
Asian Clam biofouling and thermoelectric cooling (although some of that literature
focuses on single metrics rather than multiple metrics);(3) reliance on expert opinion or
best professional judgment; the application of best professional judgement by economists
was behind our design and use of our Steering Committee, although the FEGS metrics for
some beneficiaries are best designated by disciplines other than economists; and (4)
conducting primary research; for some beneficiaries, this would include the application of
social survey methods noted in the methods section.
5. Conclusion
The ecosystem service concept is a powerful way to link and integrate social and ecological
sciences into a decision-making context. The beneficiary-first perspective distinguishes the
FEGS Framework from other ecosystem service frameworks. The FEGS Framework can
facilitate and improve outcomes of ecosystem service analysis by focusing on parts of nature
most directly used or enjoyed by people, and selecting biophysical metrics that reflect an
ecosystem's ability to provide that ecosystem service of interest. The selected FEGS metrics can
then improve the hand-offs between ecosystem production and subsequent social analysis,
communication with beneficiaries or tradeoff analysis by policy makers (Boyd et al., 2016).
In this report we suggest metrics for 45 beneficiaries and their interactions with seven
ecosystems. This is not an exhaustive list but a place to begin this process; others interested in
ecosystem service assessments can build upon this work. They can extend this work to more
beneficiaries and ecosystems and to refine our methodology and to adapt our recommendations
to specific decision- settings rather than to the general ones described in this report. This report
and its metrics are from a national and regional; they illustrate how diverse biophysical scientists
translate FEGS concepts into metrics. More detailed study is needed not only to evaluate and
refine these metrics, but also to delineate metrics for other specific beneficiaries, ecosystems, and
crosscutting issues. Rather, one must begin with considering the beneficiary and user of the
ecosystem services first and understand that people interact with and depend on nature in a
myriad of ways.
This general report is complemented by beneficiary and ecosystem-specific studies led by social
and natural scientists who contributed to this project. One example is the importance of water
clarity to many beneficiaries. This topic was explored by Angradi et al. (2018) where they
examined regional patterns in lake water clarity. Results from this work found considerable
variability not only in water clarity, but also in the ways in which different levels of water clarity
translated to qualitative descriptions on a regional basis. Additional FEGS research showed the
applicability of FEGS concepts to the delineation of cultural descriptions of medical plants in
wetland ecosystems (Nahlik et al., In prep). Other researchers also developed a recreational
fishing index from existing stream datasets that focus on game fish desired by anglers (Hughes et
al., 2021). This work combined fish taxa, abundance and size data with a set of replacement cost
weights to create a place-based estimate of the recreational fishing index for rivers and streams
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across the country (Hughes et al., 2021). The FEGS concept was also applied to coral reefs
where they identified the components of reefs that people directly experience or perceive and
that provide ecosystem services to different beneficiaries (Santavy, Horstmann, Sharpe, Yee, &
Ringold, in press). We also explored the intangible values of existence values and how best to
relate these to biophysical metrics that represent ecological integrity (Boyd et al., In prep). The
breadth of these publications demonstrates the organizing ideas of the FEGS concept by
identifying metrics from ecological datasets and translating them into a social context and
perspectives, thus improving social analysis.
This report is the product of a large interdisciplinary team of natural and social scientists. This
collaboration, which began as an ad-hoc group, grew into a formal working group within the
EPA and then an interagency team. We identified two key recommendations that can accelerate
the selection of FEGS metrics. First, form a strong team of interested experts who understand the
ecological systems and value interdisciplinary research. Second, specific to the FEGS
Framework, we were reminded to begin with the beneficiaries of interest to the ecosystem -
engage early, often, and thoughtfully. Understanding what beneficiaries appreciate and use from
an ecosystem is the key to then identify what part of the nature can serve as the metric for
evaluation. Then select a final ecosystem good that is causally related in the ecosystem
production framework that can be 1) regularly sampled (ideally from an existing sampling
methodology); and 2) can be easily understood by other experts and non-experts. The best
indicators are easy to understand and as close as possible to what is actually directly perceived to
be important by the beneficiary.
This project and report demonstrate the promise and potential for employing FEGS in analysis
and decision-making with the goal of more closely linking together social and natural sciences.
In the beginning of this report, we describe the need to identify causal linkages between human
well-being, ecosystem services, and the environment (Figure 1). This report provides the steps
necessary to identify or hypothesize metrics that matter to beneficiaries in a standardized fashion
that can assist regional and national ecosystem service analysis.
This report is one part of a suite of related tools developed by the EPA that use the same
Framework. They are designed to be useful individually, but even more useful when used
together. Another tool is a classification system for ecosystem services. The classification system
provides a consistent architecture and taxonomy. It also contains the rationale and a consistent
delineation of the three dimensions of our shared Framework— beneficiaries, environmental
classes and attributes to be used elsewhere including in this report on metrics. It also contains
tables of the relationships between dimensions. It is the National Ecosystem Goods and Services
Classification System or NESCS Plus (Newcomer-Johnson et al., 2020). The last part of the
FEGS Framework, the FEGS Community Scoping Tool identifies the priority stakeholders,
beneficiaries, ecosystem attributes and, in some instances suggests FEGS metrics for use by
individual communities (Sharpe et al., 2020).
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