EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
EM Short Name
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EPA H2O, Tampa Bay Region, FL,USA | Wetland shellfish production, Gulf of Mexico, USA | WESP Method | SLAMM, Tampa Bay, FL, USA | Neighborhood greenness and health, FL, USA |
EM Full Name
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EPA H2O, Tampa Bay Region, FL, USA | Wetland shellfish production, Gulf of Mexico, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA |
EM Source or Collection
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US EPA |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
None | None | None |
EM Source Document ID
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321 | 324 | 390 |
415 ?Comment:Secondary sources: Documents 412 and 413. |
417 |
Document Author
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Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Adamus, P. R. | Sherwood, E. T. and H. S. Greening | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik |
Document Year
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2015 | 2012 | 2016 | 2014 | 2016 |
Document Title
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EPA H20 User Manual | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats | Neighborhood greenness and chronic health conditions in Medicare beneficiaries |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published EPA report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
http://www.epa.gov/ged/tbes/EPAH2O | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | Not applicable | |
Contact Name
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Marc J. Russell, Ph.D. | Stephen J. Jordan | Paul R. Adamus | Edward T. Sherwood | Scott C. Brown |
Contact Address
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USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA | Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 |
Contact Email
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russell.marc@epa.gov | jordan.steve@epa.gov | adamus7@comcast.net | esherwood@tbep.org | sbrown@med.miami.edu |
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
Summary Description
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AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" | ABSTRACT: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." |
Specific Policy or Decision Context Cited
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None reported | None identified | None identified | None identified | None identified |
Biophysical Context
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Not applicable | Estuarine environments and marsh-land interfaces | None | No additional description provided | No additional description provided |
EM Scenario Drivers
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Land Use, EGS algorithm values, | Shellfish type; Changes to submerged aquatic vegetation (SAV) | N/A | Varying sea level rise (baseline - 2m), and two habitat adaption strategies | No scenarios presented |
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
Method Only, Application of Method or Model Run
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Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
Document ID for related EM
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None | None | None | Doc-412 | Doc-413 | None |
EM ID for related EM
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None | EM-604 | EM-603 | EM-718 | EM-857 | None |
EM Modeling Approach
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
EM Temporal Extent
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Not applicable | 1950 - 2050 | Not applicable | 2002-2100 | 2010-2011 |
EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Varies by Run | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Not applicable | Not applicable |
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
Bounding Type
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Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
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Tampa Bay region | Gulf of Mexico (estuarine and coastal) | Not applicable | Tampa Bay estuary watershed | Miami-Dade County |
Spatial Extent Area (Magnitude)
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1000-10,000 km^2. | 10,000-100,000 km^2 | Not applicable | 1000-10,000 km^2. | 1000-10,000 km^2. |
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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30m x 30m | 55.2 km^2 | not reported | 10 x 10 m | Census block |
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
EM Computational Approach
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Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
Model Calibration Reported?
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No | Yes | Not applicable | No | Not applicable |
Model Goodness of Fit Reported?
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No | No | Not applicable | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
Model Operational Validation Reported?
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No | No | No | No | No |
Model Uncertainty Analysis Reported?
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No | No | Not applicable | No | No |
Model Sensitivity Analysis Reported?
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No | No | Not applicable | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
None |
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None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
Centroid Latitude
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28.05 | 30.44 | Not applicable | 27.76 | 25.64 |
Centroid Longitude
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-82.52 | -87.99 | Not applicable | -82.54 | -80.5 |
Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Not applicable | Estimated | Estimated |
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Inland Wetlands | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace |
Specific Environment Type
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All terestrial landcover and waterbodies | Submerged aquatic vegetation in estuaries and coastal lagoons | Wetlands | Esturary and associated urban and terrestrial environment | urban neighborhood greenspace |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
EM Organismal Scale
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Not applicable | Species | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
None Available |
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None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-392 |
EM-397 ![]() |
EM-706 |
EM-863 ![]() |
EM-876 |
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None |
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