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
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EM ID
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EM-97 |
EM-102 |
EM-208 |
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EM Short Name
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AnnAGNPS, Kaskaskia River watershed, IL, USA | Fish species habitat value, Tampa Bay, FL, USA | FORCLIM v2.9, Santiam watershed, OR, USA |
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EM Full Name
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AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Fish species habitat value, Tampa Bay, FL, USA | FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA |
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EM Source or Collection
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US EPA | US EPA | US EPA |
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EM Source Document ID
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137 | 187 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
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Document Author
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Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. |
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Document Year
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2011 | 2016 | 2007 |
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Document Title
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AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript |
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EM ID
em.detail.idHelp
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EM-97 |
EM-102 |
EM-208 |
| https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | Not applicable | |
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Contact Name
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Yongping Yuan | Richard Fulford | Richard T. Busing |
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Contact Address
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U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA |
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Contact Email
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yuan.yongping@epa.gov | Fulford.Richard@epa.gov | rtbusing@aol.com |
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EM ID
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EM-97 |
EM-102 |
EM-208 |
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Summary Description
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AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045. |
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Specific Policy or Decision Context Cited
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Not reported | None identifed | None identified |
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Biophysical Context
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Upper Mississipi River basin, elevation 142-194m, | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | No additional description provided |
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EM Scenario Drivers
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Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | Land Management (3); Climate Change (3) |
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EM ID
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EM-97 |
EM-102 |
EM-208 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs differentiated by scenario combination. |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-97 |
EM-102 |
EM-208 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-142 | None |
Doc-22 | Doc-23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None | EM-146 | EM-186 | EM-224 |
EM Modeling Approach
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EM ID
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EM-97 |
EM-102 |
EM-208 |
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EM Temporal Extent
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1980-2006 | 2006-2011 | 1990-2050 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time |
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EM Time Continuity
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Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year |
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EM ID
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EM-97 |
EM-102 |
EM-208 |
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Bounding Type
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Watershed/Catchment/HUC | Physiographic or Ecological | Watershed/Catchment/HUC |
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Spatial Extent Name
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East Fork Kaskaskia River watershed basin | Tampa Bay | South Santiam watershed |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 |
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EM ID
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EM-97 |
EM-102 |
EM-208 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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1 km^2 | 1 km^2 | 0.08 ha |
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EM ID
em.detail.idHelp
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EM-97 |
EM-102 |
EM-208 |
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EM Computational Approach
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Numeric | Analytic | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
em.detail.idHelp
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EM-97 |
EM-102 |
EM-208 |
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Model Calibration Reported?
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No | No | No |
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Model Goodness of Fit Reported?
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No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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Yes | No | No |
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Model Uncertainty Analysis Reported?
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Yes | No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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Unclear | No | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | N/A |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-97 |
EM-102 |
EM-208 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-97 |
EM-102 |
EM-208 |
| None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-97 |
EM-102 |
EM-208 |
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Centroid Latitude
em.detail.ddLatHelp
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38.69 | 27.74 | 44.24 |
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Centroid Longitude
em.detail.ddLongHelp
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-89.1 | -82.57 | -122.24 |
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Centroid Datum
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WGS84 | WGS84 | None provided |
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Centroid Coordinates Status
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Provided | Estimated | Provided |
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EM ID
em.detail.idHelp
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EM-97 |
EM-102 |
EM-208 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Near Coastal Marine and Estuarine | Forests |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Row crop agriculture in Kaskaskia river basin | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | primarily Conifer Forest |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale corresponds to the Environmental Sub-class | Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-97 |
EM-102 |
EM-208 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Species | Species |
Taxonomic level and name of organisms or groups identified
| EM-97 |
EM-102 |
EM-208 |
| None Available |
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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-97 |
EM-102 |
EM-208 |
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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-97 |
EM-102 |
EM-208 |
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None |
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