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-604 | EM-964 | EM-982 |
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EM Short Name
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Chinook salmon value (household), Yaquina Bay, OR | EcoSim II - method | Specific conductivity, USA |
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EM Full Name
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Economic value of Chinook salmon per household method, Yaquina Bay, OR | EcoSim II - method | Specific Conductivity, USA |
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EM Source or Collection
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US EPA | None | US EPA |
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EM Source Document ID
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324 | 448 | 460 |
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Document Author
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Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Olson, J.R., and S.M. Cormier |
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Document Year
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2012 | 2000 | 2019 |
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Document Title
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Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity |
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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
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EM-604 | EM-964 | EM-982 |
| Not applicable | https://ecopath.org/downloads/ | (https://edg.epa.gov/ metadata/catalog/main/home.page) | |
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Contact Name
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Stephen Jordan | Carl Walters | John Olson |
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Contact Address
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U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | California State Univ. Monterey Bay, 100 Campus Center, Seaside CA 93955 |
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Contact Email
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jordan.steve@epa.gov | c.walters@oceans.ubc.ca | joolson@csumb.edu |
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EM ID
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EM-604 | EM-964 | EM-982 |
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Summary Description
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ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. 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 recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " | We developed a random forest model that predicts natural background specific conductivity (SC), a measure of total dissolved ions, for all stream segments in the contiguous United States at monthly time steps between the years 2001 to 2015. Models were trained using 11 796 observations made at 1785 minimally impaired stream segments and validated with observations from an additional 92 segments. Static predictors of SC included geology, soils, and vegetation parameters. Temporal predictors were related to climate and enabled the model to make predictions for different dates. The model explained 95% of the variation in SC among validation observations (mean absolute error = 29 μS/cm, Nash-Sutcliffe efficiency = 0.85). The model performed well across the period of interest but exhibited bias in Coastal Plain and Xeric regions (26 and 30%, respectively). National model predictions showed large spatial variation with the greatest SC predicted to occur in the desert southwest and plains. Model predictions also reflected changes at individual streams during drought. |
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Specific Policy or Decision Context Cited
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None identified | None | N/A |
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Biophysical Context
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Yaquina Bay estuary | None, Ocean ecosystems | Stream segment taken from StreamCat database |
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EM Scenario Drivers
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No scenarios presented | N/A | N/A |
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EM ID
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EM-604 | EM-964 | EM-982 |
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Method Only, Application of Method or Model Run
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Method + Application | Method Only | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised 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-604 | EM-964 | EM-982 |
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Document ID for related EM
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Doc-324 | None | None |
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EM ID for related EM
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EM-603 | EM-397 | EM-1055 | None |
EM Modeling Approach
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EM ID
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EM-604 | EM-964 | EM-982 |
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EM Temporal Extent
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2003-2008 | Not applicable | 2001-2015 |
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EM Time Dependence
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time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | both | past time |
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EM Time Continuity
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Not applicable |
discrete ?Comment:Modeller dependent |
discrete |
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EM Temporal Grain Size Value
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Not applicable | 1 | 3 |
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EM Temporal Grain Size Unit
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Not applicable | Day | Month |
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EM ID
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EM-604 | EM-964 | EM-982 |
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Bounding Type
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Geopolitical | Other | Geopolitical |
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Spatial Extent Name
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Pacific Northwest | Not applicable | Contiguous United States |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | Not applicable | >1,000,000 km^2 |
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EM ID
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EM-604 | EM-964 | EM-982 |
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EM Spatial Distribution
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spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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Not applicable | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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Not applicable | Not applicable | 3.1 km2 |
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EM ID
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EM-604 | EM-964 | EM-982 |
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EM Computational Approach
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Analytic | Analytic | Analytic |
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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
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EM-604 | EM-964 | EM-982 |
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Model Calibration Reported?
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No | No | Yes |
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Model Goodness of Fit Reported?
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No | No | Yes |
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Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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Yes | Not applicable | Yes |
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Model Uncertainty Analysis Reported?
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No | Not applicable | No |
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Model Sensitivity Analysis Reported?
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No | Not applicable | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Yes |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-604 | EM-964 | EM-982 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-604 | EM-964 | EM-982 |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-604 | EM-964 | EM-982 |
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Centroid Latitude
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44.62 | Not applicable | 39.83 |
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Centroid Longitude
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-124.02 | Not applicable | 98.58 |
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Centroid Datum
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WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
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Estimated | Not applicable | Estimated |
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EM ID
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EM-604 | EM-964 | EM-982 |
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EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Open Ocean and Seas | Rivers and Streams |
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Specific Environment Type
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Yaquina Bay estuary and ocean | Pelagic | Stream segment |
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EM Ecological Scale
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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 |
Scale of differentiation of organisms modeled
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EM ID
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EM-604 | EM-964 | EM-982 |
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EM Organismal Scale
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Other (multiple scales) |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-604 | EM-964 | EM-982 |
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None Available |
EnviroAtlas URL
| EM-604 | EM-964 | EM-982 |
| Dasymetric Allocation of Population | Big game hunting recreation demand | GAP Ecological Systems, Average Annual Precipitation, Average Annual Daily Potential Wind Energy |
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-604 | EM-964 | EM-982 |
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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-604 | EM-964 | EM-982 |
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