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-617 | EM-684 |
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EM Short Name
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AnnAGNPS, Kaskaskia River watershed, IL, USA | RBI Spatial Analysis Method | Beach visitation, Barnstable, MA, 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 | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Beach visitation, Barnstable, Massachusetts, 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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137 | 367 | 386 |
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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. | Bousquin, J., Mazzotta M., and W. Berry | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta |
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Document Year
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2011 | 2017 | 2018 |
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Document Title
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AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts |
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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 EPA report | Published journal manuscript |
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EM ID
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EM-97 | EM-617 | EM-684 |
| 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 | Justin Bousquin | Kate K, Mulvaney |
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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 | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | Not reported |
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Contact Email
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yuan.yongping@epa.gov | bousquin.justin@epa.gov | Mulvaney.Kate@EPA.gov |
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EM ID
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EM-97 | EM-617 | EM-684 |
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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" | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. |
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Specific Policy or Decision Context Cited
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Not reported | None identified | To assess the number of people who would be impacted by beach closures. |
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Biophysical Context
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Upper Mississipi River basin, elevation 142-194m, | wetlands | Four separate beaches within the community of Barnstable |
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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 | N/A | No scenarios presented |
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EM ID
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EM-97 | EM-617 | EM-684 |
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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-97 | EM-617 | EM-684 |
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Document ID for related EM
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Doc-142 | None | Doc-386 | Doc-387 |
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EM ID for related EM
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None | None | EM-682 | EM-685 | EM-683 | EM-686 |
EM Modeling Approach
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EM ID
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EM-97 | EM-617 | EM-684 |
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EM Temporal Extent
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1980-2006 | Not applicable | 2011 - 2016 |
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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 | past 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
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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 | Day |
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EM ID
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EM-97 | EM-617 | EM-684 |
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Bounding Type
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Watershed/Catchment/HUC | Not applicable | Physiographic or ecological |
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Spatial Extent Name
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East Fork Kaskaskia River watershed basin | Not applicable | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | Not applicable | 10-100 ha |
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EM ID
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EM-97 | EM-617 | EM-684 |
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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 | length, for linear feature (e.g., stream mile) |
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Spatial Grain Size
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1 km^2 | Not reported | by beach site |
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EM ID
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EM-97 | EM-617 | EM-684 |
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EM Computational Approach
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Numeric | 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-97 | EM-617 | EM-684 |
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Model Calibration Reported?
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No | Not applicable | Yes |
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Model Goodness of Fit Reported?
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No | Not applicable | 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 | Not applicable | No |
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Model Uncertainty Analysis Reported?
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Yes | Not applicable | No |
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Model Sensitivity Analysis Reported?
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Unclear | Not applicable | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-97 | EM-617 | EM-684 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-97 | EM-617 | EM-684 |
| None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-97 | EM-617 | EM-684 |
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Centroid Latitude
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38.69 | Not applicable | 41.64 |
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Centroid Longitude
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-89.1 | Not applicable | -70.29 |
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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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Provided | Not applicable | Estimated |
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EM ID
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EM-97 | EM-617 | EM-684 |
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EM Environmental Sub-Class
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Agroecosystems | Inland Wetlands | Near Coastal Marine and Estuarine |
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Specific Environment Type
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Row crop agriculture in Kaskaskia river basin | Restored wetlands | Saltwater beach |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-97 | EM-617 | EM-684 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-97 | EM-617 | EM-684 |
| 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-97 | EM-617 | EM-684 |
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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-617 | EM-684 |
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