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-59 |
EM-937 | EM-1021 |
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EM Short Name
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EnviroAtlas-Air pollutant removal | EPA national stormwater calculator tool | CMAQ chemical transport model, UK |
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EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Environmental Protection Agency National stormwater calculator tool | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
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EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
US EPA | None |
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EM Source Document ID
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223 |
428 ?Comment:This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool. |
483 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon | Chemel, C., Fisher, B.E.A., Kong, X., Francis, X.V., Sokhi, R.S., Good, N., Collins, W.J. and Folberth, G.A. |
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Document Year
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2013 | 2022 | 2014 |
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Document Title
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EnviroAtlas - Featured Community | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
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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 on US EPA EnviroAtlas website | Published EPA report | Published journal manuscript |
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EM ID
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EM-59 |
EM-937 | EM-1021 |
| https://www.epa.gov/enviroatlas | https://www.epa.gov/water-research/national-stormwatercalculator | https://www.epa.gov/cmaq/download-cmaq | |
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Contact Name
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EnviroAtlas Team | Lewis Rossman | B.E.A. Fisher |
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Contact Address
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Not reported | Center for environmental solutions and emergency response, Cincinnati, Ohio | Little Beeches, Headley Road, Leatherhead KT22 8PT, UK. |
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Contact Email
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enviroatlas@epa.gov | n.a. | None provided |
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EM ID
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EM-59 |
EM-937 | EM-1021 |
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Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | "Abstract: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data." | This paper shows how the advanced chemical transport model CMAQ can be used to estimate future levels of PM2.5 in the UK, the key air pollutant in terms of human health effects, but one which is largely made up from the formation of secondary particulate in the atmosphere. By adding the primary particulate contribution from typical urban roads and including a margin for error, it is concluded that the current indicative limit value for PM2.5 will largely be met in 2020 assuming 2006 meteorological conditions. Contributions to annual average regional PM2.5 concentration from wild fires in Europe in 2006 and from possible climate change between 2006 and 2020 are shown to be small compared with the change in PM2.5 concentration arising from changes in emissions between 2006 and 2020. The contribution from emissions from major industrial sources regulated in the UK is estimated from additional CMAQ calculations. The potential source strength of these emissions is a useful indicator of the linearity of the response of the atmosphere to changes in emissions. Uncertainties in the modelling of regional and local sources are taken into account based on previous evaluations of the models. Future actual trends in emissions mean that exceedences of limit values may arise, and these and further research into PM2.5 health effects will need to be part of the future strategy to manage PM2.5 concentrations. |
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Specific Policy or Decision Context Cited
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None identified | None given | None identified |
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Biophysical Context
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No additional description provided | Sites up to 12 acres | United kingdom atmosphere |
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EM Scenario Drivers
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No scenarios presented | Climate change scenarios | 2020 European emissions scenario |
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EM ID
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EM-59 |
EM-937 | EM-1021 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application |
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New or Pre-existing EM?
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Application of existing 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
em.detail.idHelp
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EM-59 |
EM-937 | EM-1021 |
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Document ID for related EM
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Doc-345 | None | Doc-478 | Doc-481 | Doc-482 |
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EM ID for related EM
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None | None | EM-1012 | EM-1019 | EM-1020 |
EM Modeling Approach
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EM ID
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EM-59 |
EM-937 | EM-1021 |
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EM Temporal Extent
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2008-2010 | Not applicable | 2006-2020 |
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EM Time Dependence
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time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | both |
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EM Time Continuity
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discrete | Not applicable | discrete |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | Not applicable | 14 |
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EM Temporal Grain Size Unit
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Hour | Not applicable | Year |
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EM ID
em.detail.idHelp
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EM-59 |
EM-937 | EM-1021 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Not applicable | Geopolitical |
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Spatial Extent Name
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Durham NC and vicinity | Not applicable | United Kingdom |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | Not applicable | 100,000-1,000,000 km^2 |
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EM ID
em.detail.idHelp
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EM-59 |
EM-937 | EM-1021 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | Not applicable | Not applicable |
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EM ID
em.detail.idHelp
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EM-59 |
EM-937 | EM-1021 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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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-59 |
EM-937 | EM-1021 |
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Model Calibration Reported?
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Unclear | Not applicable | Yes |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Not applicable |
Yes ?Comment:Two versions of CMAQ (v4.6 and v4.7) were used to assess performance. Both values are provided here respectively. |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Not applicable | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Not applicable | Unclear |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Not applicable | Unclear |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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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])
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EM-59 |
EM-937 | EM-1021 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-59 |
EM-937 | EM-1021 |
| None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-59 |
EM-937 | EM-1021 |
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Centroid Latitude
em.detail.ddLatHelp
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35.99 | Not applicable | 54 |
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Centroid Longitude
em.detail.ddLongHelp
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-78.96 | Not applicable | 4 |
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Centroid Datum
em.detail.datumHelp
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None provided | Not applicable | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Not applicable | Estimated |
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EM ID
em.detail.idHelp
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EM-59 |
EM-937 | EM-1021 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Created Greenspace | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Atmosphere |
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Specific Environment Type
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Urban and vicinity | Terrrestrial landcover | United Kingdom atmosphere |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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 |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-59 |
EM-937 | EM-1021 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-59 |
EM-937 | EM-1021 |
| 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)
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EM-59 |
EM-937 | EM-1021 |
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None |
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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)
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EM-59 |
EM-937 | EM-1021 |
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