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-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
EM Short Name
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EnviroAtlas-Nat. filtration-water | ARIES sediment regulation, Puget Sound Region, USA | Savannah Sparrow density, CREP, Iowa, USA | HAWQS model method | EcoSim II - method |
EM Full Name
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US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA | ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA | Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Hydrologic and water quality system (HAWQS) model v.1.1 user's guide methodology | EcoSim II - method |
EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
ARIES | None | US EPA | None |
EM Source Document ID
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223 | 302 | 372 | 445 | 448 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | United States Environmental Protection Agency | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell |
Document Year
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2013 | 2014 | 2010 | 2019 | 2000 |
Document Title
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EnviroAtlas - Featured Community | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | HAWQS 1.0 (Hydrologic and Water Quality System) modeling framework | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II |
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 on US EPA EnviroAtlas website | Published journal manuscript | Published report | Published EPA report | Published journal manuscript |
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
https://www.epa.gov/enviroatlas | http://aries.integratedmodelling.org/ | Not applicable | https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/GDOPBA | https://ecopath.org/downloads/ | |
Contact Name
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EnviroAtlas Team | Ken Bagstad | David Otis | Raghavan Srinivasan | Carl Walters |
Contact Address
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Not reported | Geosciences and Environmental Change Science Center, US Geological Survey | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Spatial Sciences Laboratory, Dept. of ecology and conservatin Biology, Texas A&M university | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 |
Contact Email
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enviroatlas@epa.gov | kjbagstad@usgs.gov | dotis@iastate.edu | r-srinivasan@tamu.edu | c.walters@oceans.ubc.ca |
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
Summary Description
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The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects 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 DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) | Author overview: " The Hydrologic and Water Quality System (HAWQS) is a web-based interactive water quantity and water quality modeling system that employs the internationally-recognized public domain model Soil and Water Assessment Tool (SWAT) as its core modeling engine. HAWQS provides users with: 1) interactive web interfaces and maps and pre-loaded input data; 2) Output data includes tables, charts, graphs, and raw data; 3) A user guide; and 4) Online development, execution, and storage for users modeling projects. HAWQS enables use of SWAT to simulate the effects of management practices based on an extensive array of crops, soils, natural vegetation types, land uses, and climate change scenarios for hydrology and the following water quality parameters: Sediment pathogens, nutrients, biological oxygen demand, dissolved oxygen, pesticides, and water temperature. HAWQS users can select from three watershed scales, or hydrologic unit codes (HUCs)—small (HUC 12), medium (HUC 10), and large (HUC 8)—to run simulations. HAWQS allows for further aggregation and scalability of annual, monthly, and daily estimates of water quality across large geographic areas up to and including the continental United States. The United States Environmental Protection Agency (USEPA) Office of Water (OW) supports and provides project management and funding for HAWQS. The Texas A&M University Spatial Sciences Laboratory and EPA subject matter experts provide ongoing technical support including system design, modeling, and software development. The United States Department of Agriculture (USDA) and Texas A&M University jointly developed SWAT and have actively supported the model for more than 25 years. The system was developed to meet the needs of the USEPA Office of Water. It can also be employed by other Federal Agencies, State and local governments, academics, and contractors. " | 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 " |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None |
Biophysical Context
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No additional description provided | No additional description provided | Prairie pothole region of north-central Iowa | N/A | None, Ocean ecosystems |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A |
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method Only |
New or Pre-existing EM?
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Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised 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-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
Document ID for related EM
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Doc-198 | Doc-303 | Doc-305 | Doc-372 | None | None |
EM ID for related EM
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EM-137 | EM-142 | None | EM-648 | EM-649 | EM-650 | EM-651 | None | EM-1055 |
EM Modeling Approach
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
EM Temporal Extent
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1999-2010 | 1971-2005 | 1992-2007 | Not applicable | Not applicable |
EM Time Dependence
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time-stationary ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. |
time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | both |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
discrete ?Comment:Time can be in day, month or year increments |
discrete ?Comment:Modeller dependent |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Year | Day |
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
Bounding Type
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Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Other |
Spatial Extent Name
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Durham, NC and vicinity | Puget Sound Region | CREP (Conservation Reserve Enhancement Program) wetland sites | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10,000-100,000 km^2 | 1-10 km^2 | Not applicable | Not applicable |
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
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) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
Spatial Grain Size
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irregular | 200m x 200m | multiple, individual, irregular shaped sites | Not applicable | Not applicable |
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
EM Computational Approach
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Analytic ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. |
Analytic | Analytic | Numeric | 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-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
Model Calibration Reported?
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Unclear | Yes | Unclear | No | No |
Model Goodness of Fit Reported?
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No | No | No | 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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Unclear | No | Unclear | No | Not applicable |
Model Uncertainty Analysis Reported?
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Unclear | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
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Unclear | No | No | No | Not applicable |
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-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
Centroid Latitude
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35.99 | 48 | 42.62 | Not applicable | Not applicable |
Centroid Longitude
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-78.96 | -123 | -93.84 | Not applicable | Not applicable |
Centroid Datum
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None provided | WGS84 | WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable | Not applicable |
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
EM Environmental Sub-Class
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Rivers and Streams | Created Greenspace | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Open Ocean and Seas |
Specific Environment Type
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Urban areas including streams | Terrestrial environment surrounding a large estuary | Grassland buffering inland wetlands set in agricultural land | HUCs | Pelagic |
EM Ecological Scale
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Not applicable | Ecological scale corresponds to 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 corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
EM Organismal Scale
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Not applicable | Not applicable | Species | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Taxonomic level and name of organisms or groups identified
EM-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
None Available | None Available |
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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-51 ![]() |
EM-327 | EM-652 | EM-960 | EM-964 |
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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-327 | EM-652 | EM-960 | EM-964 |
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None |
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None |
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