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-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Runoff potential of pesticides, Europe | Pollinators on landfill sites, United Kingdom | Recreational fishery index, USA | EPA national stormwater calculator tool |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Runoff potential of pesticides, Europe | Pollinating insects on landfill sites, East Midlands, United Kingdon | Recreational fishery index for streams and rivers, USA | Environmental Protection Agency National stormwater calculator tool |
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EM Source or Collection
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US EPA | EnviroAtlas | None | None | US EPA | US EPA |
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EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
254 | 389 | 414 |
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. |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Schriever, C. A., and Liess, M. | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon |
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Document Year
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2013 | 2007 | 2013 | 2021 | 2022 |
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Document Title
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EnviroAtlas - National | Mapping ecological risk of agricultural pesticide runoff | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. |
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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 |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report |
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/national-stormwatercalculator | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Carola Alexandra Schriever | Sam Tarrant | Gregg Lomnicky | Lewis Rossman |
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Contact Address
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Not reported | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | 200 SW 35th St., Corvallis, OR, 97333 | Center for environmental solutions and emergency response, Cincinnati, Ohio |
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Contact Email
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enviroatlas@epa.gov | carola.schriever@ufz.de | sam.tarrant@rspb.org.uk | lomnicky.gregg@epa.gov | n.a. |
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] | "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." |
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Specific Policy or Decision Context Cited
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None Identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | None identified | None given |
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Biophysical Context
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No additional description provided | Not applicable | No additional description provided | None | Sites up to 12 acres |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | N/A | Climate change scenarios |
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
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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 | 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-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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Document ID for related EM
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-255 | Doc-256 | Doc-257 | Doc-389 | None | None |
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EM ID for related EM
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None | None | EM-697 | None | None |
EM Modeling Approach
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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EM Temporal Extent
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2006-2010 | 2000 | 2007-2008 | 2013-2014 | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | past time | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Year | Not applicable |
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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Bounding Type
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Geopolitical | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Not applicable |
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Spatial Extent Name
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counterminous United States | EU-15 | East Midlands | United States | Not applicable |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | Not applicable |
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
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) |
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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) | length, for linear feature (e.g., stream mile) | Not applicable |
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Spatial Grain Size
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irregular | 10 km x 10 km | multiple unrelated locations | stream reach (site) | Not applicable |
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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Model Calibration Reported?
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No | No | Not applicable | No | Not applicable |
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Model Goodness of Fit Reported?
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No | No | Not applicable | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | Not applicable | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | Yes | Not applicable | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | Yes | Not applicable | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | No | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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Centroid Latitude
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39.5 | 50.01 | 52.22 | 36.21 | Not applicable |
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Centroid Longitude
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-98.35 | 4.67 | -0.91 | -113.76 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Not applicable |
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EM ID
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EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Created Greenspace | Grasslands | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Terrestrial | Arable lands in near-stream environments | restored landfills and grasslands | reach | Terrrestrial landcover |
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EM Ecological Scale
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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 | Ecological scale is finer than that of 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-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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EM Organismal Scale
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Not applicable | Not applicable | Individual or population, within a species | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
| None Available | None Available |
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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-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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None |
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None |
<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-63 | EM-92 |
EM-709 |
EM-862 | EM-937 |
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None | None |
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