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-93 | EM-306 | EM-836 | EM-938 |
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EM Short Name
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Stream nitrogen removal, Mississippi R. basin, USA | Urban Temperature, Baltimore, MD, USA | Bird abundance on restored landfills, UK | OpenNSPECT v. 1.2 |
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EM Full Name
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Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Urban Air Temperature Change, Baltimore, MD, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK | OpenNSPECT v. 1.2 |
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EM Source or Collection
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US EPA | i-Tree | USDA Forest Service | None | None |
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EM Source Document ID
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52 | 217 | 406 | 431 |
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Document Author
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Hill, B. and Bolgrien, D. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Eslinger, David L., H. Jamieson Carter, Matt Pendleton, Shan Burkhalter, Margaret Allen |
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Document Year
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2011 | 2016 | 2011 | 2012 |
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Document Title
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Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | “OpenNSPECT: The Open-source Nonpoint Source Pollution and Erosion Comparison Tool.” NOAA Office for Coastal Management, Charleston, South Carolina. Accessed (11/2022) at https://coast.noaa.gov/digitalcoast/tools/opennspect.html |
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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 |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage |
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
| Not applicable | Not applicable | Not applicable | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | |
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Contact Name
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Brian Hill | Gordon M. Heisler | Lutfor Rahman | Not reported |
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Contact Address
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Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413 |
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Contact Email
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hill.brian@epa.gov | gheisler@fs.fed.us | lutfor.rahman@northampton.ac.uk | Not reported |
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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Summary Description
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ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | "This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)" |
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Specific Policy or Decision Context Cited
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Not applicable | None identified | None identified | None identified |
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Biophysical Context
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Agricultural landuse , 1st-10th order streams | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | No additional description provided |
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EM Scenario Drivers
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Not applicable | No scenarios presented | No scenarios presented | No scenarios presented |
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | 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 |
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-93 | EM-306 | EM-836 | EM-938 |
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Document ID for related EM
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Doc-154 | Doc-155 | Doc-220 | Doc-219 | Doc-218 | None | None |
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EM ID for related EM
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None | None | EM-837 | EM-940 |
EM Modeling Approach
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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EM Temporal Extent
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2000-2008 | May 5-Sept 30 2006 | Not applicable | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Hour | Not applicable | Not applicable |
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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Bounding Type
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Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Not applicable |
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Spatial Extent Name
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Upper Mississippi, Ohio and Missouri River sub-basins | Baltimore, MD | East Midland | Not applicable |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | Not applicable |
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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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) | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
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1 km | 10m x 10m | multiple unrelated sites | 30 m |
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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Model Calibration Reported?
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No | Yes | Not applicable | Not applicable |
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Model Goodness of Fit Reported?
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No | Yes | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None | None |
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Model Operational Validation Reported?
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No | No | Not applicable | Not applicable |
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Model Uncertainty Analysis Reported?
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Yes | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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Unclear | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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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-93 | EM-306 | EM-836 | EM-938 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-93 | EM-306 | EM-836 | EM-938 |
| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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Centroid Latitude
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36.98 | 39.28 | 52.22 | Not applicable |
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Centroid Longitude
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-89.13 | -76.62 | -0.91 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable |
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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EM Environmental Sub-Class
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Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Created Greenspace | Grasslands | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Not applicable | Urban landscape and surrounding area | restored landfills and conserved grasslands | Coastal and non-coastal |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | 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 |
Scale of differentiation of organisms modeled
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EM ID
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EM-93 | EM-306 | EM-836 | EM-938 |
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EM Organismal Scale
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Not applicable | Not applicable | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-93 | EM-306 | EM-836 | EM-938 |
| None Available | None Available |
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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-93 | EM-306 | EM-836 | EM-938 |
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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-93 | EM-306 | EM-836 | EM-938 |
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None | None |
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