EcoService Models Library (ESML)

Compare EMs

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.

Compare Criteria:

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Comparing:

EM-93
EM-463
EM-650
EM-660
EM-836
EM-850
EM-942

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
EM Short Name em.detail.shortNameHelp ?	Stream nitrogen removal, Mississippi R. basin, USA	Curios/jewelry production, St. Croix, USVI	Sedge Wren density, CREP, Iowa, USA	RUM: Valuing fishing quality, Michigan, USA	Bird abundance on restored landfills, UK	Invertebrate community index, Alabama	Pollutant dispersion by vegetation barriers
EM Full Name em.detail.fullNameHelp ?	Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA	Relative curios/jewelry production (on reef), St. Croix, USVI	Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA	Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK	Invertebrate community index, Choctawhatchee-Pea Rivers watershed, Alabama	Pollutant dispersion by vegetation barriers
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	US EPA	None	None	None	None	US EPA
EM Source Document ID	52	335	372	382 ? Comment: Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010.	406	409	435
Document Author em.detail.documentAuthorHelp ?	Hill, B. and Bolgrien, D.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	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	Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson	Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton	Bennett, H.H., Mullen, M.W., Stewart, P.M., Sawyer, J.A., and E. C. Webber	Hashad, K. B. Yang, J. T. Steffens, R. W. Baldauf, P. Deshmukh, K. M. Zhang
Document Year em.detail.documentYearHelp ?	2011	2014	2010	2014	2011	2004	2021
Document Title em.detail.sourceIdHelp ?	Nitrogen removal by streams and rivers of the Upper Mississippi River basin	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Valuing recreational fishing quality at rivers and streams	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	Development of an invertebrate community index for an Alabama coastal plain watershed	Parameterizing pollutant dispersion downwind of roadside vegetation barriers
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed but unpublished (explain in Comment)
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Journal manuscript submitted or in review

Software and Access

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Brian Hill	Susan H. Yee	David Otis	Richard Melstrom	Lutfor Rahman	E. Cliff Webber	K. Max Zhang
Contact Address	Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA	Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK	Troy State University, 4004 Clairmont Avenue South, Birmingham, Alabama 35222 progress.	Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA
Contact Email	hill.brian@epa.gov	yee.susan@epa.gov	dotis@iastate.edu	melstrom@okstate.edu	lutfor.rahman@northampton.ac.uk	hbennett1978@hotmail.com	kz33@cornell.edu

EM Description

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
Summary Description em.detail.summaryDescriptionHelp ?	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/kmd in the Upper Mississippi River to 0.01–54 kg/ kmd 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/kmd ), followed by the Ohio and Missouri Rivers (0.01–236 kg/kmd ) 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/kmd in the Upper Mississippi River to 0.001–1.43 kg/kmd 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)."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(6) production of curios and jewelry associated with each habitat."	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 Sedge Wren (Cistothorus platensis)... 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: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) e^(-0.7982511 + 0.0285891 bbspath + 0.0105094 *grass400)	ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. "	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)."	ABSTRACT: "Macroinvertebrates were collected from 49 randomly selected sites from first through sixth-order streams in the Choctawhatchee-Pea Rivers watershed and were identified to genus level. Thirty-eight candidate metrics were examined, and an invertebrate community index (ICI) was calibrated by eliminating metrics that failed to separate impaired from unimpaired streams. Each site was scored with those metrics, and narrative scores were assigned based on ICI scores. Least impacted sites scored significantly lower than sites impacted by row crop agriculture, cattle, and urban land uses. Conditions in the watershed suggest that the entire area has experienced degradation through past and current land use practices. An initial validation of the index was performed and is described. Additional evaluations of the index are in progress."	ABSTRACT: "Communities living and working in near-road environments are exposed to elevated levels of traffic-related air pollution (TRAP), causing adverse health effects. Roadside vegetation may help reduce TRAP through enhanced deposition and mixing….there are no studies that developed a dispersion model to characterize pollutant concentrations downwind of vegetation barriers. To account for the physical mechanisms, by which the vegetation barrier deposits and disperses pollutants, we propose a multi-region approach that describes the parameters of the standard Gaussian equations in each region. The four regions include the vegetation, a downwind wake, a transition, and a recovery zone. For each region, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition which is a major factor in pollutant reduction by vegetation barriers. We generated data from 75 (CFD)-based simulations, using the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model, to parameterize the Gaussian-based equations. The simulations used reflected a wide range of vegetation barriers, with heights from 2-10 m, and various densities, represented by leaf area index values from 4-11, and evaluated under different urban conditions, represented by wind speeds from 1-5 m/s. The CTAG model has been evaluated against two field measurements to ensure that it can properly represent the vegetation barrier’s pollutant deposition and dispersion. The proposed multi-region Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing deposition. The multi-region model’s normalized mean error (NME) ranged between 0.18-0.3, the fractional bias (FB) ranged between -0.12-0.09, and R2 value ranged from 0.47-0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable range. Even though the multi-region model is parameterized for coniferous trees, our sensitivity study indicates that the parameterized Gaussian-based model can provide useful predictions for hedge/bushes vegetative barriers as well." ADDITIONAL DESCRIPTION: Detailed variable relationships are described in the source document. The VRD associated with the ESML entry provides variables in a simplified form.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Not applicable	None identified	None identified	None identified	None identified	None reported	None identified
Biophysical Context	Agricultural landuse , 1st-10th order streams	No additional description provided	Prairie pothole region of north-central Iowa	stream and river reaches of Michigan	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).	1st through 6th order streams on low elevation coastal plains	Communities living and working in near-road environments
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Not applicable	No scenarios presented	No scenarios presented	targeted sport fish biomass	No scenarios presented	N/A	None scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method Only	Method + Application	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	Application of existing model ? Comment: Models developed by Quamen (2007).	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

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-154 \| Doc-155	None	Doc-372	None	None	Doc-407	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	EM-652 \| EM-651 \| EM-649 \| EM-648	None	EM-837	EM-848	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
EM Temporal Extent em.detail.tempExtentHelp ?	2000-2008	2006-2007, 2010	1992-2007	2008-2010	Not applicable	2002	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	Not applicable
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
Bounding Type em.detail.boundingTypeHelp ?	Watershed/Catchment/HUC	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Watershed/Catchment/HUC	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Upper Mississippi, Ohio and Missouri River sub-basins	Coastal zone surrounding St. Croix	CREP (Conservation Reserve Enhancement Program) wetland sites	HUCS in Michigan	East Midland	Choctawhatchee-Pea rivers watershed	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	1-10 km^2	100,000-1,000,000 km^2	1000-10,000 km^2.	1000-10,000 km^2.	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
EM Spatial Distribution em.detail.distributeLumpHelp ?	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)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	length, for linear feature (e.g., stream mile)	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	length, for linear feature (e.g., stream mile)
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 km	10 m x 10 m	multiple, individual, irregular shaped sites	reach in HUC	multiple unrelated sites	Not applicable	user defined

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Unclear	No	Not applicable	Yes ? Comment: Culled metrics that did not distinguish between impaired and unimpaired sites.	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	Yes	Not applicable	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	R squared \| 0.552 \| unitless	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	Unclear	No	Not applicable	Yes	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes	No	No	No	Not applicable	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Unclear	No	No	No	Not applicable	Yes	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Yes	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
North America United States New York Montana Indiana Kentucky Minnesota Kansas Alabama Nebraska Pennsylvania Iowa Colorado Illinois Missouri Ohio Georgia	None	North America United States Iowa	North America United States Michigan	Europe United Kingdom	North America United States Alabama	None

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
Centroid Latitude em.detail.ddLatHelp ?	36.98	17.73	42.62	45.12	52.22	31.39	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-89.13	-64.77	-93.84	85.18	-0.91	-85.71	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Rivers and Streams	Created Greenspace \| Grasslands	Rivers and Streams	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	Coral reefs	Grassland buffering inland wetlands set in agricultural land	stream reaches	restored landfills and conserved grasslands	1st - 6th order streams	Communities living and working in near-road environments
EM Ecological Scale em.detail.ecoScaleHelp ?	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	Ecological scale is finer than that of 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 and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Species	Not applicable	Individual or population, within a species	Other (Comment) ? Comment: To species but focused on functional group classes	Not applicable

Taxonomic level and name of organisms or groups identified

EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
None Available	None Available	Species Sedge Wren (Cistothorus platensis)	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye	Species Grey partridge (Perdix perdix) Collared dove (Streptopelia decaocto) Woodpidgeon (Columba palumbus) Lapwing (Vanellus vanellus) Magpie (Pica pica) Dunnock (Prunella modularis) Starling (Sturnus vulgaris) Skylark (Alauda arvensis) Pheasant (Phasianus colchicus) Blackbird (Turdus merula) Carrion crow (Corvus corone) Tree sparrow (Passer montanus)	None Available	None Available

EnviroAtlas URL

EM-93	EM-463	EM-650	EM-660	EM-836	EM-850	EM-942
National Hydrography Dataset Plus (NHD PlusV2), Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	Hectares of cotton crops	GAP Ecological Systems	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate	None Available	None Available	GAP Ecological Systems, Average Annual Precipitation

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-463	EM-650	EM-660	EM-836	EM-850	EM-942
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters	[Ecosystem] Provisioning Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

<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-463	EM-650	EM-660	EM-836	EM-850	EM-942
Rivers and Streams (111) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Other Natural Components (7) Seashells Stock Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	None	None	None

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CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

<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)