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-97
EM-122
EM-418
EM-653
EM-701
EM-891
EM-940

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
EM Short Name em.detail.shortNameHelp ?	AnnAGNPS, Kaskaskia River watershed, IL, USA	Land-use change and crop-based production, Europe	SIRHI, St. Croix, USVI	Natural amenities and population migration, USA	Blue-winged Teal recruits, CREP wetlands, IA, USA	Home ownership, Great Lakes, USA	OpenNSPECT v. 1.1, California, U.S.
EM Full Name em.detail.fullNameHelp ?	AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA	Land-use change effects on crop-based production, Europe	SIRHI (SImplified Reef Health Index), St. Croix, USVI	Natural amenities and rural population migration, USA	Blue-winged Teal duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Human well being indicator - home ownership, Great Lakes waterfront, USA	OpenNSPECT v. 1.1, California, U.S.
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	EU Biodiversity Action 5	US EPA	USDA Forest Service	None	US EPA	None
EM Source Document ID	137	228	335	375	372 ? Comment: Document 373 is a secondary source for this EM.	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers	433 ? Comment: Additional source for this EM: NOAA, 2012. National Oceanic and Atmospheric Administration. Technical Guide for OpenNSPECT, Version 1.1, p. 44. http://www.csc.noaa.gov/digitalcoast/tools/opennspect.
Document Author em.detail.documentAuthorHelp ?	Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M.	Haines-Young, R., Potschin, M. and Kienast, F.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Cordell H. K., V. Heboyan, F. Santos, J. C. Bergstrom	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	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick	Morrison, K. D. and C. A. Kolden
Document Year em.detail.documentYearHelp ?	2011	2012	2014	2011	2010	None	2015
Document Title em.detail.sourceIdHelp ?	AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Natural amenities and rural population migration	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Human well-being and natural capital indictors for Great Lakes waterfront revitalization	Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment
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 but unpublished (explain in Comment)	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published report	Journal manuscript submitted or in review	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://coast.noaa.gov/digitalcoast/tools/opennspect.html
Contact Name em.detail.contactNameHelp ?	Yongping Yuan	Marion Potschin	Susan H. Yee	Ken Cordell	David Otis	Ted Angradi	Crystal A. Kolden
Contact Address	U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	U.S. Department of Agriculture, Forest Service, Southern Research Station, Athens, GA 30602	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804	Not reported
Contact Email	yuan.yongping@epa.gov	marion.potschin@nottingham.ac.uk	yee.susan@epa.gov	Not reported	dotis@iastate.edu	tedangradi@gmail.com	ckolden@uidaho. Edu

EM Description

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
Summary Description em.detail.summaryDescriptionHelp ?	AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198"	ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios."	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...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati	ABSTRACT: "Research suggests that significant relationships exist between rural population change and natural amenities. Thus, understanding and predicting domestic migration trends as a function of changes in natural amenities is important for effective regional growth and development policies and strategies. In this study, we first estimated an econometric model which showed the effects of natural amenities, such as climate and landscape variables, on rural population migration patterns in the United States between 1990 and 2007. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections. Results suggest that people prefer rural areas with mild winters and cooler summers; thus we can expect a direct impact of climate change on population migration when areas associated with these conditions change. Results also suggest preference for varied landscapes that feature a mix of forest land and open space (e g , pasture and range land). During the projection period from 2010 to 2060 in the United States, changes in natural amenities were predicted to have positive effects on rural population migration trends in most parts of the Intermountain and Pacific Northwest regions, and some parts of the Southeastern, South Central, and Northeastern U S regions (e g , Southern Appalachian Mountains, Ozark Mountains, northern New England). Changes in natural amenities were predicted to have negative effects on rural population migration trends during the projection period in Midwestern regions (e g , Great Plains and North Central regions)." AUTHOR'S DESCRIPTION: "This model was estimated for 2,014 rural counties in the continental United States using various national data bases and sources. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections."	ABSTRACT: "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…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2Rn where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = HZB/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).	ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. "	ABSTRACT: "Wildfire is a common disturbance that can significantly alter vegetation in watersheds and affect the rate of sediment and nutrient transport to adjacent nearshore oceanic environments. Changes in runoff resulting from heterogeneous wildfire effects are not well-understood due to both limitations in the field measurement of runoff and temporally-limited spatial data available to parameterize runoff models. We apply replicable, scalable methods for modeling wildfire impacts on sediment and nonpoint source pollutant export into the nearshore environment, and assess relationships between wildfire severity and runoff. Nonpoint source pollutants were modeled using a GIS-based empirical deterministic model parameterized with multi-year land cover data to quantify fire-induced increases in transport to the nearshore environment. Results indicate post-fire concentration increases in phosphorus by 161 percent, sediments by 350 percent and total suspended solids (TSS) by 53 percent above pre-fire years. Higher wildfire severity was associated with the greater increase in exports of pollutants and sediment to the nearshore environment, primarily resulting from the conversion of forest and shrubland to grassland. This suggests that increasing wildfire severity with climate change will increase potential negative impacts to adjacent marine ecosystems. The approach used is replicable and can be utilized to assess the effects of other types of land cover change at landscape scales. It also provides a planning and prioritization framework for management activities associated with wildfire, including suppression, thinning, and post-fire rehabilitation, allowing for quantification of potential negative impacts to the nearshore environment in coastal basins."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Not reported	None identified	None identified	None identified	None identified	None identified	None identified
Biophysical Context	Upper Mississipi River basin, elevation 142-194m,	No additional description provided	No additional description provided	No additional description provided	Prairie Pothole Region of Iowa	Waterfront districts on south Lake Michigan and south lake Erie	Central California coast includes twelve adjacent watersheds covering 87,638 ha and rises steeply from sea level to just below 1800 m within a few km from the coast, and experiences a Mediterranean climate, with fire season typically lasting from June to November. Precipitation is dependent on elevation ranging from 65 cm near the coast to over 130 cm at ridge top. Three ecological zones occur within the study area. These zones are comprised of grasslands, coastal sage scrub, chaparral, oak forests, mixed broadleaf evergreen forest, and coniferous forests.
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target	Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030)	No scenarios presented	Climate projections based on the CGCM 3 1 general circulation model of moderate warming (IPCC). The A1B scenario assumes a growing world population that peaks in the mid-century and balanced technological growth.	No scenarios presented	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	Application of existing model

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-142	Doc-238 \| Doc-239 \| Doc-240 \| Doc-241 \| Doc-242 \| Doc-228	None	None	Doc-372 \| Doc-373	Doc-422	Doc-431
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-123 \| EM-124 \| EM-125 \| EM-162 \| EM-164 \| EM-165 \| EM-166 \| EM-170 \| EM-171 \| EM-99 \| EM-119 \| EM-120 \| EM-121	None	None	EM-705 \| EM-704 \| EM-703 \| EM-702 \| EM-700 \| EM-632	EM-886 \| EM-888 \| EM-889 \| EM-890 \| EM-893 \| EM-894 \| EM-895	EM-938

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
EM Temporal Extent em.detail.tempExtentHelp ?	1980-2006	1990-2030	2006-2007, 2010	1982-2060	1987-2007	2022	2005-2008
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	Not applicable	future time	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	Not applicable	discrete	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	6, 10, and 30	Not applicable	1	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Not applicable	Year	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
Bounding Type em.detail.boundingTypeHelp ?	Watershed/Catchment/HUC	Geopolitical	Physiographic or ecological	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Geopolitical	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	East Fork Kaskaskia River watershed basin	The EU-25 plus Switzerland and Norway	Coastal zone surrounding St. Croix	continental United States	CREP (Conservation Reserve Enhancement Program	Great Lakes waterfront	Big Sur region, central California
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	>1,000,000 km^2	100-1000 km^2	>1,000,000 km^2	10,000-100,000 km^2	1000-10,000 km^2.	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
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	area, for pixel or radial feature	map scale, for cartographic feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 km^2	1 km x 1 km	10 m x 10 m	varies	multiple, individual, irregular sites	Not applicable	irregular

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Logic- or rule-based	Analytic	Numeric	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Yes	Yes	Unclear	No	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	Yes	No	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Unclear	No	No	No	No	Yes	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
North America United States Illinois	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia France Lithuania Hungary Switzerland Spain Czech Republic Belgium Norway Finland Denmark Italy Germany Estonia	None	North America United States	North America United States Iowa	North America United States Illinois Minnesota Ohio Wisconsin	North America United States California

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

EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Northern California

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
Centroid Latitude em.detail.ddLatHelp ?	38.69	50.53	17.73	39.8	42.62	42.26	35.96
Centroid Longitude em.detail.ddLongHelp ?	-89.1	7.6	-64.77	-98.55	-93.84	-87.84	-121.43
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland \| Barren	Inland Wetlands \| Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Row crop agriculture in Kaskaskia river basin	Not applicable	Coral reefs	Terrestrial environments including water bodies and coastlines	Wetlands buffered by grassland within agroecosystems	Lake Michigan & Lake Erie waterfront	Coastal watersheds
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 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 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-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Guild or Assemblage	Not applicable	Individual or population, within a species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
None Available	None Available	other snapper Family grouper parrotfish surgeonfish	None Available	Species Blue-winged teal (Anas discors)	None Available	None Available

EnviroAtlas URL

EM-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Agricultural water use (million gallons/day), Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	Hectares of Vegetable Crops	None Available	Dasymetric Allocation of Population, GAP Ecological Systems, Average Annual Precipitation, Total Employment, Employment Rate	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	GAP Ecological Systems, Enabling Conditions	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-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic Entertainment	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	None	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems

<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-97	EM-122	EM-418	EM-653	EM-701	EM-891	EM-940
Rivers and Streams (111) Water (3) Liquid water Quality Indicator	Agroecosystems (22) Flora (5) food crops Stock Indicator	None	None	Wetlands (12) Fauna (4) Duck Stock Indicator	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)