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-71
EM-121
EM-131
EM-414
EM-418
EM-632
EM-939

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
EM Short Name em.detail.shortNameHelp ?	Community flowering date, Central French Alps	Landscape importance for recreation, Europe	InVEST marine water quality, Hood Canal, WA, USA	SAV occurrence, St. Louis River, MN/WI, USA	SIRHI, St. Croix, USVI	Waterfowl pairs, CREP wetlands, Iowa, USA	ESTIMAP- Recreation, Europe
EM Full Name em.detail.fullNameHelp ?	Community weighted mean flowering date, Central French Alps	Landscape importance for recreation, Europe	InVEST (Integrated Valuation of Envl. Services and Tradeoffs) marine water quality, Hood Canal, WA, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	SIRHI (SImplified Reef Health Index), St. Croix, USVI	Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	ESTIMAP- Recreation, Europe
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	InVEST	US EPA	US EPA	None	None
EM Source Document ID	260	228	205	330	335	372	432
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Haines-Young, R., Potschin, M. and Kienast, F.	Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	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	Zulian, G., Parrachini, M.L., Maes, J.,
Document Year em.detail.documentYearHelp ?	2011	2012	2013	2013	2014	2010	2013
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	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	ESTIMAP: Ecosystem services mapping at the European scale
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 and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	Not applicable	N.A.
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Marion Potschin	J.E. Toft	Ted R. Angradi	Susan H. Yee	David Otis	Grazia Zulian
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	Not reported	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 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	Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy
Contact Email	sandra.lavorel@ujf-grenoble.fr	marion.potschin@nottingham.ac.uk	jetoft@stanford.edu	angradi.theodore@epa.gov	yee.susan@epa.gov	dotis@iastate.edu	grazia.zulian@jrc.ec.europa.e

EM Description

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables."	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 methods are explored in relation to mapping and assessing … “Recreation” ... The potential to deliver services is assumed to be influenced by land-use ... and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Recreation… is broadly defined as all areas where landscape properties are favourable for active recreation purposes."	Marine Water Quality Model. Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We used outputs from the freshwater models as inputs to the marine water quality model.We adapted a box model that has been successfully applied in Puget Sound (Babson et al., 2006; Sutherland et al., 2011) to simulate seasonal and interannual variations in salinity, water temperature, and nitrates in the Canal." (p. 4)	ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."	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: "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: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)"	AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Land use change	None identified	None identified	None identified	None
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	No additional description provided	No additional description provided	submerged aquatic vegetation	No additional description provided	Prairie pothole region of north-central Iowa	Continential Scale
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	future land use and land cover; Climate change	No scenarios presented	No scenarios presented	No scenarios presented	N.A.

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist)	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	Application of existing model	New or revised model	Application of existing 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-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	Doc-231 \| Doc-228	None	None	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-99 \| EM-119 \| EM-120 \| EM-162 \| EM-164 \| EM-165 \| EM-122 \| EM-123 \| EM-124 \| EM-125 \| EM-170 \| EM-171	None	None	None	EM-705 \| EM-703 \| EM-702 \| EM-701 \| EM-700	EM-941

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2008	2000	varies by run, see runs for values	2010 - 2012	2006-2007, 2010	2002-2007	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-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Physiographic or ecological	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)	No location (no locational reference given)
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	The EU-25 plus Switzerland and Norway	Hood Canal	St. Louis River Estuary	Coastal zone surrounding St. Croix	CREP (Conservation Reserve Enhancement Program) wetland sites	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	>1,000,000 km^2	100-1000 km^2	10-100 km^2	100-1000 km^2	1-10 km^2	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
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) ? Comment: BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively.	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	1 km x 1 km	Not reported	0.07 m^2 to 0.70 m^2	10 m x 10 m	multiple, individual, irregular shaped sites	Pixel size

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Logic- or rule-based	Analytic	Analytic	Analytic	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Other or unclear (comment)	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Yes	Yes	Unclear	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	Yes	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R squared \| 45.2 \| Not applicable	None	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	Yes	Yes	Unclear	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	Yes
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Unclear

EM Locations, Environments, Ecology

Location of EM Application

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

EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
Europe France	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	North America United States Washington	North America United States Minnesota Wisconsin	None	North America United States Iowa	Europe

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

EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
Centroid Latitude em.detail.ddLatHelp ?	45.05	50.53	47.8	46.72	17.73	42.62	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	7.6	-122.7	-96.13	-64.77	-93.84	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	NAD83	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows.	Not applicable	glacier-carver saltwater fjord	Freshwater estuarine system	Coral reefs	Wetlands buffered by grassland set in agricultural land	Not applicable
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale 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-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Not applicable	Not applicable	Guild or Assemblage	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
None Available	None Available	None Available	None Available	other snapper Family grouper parrotfish surgeonfish	Species Wood Duck (Aix sponsa) Northern Pintail (Anas acuta) Mallard (Anas platyrhynchos) Blue-winged Teal (Anas discors) Northern Shoveler (Anas clypeata) Gadwall (Anas strepera)	None Available

EnviroAtlas URL

EM-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
None Available	Percent GAP Status 1 & 2	None Available	Average Annual Precipitation	None Available	None Available	Dasymetric Allocation of Population, Ecosystem Markets: Imperiled Species and Habitats, Waterbody area

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-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
None	[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] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of salt waters Mediation of flows Liquid flows Hydrological cycle and water flow maintenance	[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] Provisioning Nutrition Biomass Wild animals and their outputs	[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

<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-71	EM-121	EM-131	EM-414	EM-418	EM-632	EM-939
None	Rivers and Streams (111) Composite (8) Presence of environmental class/subclass Quality Indicator Barren / Rock and Sand (28) Composite (8) Presence of environmental class/subclass Quality Indicator Forests (21) Composite (8) Presence of environmental class/subclass Quality Indicator Tundra (25) Composite (8) Presence of environmental class/subclass Quality Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Quality Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Quality Indicator Wetlands (12) Composite (8) Presence of environmental class/subclass Quality Indicator Lakes and Ponds (112) Composite (8) Presence of environmental class/subclass Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	None	None	Wetlands (12) Fauna (4) Duck Stock Indicator	None