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-81
EM-91
EM-112
EM-113
EM-492
EM-682

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
EM Short Name em.detail.shortNameHelp ?	Cultural ES and plant traits, Central French Alps	RHyME2, Upper Mississippi River basin, USA	InVEST nutrient retention, Hood Canal, WA, USA	Wetland conservation for birds, Midwestern USA	EnviroAtlas - Restorable wetlands	WTP for a beach day, Massachusetts, USA
EM Full Name em.detail.fullNameHelp ?	Cultural ecosystem service estimated from plant functional traits, Central French Alps	RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA	InVEST (Integrated Valuation of Envl. Services and Tradeoffs) nutrient retention, Hood Canal, WA, USA	Prioritizing wetland conservation for birds, Midwestern USA	US EPA EnviroAtlas - Percent potentially restorable wetlands, USA	Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	US EPA	InVEST	None	US EPA \| EnviroAtlas	US EPA
EM Source Document ID	260	123	205	122	262	386
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C.	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.	Thogmartin, W. A., Potter, B. A. and Soulliere, G. J.	US EPA Office of Research and Development - National Exposure Research Laboratory	Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta
Document Year em.detail.documentYearHelp ?	2011	2013	2013	2011	2013	2018
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads	From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA	Bridging the conservation design and delivery gap for wetland bird habitat maintenance and restoration in the midwestern United States	EnviroAtlas - National	Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts
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
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published on US EPA EnviroAtlas website	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	https://www.epa.gov/enviroatlas	Not applicable
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Liem Tran	J.E. Toft	Wayne Thogmartin, USGS	EnviroAtlas Team	Kate K, Mulvaney
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA	The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA	Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603	Not reported	Not reported
Contact Email	sandra.lavorel@ujf-grenoble.fr	ltran1@utk.edu	jetoft@stanford.edu	wthogmartin@usgs.gov	enviroatlas@epa.gov	Mulvaney.Kate@EPA.gov

EM Description

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
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: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions."	ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin)	InVEST Nutrient Retention 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 modelled discharge and total nitrogen for the 153 perennial sub-watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparameterized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)" (2) "We used the InVEST Nutrient Retention model to quantify the total nitrogen load for each subwatershed. Inputs to the Nutrient Retention model include water yield, land use and land cover, and nutrient loading and filtration rates (Table 1; Conte et al., 2011; Tallis et al., 2011). The nutrient model quantifies natural and anthropogenic sources of total nitrogen within each subwatershed, allowing managers to identify subwatersheds potentially at risk of contributing excessive nitrogen loads given the predicted development and climate future." ( P. 4)	ABSTRACT: "The U.S. Fish and Wildlife Service’s adoption of Strategic Habitat Conservation is intended to increase the effectiveness and efficiency of conservation delivery by targeting effort in areas where biological benefits are greatest. Conservation funding has not often been allocated in accordance with explicit biological endpoints, and the gap between conservation design (the identification of conservation priority areas) and delivery needs to be bridged to better meet conservation goals for multiple species and landscapes. We introduce a regional prioritization scheme for North American Wetlands Conservation Act funding which explicitly addresses Midwest regional goals for wetland-dependent birds. We developed decision-support maps to guide conservation of breeding and non-breeding wetland bird habitat. This exercise suggested ~55% of the Midwest consists of potential wetland bird habitat, and areas suited for maintenance (protection) were distinguished from those most suited to restoration. Areas with greater maintenance focus were identified for central Minnesota, southeastern Wisconsin, the Upper Mississippi and Illinois rivers, and the shore of western Lake Erie and Saginaw Bay. The shores of Lakes Michigan and Superior accommodated fewer waterbird species overall, but were also important for wetland bird habitat maintenance. Abundant areas suited for wetland restoration occurred in agricultural regions of central Illinois, western Iowa, and northern Indiana and Ohio. Use of this prioritization scheme can increase effectiveness, efficiency, transparency, and credibility to land and water conservation efforts for wetland birds in the Midwestern United States."	DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map."	ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "We used existing studies in a meta-analysis to estimate appropriate benefit transfer values of consumer surplus per beach visit for Barnstable. The studies we include in the model are for beaches across the United States, allowing the metaregression model to be more broadly applicable to other beaches and for values to be adjusted based on appropriate site attributes...To identify relevant studies, we selected 25 studies of beach use and swimming from the Recreation Use Values Database (RUVD), where consumer surplus values are presented as value per day in 2016 dollars...We added beach length and history of closures to contextualize the model for our application by proxying water quality and site quality." Equation 1, page 11, provides the meta-regression.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	Not reported	Land use change	Strategic habitat conservation by USFW for Wetland Conservation Act funding	None Identified	Economic value of protecting coastal beach water quality from contamination caused closures.
Biophysical Context	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	No additional description provided	No additional description provided	Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods	No additional description provided	Four separate beaches within the community of Barnstable
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Future land use and land cover; climate change	Conservation efforts for: marsh-wetland breeding birds, regional marsh and open-water for non-breeding birds, mudflat/shallows for birds during non-breeding period.	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	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

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

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-123	Doc-309 \| Doc-338	Doc-169 \| Doc-170 \| Doc-171 \| Doc-172 \| Doc-173 \| Doc-174 \| Doc-175	None	Doc-386 \| Doc-387
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-82 \| EM-83	None	EM-363 \| EM-438	None	None	EM-684 \| EM-685 \| EM-683 \| EM-686

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	1987-1997	2005-7; 2035-45	2007	2006-2013	July 1, 2011 to June 31, 2016
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	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
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	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

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Physiographic or ecological	Geopolitical	Physiographic or ecological
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Upper Mississippi River basin; St. Croix River Watershed	Hood Canal	Upper Mississippi River and Great Lakes Region	conterminous United States	Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach)
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	100,000-1,000,000 km^2	100,000-1,000,000 km^2	>1,000,000 km^2	>1,000,000 km^2	10-100 ha

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
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 distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	NHDplus v1	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	length, for linear feature (e.g., stream mile)
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	NHDplus v1	30 m x 30 m	1 ha	irregular	by beach site

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Other or unclear (comment)	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Other or unclear (comment)	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Yes	No	No	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	No	No	Yes
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	R^2 \| 0.923 \| none Mean Square Error \| 25.1 \| %	None	None	None	R-squared \| 0.486 \| Not reported Adjusted R-squared \| 0.445 \| Not reported Residual Std. Error \| 0.971 \| Not reported F-statistic \| 11.9 \| Not reported AIC \| 276.47 \| Not reported
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Yes	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No ? Comment: Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify.	Yes	No	No	Yes ? Comment: p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables.
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	No	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-81	EM-91	EM-112	EM-113	EM-492	EM-682
Europe France	North America United States Indiana Illinois Missouri South Dakota Minnesota Idaho Wisconsin	North America United States Washington	North America United States Iowa Indiana Illinois Missouri Minnesota Ohio Kansas Wisconsin Michigan Nebraska	North America United States	North America United States Massachusetts

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

EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	None	None	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
Centroid Latitude em.detail.ddLatHelp ?	45.05	42.5	47.8	42.05	39.5	41.64
Centroid Longitude em.detail.ddLongHelp ?	6.4	-90.63	-122.7	-88.6	-98.35	-70.29
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Terrestrial Environment (sub-classes not fully specified) \| Agroecosystems \| Atmosphere	Near Coastal Marine and Estuarine	Inland Wetlands	Agroecosystems	Near Coastal Marine and Estuarine
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows.	None	glacier-carved saltwater fjord	Not reported	Terrestrial	Saltwater beach
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser than that of the Environmental Sub-class	Ecosystem	Ecological scale is finer than that of 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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Not applicable	Species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
None Available	None Available	None Available	Species Tundra Swan (eastern) Wilson's Snipe Black Tern Sanderling Black-crowned Night-Heron Wilson's Phalarope American Black Duck Upland Sandpiper Mallard Duck Common Tern Piping Plover Wood Duck Canvasback American Golden Plover Blue-winged Teal Lesser Scaup Yellow Rail King Rail Dunlin American Woodcock Short-billed Dowitcher Killdeer	None Available	None Available

EnviroAtlas URL

EM-81	EM-91	EM-112	EM-113	EM-492	EM-682
GAP Ecological Systems	The National Hydrography Dataset (NHD), National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	GAP Ecological Systems, The Watershed Boundary Dataset (WBD)	None Available

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-81	EM-91	EM-112	EM-113	EM-492	EM-682
[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 Intellectual and representative interactions Aesthetic Heritage, cultural	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	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

<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-81	EM-91	EM-112	EM-113	EM-492	EM-682
None	None	None	Wetlands (12) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator Fauna (4) Birds Stock Indicator	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Site Indicator