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-91
EM-112
EM-113
EM-416
EM-492
EM-682
EM-719

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
EM Short Name em.detail.shortNameHelp ?	RHyME2, Upper Mississippi River basin, USA	InVEST nutrient retention, Hood Canal, WA, USA	Wetland conservation for birds, Midwestern USA	Sed. denitrification, St. Louis River, MN/WI, USA	EnviroAtlas - Restorable wetlands	WTP for a beach day, Massachusetts, USA	Seed mix for native plant establishment, IA, USA
EM Full Name em.detail.fullNameHelp ?	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	Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA	US EPA EnviroAtlas - Percent potentially restorable wetlands, USA	Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA	Cost-effective seed mix design for native plant establishment, Iowa, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	InVEST	None	US EPA	US EPA \| EnviroAtlas	US EPA	None
EM Source Document ID	123	205	122	333	262	386	394
Document Author em.detail.documentAuthorHelp ?	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.	Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill	US EPA Office of Research and Development - National Exposure Research Laboratory	Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta	Meissen, J.
Document Year em.detail.documentYearHelp ?	2013	2013	2011	2014	2013	2018	2018
Document Title em.detail.sourceIdHelp ?	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	Sediment nitrification and denitrification in a Lake Superior estuary	EnviroAtlas - National	Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts	Cost-effective seed mix design and first-year management
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 on US EPA EnviroAtlas website	Published journal manuscript	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	https://www.epa.gov/enviroatlas	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Liem Tran	J.E. Toft	Wayne Thogmartin, USGS	Brent J. Bellinger	EnviroAtlas Team	Kate K, Mulvaney	Justin Meissen
Contact Address	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	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	Not reported	Not reported	Tallgrass Prairie Center, University of Northern Iowa
Contact Email	ltran1@utk.edu	jetoft@stanford.edu	wthogmartin@usgs.gov	bellinger.brent@epa.ogv	enviroatlas@epa.gov	Mulvaney.Kate@EPA.gov	Not reported

EM Description

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
Summary Description em.detail.summaryDescriptionHelp ?	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."	ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ? Comment: BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model.	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.	AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Not reported	Land use change	Strategic habitat conservation by USFW for Wetland Conservation Act funding	None identified	None Identified	Economic value of protecting coastal beach water quality from contamination caused closures.	Seed mix design and management practices for native plant restoration
Biophysical Context	No additional description provided	No additional description provided	Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods	Estuarine system	No additional description provided	Four separate beaches within the community of Barnstable	The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site.
EM Scenario Drivers em.detail.scenarioDriverHelp ?	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	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
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 (multiple runs exist) View EM Runs
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-123	Doc-309 \| Doc-338	Doc-169 \| Doc-170 \| Doc-171 \| Doc-172 \| Doc-173 \| Doc-174 \| Doc-175	None	None	Doc-386 \| Doc-387	Doc-395
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-363 \| EM-438	None	None	None	EM-684 \| EM-685 \| EM-683 \| EM-686	EM-728

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
EM Temporal Extent em.detail.tempExtentHelp ?	1987-1997	2005-7; 2035-45	2007	2011 - 2012	2006-2013	July 1, 2011 to June 31, 2016	2015-2017
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent
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	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Year

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
Bounding Type em.detail.boundingTypeHelp ?	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Physiographic or ecological	Watershed/Catchment/HUC	Geopolitical	Physiographic or ecological	Other
Spatial Extent Name em.detail.extentNameHelp ?	Upper Mississippi River basin; St. Croix River Watershed	Hood Canal	Upper Mississippi River and Great Lakes Region	St. Louis River estuary	conterminous United States	Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach)	Iowa State University Northeast Research and Demonstration Farm
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100,000-1,000,000 km^2	100,000-1,000,000 km^2	>1,000,000 km^2	10-100 km^2	>1,000,000 km^2	10-100 ha	<1 ha

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
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 lumped (in all 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 ?	NHDplus v1	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	length, for linear feature (e.g., stream mile)	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	NHDplus v1	30 m x 30 m	1 ha	Not applicable	irregular	by beach site	20 ft x 28 ft

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Other or unclear (comment)	Analytic	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Other or unclear (comment)	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Yes	No	No	No	Yes	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	No	No	Yes	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R^2 \| 0.923 \| none Mean Square Error \| 25.1 \| %	None	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	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	No	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	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	No	Yes ? Comment: p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables.	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	No	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-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
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 Minnesota Wisconsin	North America United States	North America United States Massachusetts	North America United States Iowa

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

EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	None	None	None	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
Centroid Latitude em.detail.ddLatHelp ?	42.5	47.8	42.05	46.75	39.5	41.64	42.93
Centroid Longitude em.detail.ddLongHelp ?	-90.63	-122.7	-88.6	-92.08	-98.35	-70.29	-92.57
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Provided

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	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	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Agroecosystems	Near Coastal Marine and Estuarine	Agroecosystems \| Grasslands
Specific Environment Type em.detail.specificEnvTypeHelp ?	None	glacier-carved saltwater fjord	Not reported	Freshwater estuary	Terrestrial	Saltwater beach	Research farm in historic grassland
EM Ecological Scale em.detail.ecoScaleHelp ?	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 corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Species	Not applicable	Not applicable	Not applicable	Community

Taxonomic level and name of organisms or groups identified

EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
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	None Available	None Available

EnviroAtlas URL

EM-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
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	Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	GAP Ecological Systems, The Watershed Boundary Dataset (WBD)	None Available	GAP Ecological Systems

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-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
[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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Pollination and seed dispersal

<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-91	EM-112	EM-113	EM-416	EM-492	EM-682	EM-719
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	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Site Indicator	Grasslands (23) Composite (8) Presence of environmental class/subclass Quality Indicator

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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)