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.

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Comparing:

EM-133
EM-419
EM-650
EM-654
EM-735
EM-893

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
EM Short Name em.detail.shortNameHelp ?	Flood regulation supply-demand, Etropole, Bulgaria	ARIES viewsheds, Puget Sound Region, USA	Sedge Wren density, CREP, Iowa, USA	Forest recreation, Wisconsin, USA	C sequestration in grassland restoration, England	HWB indicator-ADI, Great Lakes, USA
EM Full Name em.detail.fullNameHelp ?	Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria	ARIES (Artificial Intelligence for Ecosystem Services) Scenic viewsheds for homeowners, Puget Sound Region, Washington, USA	Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Forest recreation, Wisconsin, USA	Carbon sequestration in grassland diversity restoration, England	Human well being indicator- Area Deprivation Index (ADI) , Great Lakes waterfront, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	ARIES	None	None	None	US EPA
EM Source Document ID	248	302	372	376	396	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers
Document Author em.detail.documentAuthorHelp ?	Nedkov, S., Burkhard, B.	Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.	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	Qiu, J. and M. G. Turner	De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick
Document Year em.detail.documentYearHelp ?	2012	2014	2010	2013	2011	None
Document Title em.detail.sourceIdHelp ?	Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria	From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Spatial interactions among ecosystem services in an urbanizing agricultural watershed	Additional carbon sequestration benefits of grassland diversity restoration	Human well-being and natural capital indictors for Great Lakes waterfront revitalization
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed but unpublished (explain in Comment)
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript	Journal manuscript submitted or in review

Software and Access

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	http://aries.integratedmodelling.org/	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Stoyan Nedkov	Ken Bagstad	David Otis	Monica G. Turner	Gerlinde B. De Deyn	Ted Angradi
Contact Address	National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria	Geosciences and Environmental Change Science Center, US Geological Survey	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Not reported	Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804
Contact Email	snedkov@abv.bg	kjbagstad@usgs.gov	dotis@iastate.edu	turnermg@wisc.edu	g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com	tedangradi@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa."	ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "Within a given viewshed, our models quantified the contribution of viewshed source features such as mountains and water bodies and sinks that detract from view quality, including obstructions or visual blight such as industrial or commercial development. Source, sink, and use locations were linked by a flow model that computed visibility along lines of sight from use locations to scenic viewshed features. The model includes a distance decay function that accounts for changes with distance in the value of views. We then computed the ratio of actual to theoretical provision of scenic views to compare the values accruing to homeowners relative to those for the entire landscape."	ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) e^(-0.7982511 + 0.0285891 bbspath + 0.0105094 *grass400)	AUTHOR'S DESCRIPTION (from Supporting Information): "Forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area. Several assumptions were made for this assessment approach: larger areas and places with more recreational opportunities would provide more recreational service; areas near large population centers would be visited and used more than remote areas; and proximity to major roads would increase access and thus recreational use of an area… we quantified forest recreation service for each 30-m grid cells using the equation below: FRSi = Ai Σ(Oppti + Popi +Roadi), where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5,200) to a range of 0–100, with 0 representing no forest recreation service and 100 representing highest service.	ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…"	ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified	None identified	None identified
Biophysical Context	Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak.	No additional description provided	Prairie pothole region of north-central Iowa	No additional description provided	Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility.	Waterfront districts on south Lake Michigan and south lake Erie
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	Additional benefits due to biodiversity restoration practices	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	Application of existing model ? Comment: Models developed by Quamen (2007).	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-133	EM-419	EM-650	EM-654	EM-735	EM-893
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-248	Doc-303 \| Doc-305	Doc-372	None	None	Doc-422
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-130 \| EM-132	None	EM-652 \| EM-651 \| EM-649 \| EM-648	None	None	EM-886 \| EM-888 \| EM-889 \| EM-890 \| EM-891 \| EM-894 \| EM-895

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	1992-2006	1992-2007	2000-2006	1990-2007	2022
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-133	EM-419	EM-650	EM-654	EM-735	EM-893
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Watershed/Catchment/HUC	Other	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Municipality of Etropole	Puget Sound Region	CREP (Conservation Reserve Enhancement Program) wetland sites	Yahara Watershed, Wisconsin	Colt Park meadows, Ingleborough National Nature Reserve, northern England	Great Lakes waterfront
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10,000-100,000 km^2	1-10 km^2	1000-10,000 km^2.	<1 ha	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Distributed by land cover and soil type polygons	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	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	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed by irregular land cover and soil type polygons	200m x 200m	multiple, individual, irregular shaped sites	30m x 30m	3 m x 3 m	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	stochastic	deterministic	deterministic	stochastic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Unclear	No	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Unclear	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	No	No	No	Yes
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
Europe Bulgaria	North America United States Washington	North America United States Iowa	North America United States Wisconsin	Europe United Kingdom	North America United States Illinois Minnesota Ohio Wisconsin

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

EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
Centroid Latitude em.detail.ddLatHelp ?	42.8	48	42.62	43.1	54.2	42.26
Centroid Longitude em.detail.ddLongHelp ?	24	-123	-93.84	-89.4	-2.35	-87.84
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Provided	Provided	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Lakes and Ponds \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland	Lakes and Ponds \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands \| Agroecosystems \| Grasslands	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Mountainous flood-prone region	Terrestrial environment surrounding a large estuary	Grassland buffering inland wetlands set in agricultural land	Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape	fertilized grassland (historically hayed)	Lake Michigan & Lake Erie waterfront
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale 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-133	EM-419	EM-650	EM-654	EM-735	EM-893
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Species	Not applicable	Community	Not applicable

Taxonomic level and name of organisms or groups identified

EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
None Available	None Available	Species Sedge Wren (Cistothorus platensis)	None Available	None Available	None Available

EnviroAtlas URL

EM-133	EM-419	EM-650	EM-654	EM-735	EM-893
None Available	GAP Ecological Systems, Waterbody area	GAP Ecological Systems	Dasymetric Allocation of Population	None Available	GAP Ecological Systems, Enabling Conditions

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-133	EM-419	EM-650	EM-654	EM-735	EM-893
[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Intellectual and representative interactions Aesthetic Entertainment	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	None

<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-133	EM-419	EM-650	EM-654	EM-735	EM-893
Rivers and Streams (111) Composite (8) Regulation of extreme events Extreme Event Indicator Forests (21) Composite (8) Regulation of extreme events Extreme Event Indicator Grasslands (23) Composite (8) Regulation of extreme events Extreme Event Indicator Scrubland/Shrubland (24) Composite (8) Regulation of extreme events Extreme Event Indicator Lakes and Ponds (112) Composite (8) Regulation of extreme events Extreme Event Indicator Agroecosystems (22) Composite (8) Regulation of extreme events Extreme Event Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Site Indicator Forests (21) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Site Indicator Lakes and Ponds (112) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Site Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	None	None	None

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Comment:

Comment:

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

EM Modeling Approach

Comment:

EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

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)

<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)