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-71
EM-306
EM-428
EM-700
EM-895
EM-997

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
EM Short Name em.detail.shortNameHelp ?	Community flowering date, Central French Alps	Urban Temperature, Baltimore, MD, USA	Retained rainwater, Guánica Bay, Puerto Rico	Mallard recruits, CREP wetlands, Iowa, USA	HWB indicator-College degree, Great Lakes, USA	CEASAR and TRACER models, EU
EM Full Name em.detail.fullNameHelp ?	Community weighted mean flowering date, Central French Alps	Urban Air Temperature Change, Baltimore, MD, USA	Retained rainwater, Guánica Bay, Puerto Rico, USA	Mallard duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Human well being indicator-College degree, Great Lakes waterfront, USA	Modelling remediation scenarios in historical mining catchments
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	i-Tree \| USDA Forest Service	US EPA	None	US EPA	None
EM Source Document ID	260	217	338	372 ? Comment: Document 373 is a secondary source for this EM.	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers	467
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I.	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick	Gamarra, J. G., Brewer, P. A., Macklin, M. G., & Martin, K.
Document Year em.detail.documentYearHelp ?	2011	2016	2017	2010	None	2014
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Human well-being and natural capital indictors for Great Lakes waterfront revitalization	Modelling remediation scenarios in historical mining catchments
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed but unpublished (explain in Comment)	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Journal manuscript submitted or in review	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Gordon M. Heisler	Susan H. Yee	David Otis	Ted Angradi	Javier G. P. Gamarra
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804	Institute of Biological, Environmental and Rural Sciences, Aberystwyth, SY23 3DB, UK
Contact Email	sandra.lavorel@ujf-grenoble.fr	gheisler@fs.fed.us	yee.susan@epa.gov	dotis@iastate.edu	tedangradi@gmail.com	jgg@aber.ac.uk

EM Description

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
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."	An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation."	AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…"	ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2Rn where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = HZB/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).	ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. "	Local remediation measures, particularly those undertaken in historical mining areas, can often be ineffective or even deleterious because erosion and sedimentation processes operate at spatial scales beyond those typically used in point-source remediation. Based on realistic simulations of a hybrid landscape evolution model combined with stochastic rainfall generation, we demonstrate that similar remediation strategies may result in differing effects across three contrasting European catchments depending on their topographic and hydrologic regimes. Based on these results, we propose a conceptual model of catchment-scale remediation effectiveness based on three basic catchment characteristics: the degree of contaminant source coupling, the ratio of contaminated to non-contaminated sediment delivery, and the frequency of sediment transport events.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Meeting water demands for agriculture and domestic purposes.	None identified	None identified	None identified
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site.	No additional descriptions provided	Prairie Pothole Region of Iowa	Waterfront districts on south Lake Michigan and south lake Erie	Rver system catchments associated with mining sites distributed across Europe
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	Application of existing model

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

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	Doc-220 \| Doc-219 \| Doc-218	None	Doc-372 \| Doc-373	Doc-422	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	None	None	EM-705 \| EM-704 \| EM-703 \| EM-702 \| EM-701 \| EM-632	EM-886 \| EM-888 \| EM-889 \| EM-890 \| EM-891 \| EM-893 \| EM-894	EM-998

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2008	May 5-Sept 30 2006	2006 - 2012	1987-2007	2022	1800-2100
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	Not applicable	Not applicable	Not applicable	both
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	Not applicable	Not applicable	Not applicable	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Hour	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Geopolitical	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Baltimore, MD	Guanica Bay watershed	CREP (Conservation Reserve Enhancement Program	Great Lakes waterfront	Ystwyth, Ampoi, and Naracauli
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	100-1000 km^2	1000-10,000 km^2.	10,000-100,000 km^2	1000-10,000 km^2.	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
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 lumped (in all 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	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	map scale, for cartographic feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	10m x 10m	30 m x 30 m	multiple, individual, irregular sites	Not applicable	Not reported

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Machine learning	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	No	Unclear	No	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	No	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R squared \| 45.2 \| Not applicable	Adjusted R squared \| 0.50 \| unitless	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	No	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	Yes	Unclear
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-71	EM-306	EM-428	EM-700	EM-895	EM-997
Europe France	North America United States Maryland	North America U.S. Caribbean Territories	North America United States Iowa	North America United States Illinois Minnesota Ohio Wisconsin	Europe

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

EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
Centroid Latitude em.detail.ddLatHelp ?	45.05	39.28	17.96	42.62	42.26	52.5 ? Comment: There are 3 locations provided in this study with latitudes of 52.5, 46, and 40 as well as longitudes of -4, 10, and 25, respectively.
Centroid Longitude em.detail.ddLongHelp ?	6.4	-76.62	-67.02	-93.84	-87.84	-4
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	None provided
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified) \| Created Greenspace \| Atmosphere	Inland Wetlands \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland \| Barren	Inland Wetlands \| Agroecosystems \| Grasslands	Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland \| Barren \| Tundra \| Ice and Snow \| Atmosphere	Rivers and Streams \| Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows.	Urban landscape and surrounding area	13 LULC were used	Wetlands buffered by grassland within agroecosystems	Lake Michigan & Lake Erie waterfront	Watershed catchment
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	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	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-71	EM-306	EM-428	EM-700	EM-895	EM-997
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Not applicable	Individual or population, within a species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
None Available	None Available	None Available	Species Mallard (Anas platyrhynchos)	None Available	None Available

EnviroAtlas URL

EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
None Available	Average Annual Precipitation, Percent Impervious Area	The Watershed Boundary Dataset (WBD)	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	Dasymetric Allocation of Population, GAP Ecological Systems, Enabling Conditions	Average Annual Precipitation

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	None	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Mediation of waste, toxics and other nuisances By natural chemical and physical processes Not applicable [Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Dilution by atmosphere, freshwater and marine ecosystems

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

EM-71	EM-306	EM-428	EM-700	EM-895	EM-997
None	Atmosphere Atmosphere (1) Air Site Characteristic Indicator	None	Wetlands (12) Fauna (4) Duck Stock Indicator	None	Rivers and Streams (111) Water (3) Liquid water Flow Indicator

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

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)

Comment:

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)