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-59
EM-71
EM-87
EM-93
EM-418
EM-419
EM-446

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
EM Short Name em.detail.shortNameHelp ?	EnviroAtlas-Air pollutant removal	Community flowering date, Central French Alps	Area & hotspots of soil accumulation, South Africa	Stream nitrogen removal, Mississippi R. basin, USA	SIRHI, St. Croix, USVI	ARIES viewsheds, Puget Sound Region, USA	CRPI, St. Croix, USVI
EM Full Name em.detail.fullNameHelp ?	US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA	Community weighted mean flowering date, Central French Alps	Area and hotspots of soil accumulation, South Africa	Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA	SIRHI (SImplified Reef Health Index), St. Croix, USVI	ARIES (Artificial Intelligence for Ecosystem Services) Scenic viewsheds for homeowners, Puget Sound Region, Washington, USA	CRPI (Coral Reef Protection Index, St. Croix, USVI
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA \| EnviroAtlas \| i-Tree ? Comment: EnviroAtlas uses an application of the i-Tree Eco model.	EU Biodiversity Action 5	None	US EPA	US EPA	ARIES	US EPA
EM Source Document ID	223	260	271	52	335	302	335
Document Author em.detail.documentAuthorHelp ?	US EPA Office of Research and Development - National Exposure Research Laboratory	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Hill, B. and Bolgrien, D.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.	Yee, S. H., Dittmar, J. A., and L. M. Oliver
Document Year em.detail.documentYearHelp ?	2013	2011	2008	2011	2014	2014	2014
Document Title em.detail.sourceIdHelp ?	EnviroAtlas - Featured Community	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Mapping ecosystem services for planning and management	Nitrogen removal by streams and rivers of the Upper Mississippi River basin	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI
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 on US EPA EnviroAtlas website	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	Not applicable	Not applicable	http://aries.integratedmodelling.org/	Not applicable
Contact Name em.detail.contactNameHelp ?	EnviroAtlas Team	Sandra Lavorel	Benis Egoh	Brian Hill	Susan H. Yee	Ken Bagstad	Susan H. Yee
Contact Address	Not reported	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Geosciences and Environmental Change Science Center, US Geological Survey	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA
Contact Email	enviroatlas@epa.gov	sandra.lavorel@ujf-grenoble.fr	Not reported	hill.brian@epa.gov	yee.susan@epa.gov	kjbagstad@usgs.gov	yee.susan@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
Summary Description em.detail.summaryDescriptionHelp ?	The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant."	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."	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover."	ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/kmd in the Upper Mississippi River to 0.01–54 kg/ kmd in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/kmd ), followed by the Ohio and Missouri Rivers (0.01–236 kg/kmd ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/kmd in the Upper Mississippi River to 0.001–1.43 kg/kmd in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati	ABSTRACT: "...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: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...An alternative index has been developed specifically for coral reefs, the Coral Reef Protection Index (CRPI), that accounts for the continuity of the reef and distance from shore in addition to reef habitat type (Burke et al., 2008): CRPI = ((Reef type + Reef distribution + Reef distance)/10) x 4 where the scaled magnitude of coastal protection due to each factor ranges from 0 (no protection) to 4 (very high protection; Table 2)."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	Not applicable	None identified	None identified	None identified
Biophysical Context	No additional description provided	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	Agricultural landuse , 1st-10th order streams	No additional description provided	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Not applicable	No scenarios presented	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	New or revised model	New or revised model	Application of existing 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-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-345	Doc-260 \| Doc-269	Doc-271	Doc-154 \| Doc-155	None	Doc-303 \| Doc-305	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-85 \| EM-86 \| EM-88	None	None	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
EM Temporal Extent em.detail.tempExtentHelp ?	2008-2010	2007-2008	Not reported	2000-2008	2006-2007, 2010	1992-2006	2006-2007, 2010
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Hour	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Geopolitical	Watershed/Catchment/HUC	Physiographic or ecological	Watershed/Catchment/HUC	Physiographic or ecological
Spatial Extent Name em.detail.extentNameHelp ?	Durham NC and vicinity	Central French Alps	South Africa	Upper Mississippi, Ohio and Missouri River sub-basins	Coastal zone surrounding St. Croix	Puget Sound Region	Coastal zone surrounding St. Croix
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10-100 km^2	>1,000,000 km^2	>1,000,000 km^2	100-1000 km^2	10,000-100,000 km^2	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Spatial grain type is census block group.	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 ?	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)	length, for linear feature (e.g., stream mile)	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	irregular	20 m x 20 m	Distributed across catchments with average size of 65,000 ha	1 km	10 m x 10 m	200m x 200m	10 m x 10 m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	None	Bayesian statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
Model Calibration Reported? em.detail.calibrationHelp ?	Unclear	No	No	No	Yes	No	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	No	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	R squared \| 45.2 \| Not applicable	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	Yes	No	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Yes	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	Unclear	No	No	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	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-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
North America United States North Carolina	Europe France	Africa South Africa	North America United States New York Montana Indiana Kentucky Minnesota Kansas Alabama Nebraska Pennsylvania Iowa Colorado Illinois Missouri Ohio Georgia	None	North America United States Washington	None

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

EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
Centroid Latitude em.detail.ddLatHelp ?	35.99	45.05	-30	36.98	17.73	48	17.73
Centroid Longitude em.detail.ddLongHelp ?	-78.96	6.4	25	-89.13	-64.77	-123	-64.77
Centroid Datum em.detail.datumHelp ?	None provided	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Created Greenspace \| Atmosphere	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams	Near Coastal Marine and Estuarine	Lakes and Ponds \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine
Specific Environment Type em.detail.specificEnvTypeHelp ?	Urban and vicinity	Subalpine terraces, grasslands, and meadows.	Not applicable	Not applicable	Coral reefs	Terrestrial environment surrounding a large estuary	Coral reefs
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Not applicable	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Community	Not applicable	Not applicable	Guild or Assemblage	Not applicable	Community

Taxonomic level and name of organisms or groups identified

EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
None Available	None Available	None Available	None Available	other snapper Family grouper parrotfish surgeonfish	None Available	None Available

EnviroAtlas URL

EM-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
Average Annual Precipitation	None Available	None Available	National Hydrography Dataset Plus (NHD PlusV2), Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	None Available	GAP Ecological Systems, Waterbody area	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-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
[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 biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters	[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] Intellectual and representative interactions Aesthetic Entertainment	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection

<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-59	EM-71	EM-87	EM-93	EM-418	EM-419	EM-446
Atmosphere Atmosphere (1) Air Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	None	None	Rivers and Streams (111) Water (3) Liquid water Quality Indicator	None	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	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator