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-193
EM-260
EM-414
EM-493
EM-541
EM-1010

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
EM Short Name em.detail.shortNameHelp ?	Cultural ecosystem services, Bilbao, Spain	Coral taxa and land development, St.Croix, VI, USA	SAV occurrence, St. Louis River, MN/WI, USA	EnviroAtlas-Carbon sequestered by trees	InVEST fisheries, lobster, South Africa	ORVal, Valuing recreational sites, W. Norwich, UK
EM Full Name em.detail.fullNameHelp ?	Cultural ecosystem services, Bilbao, Spain	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA	Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa	ORVal short case study: number 1 valuing recreational sites in West Norwich (version 2.0)
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None ? Comment: EU Mapping Studies	US EPA	US EPA	US EPA \| EnviroAtlas \| i-Tree	InVEST	None
EM Source Document ID	191	96	330	223 ? Comment: Additional source: I-tree Eco (doc# 345).	349 ? Comment: Supplemented with the InVEST Users Guide fisheries.	476
Document Author em.detail.documentAuthorHelp ?	Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	US EPA Office of Research and Development - National Exposure Research Laboratory	Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby	Day, B., & Smith
Document Year em.detail.documentYearHelp ?	2013	2011	2013	2013	2018	None
Document Title em.detail.sourceIdHelp ?	Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning	Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	EnviroAtlas - Featured Community	Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals	ORVal short case study: number 1 valuing recreational sites in West Norwich (version 2.0)
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published on US EPA EnviroAtlas website	Published journal manuscript	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	https://www.epa.gov/enviroatlas	https://www.naturalcapitalproject.org/invest/	https://www.leep.exeter.ac.uk/orval/
Contact Name em.detail.contactNameHelp ?	Izaskun Casado-Arzuaga	Leah Oliver	Ted R. Angradi	EnviroAtlas Team	Michelle Ward	Brett Day
Contact Address	Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain	National Health and Environmental Research Effects Laboratory	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	ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia	None provided
Contact Email	izaskun.casado@ehu.es	leah.oliver@epa.gov	angradi.theodore@epa.gov	enviroatlas@epa.gov	m.ward@uq.edu.au	Brett.Day@exeter.ac.uk

EM Description

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management."	AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294)	ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."	The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group."	AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area."	The ORVal Tool is a web application (accessed at https://www.leep.exeter.ac.uk/orval) developed by the Land, Environment, Economics and Policy (LEEP) Institute at the University of Exeter with support from DEFRA. ORVal’s primary purpose is to help quantify the benefits that are derived from accessible outdoor recreation areas in England. Those outdoor recreation areas, or greenspaces, include an array of features such as beaches, parks, nature reserves and country paths This short case study uses ORVal to explore the visits and welfare values that are generated by existing greenspaces in West Norwich. It provides both practical details as to how tasks are performed in ORVal and explanations as to how the numbers reported by the Tool should be interpreted.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Land management, ecosystem management, response to EU 2020 Biodiversity Strategy	Not applicable	None identified	None identified	Future rock lobster fisheries management	None provided
Biophysical Context	Northern Spain; Bizkaia region	nearshore; <1.5 km offshore; <12 m depth	submerged aquatic vegetation	No additional description provided	No additional description provided	Urban greenspaces in the UK
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Not applicable	No scenarios presented	No scenarios presented	Fisheries exploitation; fishing vulnerability (of age classes)	NA

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
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	New or revised model	Application of existing model	Application of existing 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-193	EM-260	EM-414	EM-493	EM-541	EM-1010
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	Doc-345	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
EM Temporal Extent em.detail.tempExtentHelp ?	2000 - 2007	2006-2007	2010 - 2012	2010-2013	1986-2115	2016-2018
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	future time	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Year	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Geopolitical	Geopolitical	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	St. Louis River Estuary	Durham NC and vicinity	Table Mountain National Park Marine Protected Area	West Norwich
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10-100 km^2	10-100 km^2	100-1000 km^2	100-1000 km^2	1-10 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases) ? Comment: BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively.	spatially distributed (in at least some cases) ? Comment: Census block groups	spatially lumped (in all cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	Not applicable	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 ?	2 m x 2 m	Not applicable	0.07 m^2 to 0.70 m^2	irregular	Not applicable	Not reported

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Numeric	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Yes	No	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	Yes	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	Yes	No	Yes ? Comment: A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data.	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	No	No	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	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-193	EM-260	EM-414	EM-493	EM-541	EM-1010
Europe Spain	None	North America United States Minnesota Wisconsin	North America United States North Carolina	None	Europe United Kingdom

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

EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	Temperate South Africa South Africa Benguela Namaqua	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
Centroid Latitude em.detail.ddLatHelp ?	43.25	17.75	46.72	35.99	-34.18	52.62
Centroid Longitude em.detail.ddLongHelp ?	-2.92	-64.75	-96.13	-78.96	18.35	1.25
Centroid Datum em.detail.datumHelp ?	WGS84	NAD83	WGS84	None provided	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Provided	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Created Greenspace \| Atmosphere	Near Coastal Marine and Estuarine	Created Greenspace
Specific Environment Type em.detail.specificEnvTypeHelp ?	none	stony coral reef	Freshwater estuarine system	Urban and vicinity	Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf	Urban Greenspaces
EM Ecological Scale em.detail.ecoScaleHelp ?	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-193	EM-260	EM-414	EM-493	EM-541	EM-1010
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Individual or population, within a species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
None Available	Genus Acropora Porites spp. Diploria spp. Species Mycetophylia lamarckiana Mycetophylia ferox Montastrea cavernosa Dichocoenia stokesii Meandrina meandrites Agaricia tenuifolia Agaricia fragilis Montastrea annularis Agaricia agaricites Dendrogyra cylindricus Millepora complanata Montastrea faveolata Porites astreoides Montastrea franksii	None Available	None Available	Species West Coast Rock Lobster (Jasus lalandii)	None Available

EnviroAtlas URL

EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	Average Annual Precipitation	Carbon Storage by Tree Biomass	Big game hunting recreation demand	Dasymetric Allocation of Population, Green Space per Capita, Employment Rate

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-193	EM-260	EM-414	EM-493	EM-541	EM-1010
[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 Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Maintenance of physical, chemical, abiotic conditions By natural chemical and physical processes Not applicable	[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 Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings

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

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

EM-193	EM-260	EM-414	EM-493	EM-541	EM-1010
Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	None	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	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:

Comment:

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

EM Modeling Approach

Comment:

Comment:

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)