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-99
EM-319
EM-450
EM-845
EM-936
EM-941

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
EM Short Name em.detail.shortNameHelp ?	Landscape importance for crops, Europe	Redfish and cold water coral (EFH), Norway	Decrease in wave runup, St. Croix, USVI	Red-winged blackbird abun, Piedmont region, USA	i-Tree species selector v. 4.0	ESTIMAP - Pollination potential, Iran
EM Full Name em.detail.fullNameHelp ?	Landscape importance for crop-based production, Europe	Linkage between redfish and cold water coral, Norway (essential fish habitat model)	Decrease in wave runup (by reef), St. Croix, USVI	Red-winged blackbird abundance, Piedmont ecoregion, USA	i-Tree species selector v. 4.0	ESTIMAP - Pollination potential, Iran
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	None	US EPA	None	i-Tree	None
EM Source Document ID	228	259	335	405	426 ? Comment: Doc# 427 is an additional source for this EM.	434
Document Author em.detail.documentAuthorHelp ?	Haines-Young, R., Potschin, M. and Kienast, F.	Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Riffel, S., Scognamillo, D., and L. W. Burger	i-Tree	Rahimi, E., Barghjelveh, S., and P. Dong
Document Year em.detail.documentYearHelp ?	2012	2010	2014	2008	None	2020
Document Title em.detail.sourceIdHelp ?	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Estimating linkages between redfish and cold water coral on the Norwegian coast	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	i-Tree Species Selector User's Manual v. 4.0	Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran)
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 journal manuscript	Webpage	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	https://species.itreetools.org/	Not applicable
Contact Name em.detail.contactNameHelp ?	Marion Potschin	Naomi S. Foley	Susan H. Yee	Sam Riffell	Not reported ? Comment: send comments through any of the means listed on the i-Tree support page: http://www.itreetools.org/support/.	Ehsan Rahini
Contact Address	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	Dept. of Economics and Management, Univeristy of Tromso, Norway	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	Not reported	Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
Contact Email	marion.potschin@nottingham.ac.uk	naomifoley@gmail.com	yee.susan@epa.gov	sriffell@cfr.msstate.edu	info@itreetools.org	ehsanrahimi666@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture."	ABSTRACT: "…This paper applies the production function approach to estimate the link between cold water corals and redfish in Norway. Both the carrying capacity and growth rate of redfish are found to be functions of cold water coral habitat and thus cold water corals can be considered an essential fish habitat…The essential habitat model shows the best fit to the data…" AUTHOR'S DESCRIPTION: "…the EFH model presented by Barbier and Strand (1998), in which the habitat is considered essential to the stock; i.e., if the habitat declines to zero the fish stock will perish…based on the Gordon-Schaefer model, which is a single-species biomass model, where effort is the control variable and fish stock is the state variable. In the case of habitat-fisheries interactions, such as in our case, a second state variable is introduced, the habitat (CWC)…Scientists have stimated that 30-50% of CWC habitat has been damaged (Fossa, Mortensen, and Furevik 2002. Working within these bounds, we empirically estimate the relationship between CWC as a habitat and a fish stock..."	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...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))."	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds."	ABSTRACT: "The Species Selector is a free-standing i-Tree utility that ranks tree species based on their environmental benefits at maturity. As such, it complements existing tree selection programs that rank species based on esthetics or other features. Species are selected based on three types of information. First, hardiness is considered. The hardiness zone is determined based on state and city, and all species that are not sufficiently hardy are eliminated from consideration. Second, mature height is considered. Users are asked to specify minimum and maximum heights, and species outside of that range are eliminated. Finally, eight environmental factors are considered in the rankings created by the Species Selector: • Air pollution removal • Air temperature reduction • Ultraviolet radiation reduction • Carbon storage • Pollen allergenicity • Building energy conservation • Wind reduction • Stream flow reduction (stormwater management). Users are asked to rank the importance of each of these factors on a scale of 0 to 10. The combination of hardiness, mature height, and desired functionality produces a ranked list of appropriate species from an initial database of about 1,600 species. The large species database covers a broad range of native, naturalized and exotic trees, some of which are commonly planted in urban areas. Since only city hardiness zone, tree height and user functional preferences are used to produce the list, there may well be many species on the list that are unsuitable to the local context for a variety of reasons. A species may have particular structural, drainage, sun, pest, or soil pH limitations that should exclude it from use. Furthermore, since many native and exotic species are included, items may appear that are simply not available in the local trade. For these reasons, the list should be considered a beginning rather than an end. The list will need to be whittled down to meet local needs and limitations. Relevant cultural needs should be taken into account as well. The result will be a list of recommended species suited for local use that maximizes environmental services."	Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None reported	None identified	None reported
Biophysical Context	No additional description provided	Continental slope	No additional description provided	Conservation Reserve Program lands left to go fallow	No additional description provided	None additional
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area.	No scenarios presented	N/A	No scenarios presented	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method Only	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing 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-99	EM-319	EM-450	EM-845	EM-936	EM-941
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-231 \| Doc-228	Doc-227	Doc-335	Doc-405	Doc-427	Doc-432
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-119 \| EM-120 \| EM-121 \| EM-162 \| EM-164 \| EM-165 \| EM-122 \| EM-123 \| EM-124 \| EM-125 \| EM-166 \| EM-170 \| EM-171	EM-106	EM-447	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-846 \| EM-847	None	EM-939

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
EM Temporal Extent em.detail.tempExtentHelp ?	2000	1986-2002	2006-2007, 2010	2008	Not applicable	2020
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	Not applicable	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-99	EM-319	EM-450	EM-845	EM-936	EM-941
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Physiographic or ecological	Physiographic or ecological	Not applicable	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	The EU-25 plus Switzerland and Norway	Norwegian Sea (ICES areas I and II)	Coastal zone surrounding St. Croix	Piedmont Ecoregion	Not applicable	Iran
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	1000-10,000 km^2.	100-1000 km^2	100,000-1,000,000 km^2	Not applicable	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
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)	spatially lumped (in all cases)	Not applicable	spatially distributed (in at least some cases) ? Comment: Varies by inputs, but results are for areas of country
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	Not applicable	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 km x 1 km	Not applicable	10 m x 10 m	Not applicable	Not applicable	ha^2

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
EM Computational Approach em.detail.emComputationalApproachHelp ?	Logic- or rule-based	Analytic	Analytic	Analytic	Analytic	Numeric
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-99	EM-319	EM-450	EM-845	EM-936	EM-941
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Yes	Yes	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	No	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	Adj R2 (30% CWC decline) \| 0.8677 \| unitless Durbin-Watson (DW) test (30% CWC decline) \| 1.392486 \| unitless P value (30% CWC decline) \| 0.0000 \| unitless Adj R2 (50% CWC decline) \| 0.8880 \| unitless Durbin-Watson (DW) test (50% CWC decline) \| 1.582468 \| unitless P-value (50% CWC decline) \| 0.0000 \| unitless	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	Yes	No	Not applicable	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	No	Yes	Not applicable	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Yes	Not applicable	Unclear	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-99	EM-319	EM-450	EM-845	EM-936	EM-941
Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia France Lithuania Hungary Switzerland Spain Czech Republic Belgium Norway Finland Denmark Italy Germany Estonia	None	None	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	North America United States	Europe ? Comment: Model for Iran - no form preset id for country

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

EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
None	Temperate North Atlantic Northeast Atlantic Northern European Seas Northern Norway and Finnmark Southern Norway	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
Centroid Latitude em.detail.ddLatHelp ?	50.53	70	17.73	36.23	Not applicable	32.29
Centroid Longitude em.detail.ddLongHelp ?	7.6	10	-64.77	-81.9	Not applicable	53.68
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine \| Open Ocean and Seas	Near Coastal Marine and Estuarine	Grasslands	Created Greenspace	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	cold water coral reefs	Coral reefs	grasslands	Urban greenspace	terrestrial land types
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 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 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-99	EM-319	EM-450	EM-845	EM-936	EM-941
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Guild or Assemblage	Not applicable	Species	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
None Available	Genus Sebastes sp. Lophelia sp. Species Lophelia pertusa	None Available	Species Redwinged blackbird	None Available	Species Apis mellifera Apis florea

EnviroAtlas URL

EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
None Available	Big game hunting recreation demand	National Hydrography Dataset Plus (NHD PlusV2)	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2)	The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Precipitation, Waterbody area

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-99	EM-319	EM-450	EM-845	EM-936	EM-941
[Ecosystem] Provisioning Nutrition Biomass Cultivated crops	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation Global climate regulation by reduction of greenhouse gas concentrations Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Gaseous / air flows Storm protection Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal

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

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

EM-99	EM-319	EM-450	EM-845	EM-936	EM-941
Agroecosystems (22) Flora (5) food crops Stock Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator Open Oceans and Seas (114) Fauna (4) Fish Stock Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	None	Forests (21) Fauna (4) Wildlife Stock Indicator Scrubland/Shrubland (24) Fauna (4) Wildlife Stock Indicator Urban/Surburban (27) Fauna (4) Wildlife Stock 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

Comment:

EM Locations, Environments, Ecology

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

Comment:

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)