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-104
EM-106
EM-319
EM-345
EM-450
EM-941

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
EM Short Name em.detail.shortNameHelp ?	SPARROW, Northeastern USA	Value of Habitat for Shrimp, Campeche, Mexico	Redfish and cold water coral (EFH), Norway	InVEST habitat quality, Puli Township, Taiwan	Decrease in wave runup, St. Croix, USVI	ESTIMAP - Pollination potential, Iran
EM Full Name em.detail.fullNameHelp ?	SPARROW (SPAtially Referenced Regressions On Watershed Attributes), Northeastern USA	Value of Habitat for Shrimp, Campeche, Mexico	Linkage between redfish and cold water coral, Norway (essential fish habitat model)	InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan	Decrease in wave runup (by reef), St. Croix, USVI	ESTIMAP - Pollination potential, Iran
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	None	None	InVEST	US EPA	None
EM Source Document ID	86	227	259	308	335	434
Document Author em.detail.documentAuthorHelp ?	Moore, R. B., Johnston, C.M., Smith, R. A. and Milstead, B.	Barbier, E. B., and Strand, I.	Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M	Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Rahimi, E., Barghjelveh, S., and P. Dong
Document Year em.detail.documentYearHelp ?	2011	1998	2010	2014	2014	2020
Document Title em.detail.sourceIdHelp ?	Source and delivery of nutrients to receiving waters in the northeastern and mid-Atlantic regions of the United States	Valuing mangrove-fishery linkages: A case study of Campeche, Mexico	Estimating linkages between redfish and cold water coral on the Norwegian coast	Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	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	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Richard Moore	E.B. Barbier	Naomi S. Foley	Yu-Pin Lin ? Comment: Tel.: +886 2 3366 3467; fax: +866 2 2368 6980	Susan H. Yee	Ehsan Rahini
Contact Address	U.S. Environmental Protection Agency, 27 Tarzwell Drive, Narragansett, Rhode Island 02882	Environment Department, University of York, York YO1 5DD, UK	Dept. of Economics and Management, Univeristy of Tromso, Norway	Not reported	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
Contact Email	rmoore@usgs.gov	Not reported	naomifoley@gmail.com	yplin@ntu.edu.tw	yee.susan@epa.gov	ehsanrahimi666@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid-Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries"	AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153)	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..."	Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the 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...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: ". ..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 ?	water-quality assessment, total maximum daily load(TMDL) determination	None identified	None identified	Environmental effects of Highway 6 construction on Puli Township, Taiwan	None identified	None reported
Biophysical Context	Norteneastern region (U.S.); Mid-Atlantic region (U.S.)	Gulf of Mexico; mangrove-lagoon system	Continental slope	26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere.	No additional description provided	None additional
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area.	Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas.	No scenarios presented	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	Application of existing model	Application of existing model	Application of existing 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-104	EM-106	EM-319	EM-345	EM-450	EM-941
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	Doc-227	Doc-278	Doc-335	Doc-432
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-185 \| EM-319	EM-106	EM-143	EM-447	EM-939

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
EM Temporal Extent em.detail.tempExtentHelp ?	2002 ? Comment: Several nationwide database development and modeling efforts were necessary to create models consistent with 2002 conditions.	1980-1990	1986-2002	2010-2025	2006-2007, 2010	2020
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Geopolitical	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	NE U.S. Regions	Laguna de Terminos Mangrove system	Norwegian Sea (ICES areas I and II)	Puli Township, Nantou County	Coastal zone surrounding St. Croix	Iran
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	100-1000 km^2	100-1000 km^2	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
EM Spatial Distribution em.detail.distributeLumpHelp ?	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)	spatially distributed (in at least some cases)	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	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	30 x 30 m	1 km x 1 km	Not applicable	40 m x 40 m	10 m x 10 m	ha^2

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	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-104	EM-106	EM-319	EM-345	EM-450	EM-941
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Yes	Yes	Unclear	Yes	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes ? Comment: R-squared of .97 refers to the modelled loading whereas .83 refers to yield (see table 1, pg 972 for more information)	Yes	Yes	Not applicable	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R^2 \| 0.97 \| Not applicable Mean Square Error \| 0.12 \| Not applicable Root Mean Square Error \| 0.35 \| Not applicable R-squared \| 0.83 \| Not applicable	Adjusted R^2 \| 0.745 \| Not applicable F-stat \| 30.216 \| Not applicable SE of regression \| 639745.7 \| Not applicable	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
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	Not applicable	Yes	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Unclear	Yes	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Yes	Yes	Yes	No	No	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Unclear	Unclear	Yes	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-104	EM-106	EM-319	EM-345	EM-450	EM-941
North America United States Vermont New York West Virginia Massachusetts Maine Connecticut New Hampshire Delaware Maryland New Jersey Pennsylvania Virgina	North America Mexico	None	Asia China ? Comment: Taiwan	None	Europe ? Comment: Model for Iran - no form preset id for country

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

EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Southern Gulf of Mexico	Temperate North Atlantic Northeast Atlantic Northern European Seas Northern Norway and Finnmark Southern Norway	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
Centroid Latitude em.detail.ddLatHelp ?	42	18.61	70	23.98	17.73	32.29
Centroid Longitude em.detail.ddLongHelp ?	-73	-91.55	10	120.96	-64.77	53.68
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Ground Water \| Terrestrial Environment (sub-classes not fully specified) \| Agroecosystems \| Atmosphere	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine \| Open Ocean and Seas	Rivers and Streams \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	none	Mangrove	cold water coral reefs	Predominantly an agricultural area with associated forest land	Coral reefs	terrestrial land types
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser 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 is finer than that of 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-104	EM-106	EM-319	EM-345	EM-450	EM-941
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Guild or Assemblage	Guild or Assemblage	Community	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
None Available	other Shrimp	Genus Sebastes sp. Lophelia sp. Species Lophelia pertusa	None Available	None Available	Species Apis mellifera Apis florea

EnviroAtlas URL

EM-104	EM-106	EM-319	EM-345	EM-450	EM-941
National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, U.S. EPA (Omernik) ecoregions, Total Annual Nitrogen Deposition	GAP Ecological Systems, Big game hunting recreation demand	Big game hunting recreation demand	Percent GAP Status 1 & 2	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-104	EM-106	EM-319	EM-345	EM-450	EM-941
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters Mediation of waste, toxics and other nuisances Mediation by biota Bio-remediation by micro-organisms, algae, plants, and animals Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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 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 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-104	EM-106	EM-319	EM-345	EM-450	EM-941
Rivers and Streams (111) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Shrimp Stock Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator Open Oceans and Seas (114) Fauna (4) Fish Stock Indicator	Forests (21) Composite (8) Presence of environmental class/subclass Stock Indicator Quality Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Stock Indicator Quality Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator	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:

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])

Comment:

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)