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-104
EM-106
EM-319
EM-450
EM-630
EM-936

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
EM Short Name em.detail.shortNameHelp ?	Landscape importance for crops, Europe	SPARROW, Northeastern USA	Value of Habitat for Shrimp, Campeche, Mexico	Redfish and cold water coral (EFH), Norway	Decrease in wave runup, St. Croix, USVI	WaterWorld v2, Santa Basin, Peru	i-Tree species selector v. 4.0
EM Full Name em.detail.fullNameHelp ?	Landscape importance for crop-based production, Europe	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)	Decrease in wave runup (by reef), St. Croix, USVI	WaterWorld v2, Santa Basin, Peru	i-Tree species selector v. 4.0
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	US EPA	None	None	US EPA	None	i-Tree
EM Source Document ID	228	86	227	259	335	368	426 ? Comment: Doc# 427 is an additional source for this EM.
Document Author em.detail.documentAuthorHelp ?	Haines-Young, R., Potschin, M. and Kienast, F.	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	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Van Soesbergen, A. and M. Mulligan	i-Tree
Document Year em.detail.documentYearHelp ?	2012	2011	1998	2010	2014	2018	None
Document Title em.detail.sourceIdHelp ?	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	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	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru	i-Tree Species Selector User's Manual v. 4.0
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	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	Webpage

Software and Access

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	www.policysupport.org/waterworld	https://species.itreetools.org/
Contact Name em.detail.contactNameHelp ?	Marion Potschin	Richard Moore	E.B. Barbier	Naomi S. Foley	Susan H. Yee	Arnout van Soesbergen	Not reported ? Comment: send comments through any of the means listed on the i-Tree support page: http://www.itreetools.org/support/.
Contact Address	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	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	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK	Not reported
Contact Email	marion.potschin@nottingham.ac.uk	rmoore@usgs.gov	Not reported	naomifoley@gmail.com	yee.susan@epa.gov	arnout.van_soesbergen@kcl.ac.uk	info@itreetools.org

EM Description

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
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."	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..."	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: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r	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."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	water-quality assessment, total maximum daily load(TMDL) determination	None identified	None identified	None identified	None identified	None identified
Biophysical Context	No additional description provided	Norteneastern region (U.S.); Mid-Atlantic region (U.S.)	Gulf of Mexico; mangrove-lagoon system	Continental slope	No additional description provided	Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal.	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area.	No scenarios presented	Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist)	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	New or revised model	Application of existing 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-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-231 \| Doc-228	None	None	Doc-227	Doc-335	None	Doc-427
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	None	EM-185 \| EM-319	EM-106	EM-447	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
EM Temporal Extent em.detail.tempExtentHelp ?	2000	2002 ? Comment: Several nationwide database development and modeling efforts were necessary to create models consistent with 2002 conditions.	1980-1990	1986-2002	2006-2007, 2010	1950-2071	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	Not applicable
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	both	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	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	Not applicable	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Not applicable	Not applicable	Month	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Physiographic or ecological	Watershed/Catchment/HUC	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	The EU-25 plus Switzerland and Norway	NE U.S. Regions	Laguna de Terminos Mangrove system	Norwegian Sea (ICES areas I and II)	Coastal zone surrounding St. Croix	Santa Basin	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	>1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	100-1000 km^2	10,000-100,000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	Not applicable
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	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	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 km x 1 km	30 x 30 m	1 km x 1 km	Not applicable	10 m x 10 m	1 km2	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
EM Computational Approach em.detail.emComputationalApproachHelp ?	Logic- or rule-based	Analytic	Analytic	Analytic	Analytic	*	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	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	None	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Yes	Yes	Yes	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	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	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	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	Yes	No	No	Yes	Yes	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Unclear	Yes	No	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	Yes	Yes	No	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	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-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
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	North America United States Vermont New York West Virginia Massachusetts Maine Connecticut New Hampshire Delaware Maryland New Jersey Pennsylvania Virgina	North America Mexico	None	None	None	North America United States

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

EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
None	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	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
Centroid Latitude em.detail.ddLatHelp ?	50.53	42	18.61	70	17.73	-9.05	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	7.6	-73	-91.55	10	-64.77	-77.81	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	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	Near Coastal Marine and Estuarine	None	Created Greenspace
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	none	Mangrove	cold water coral reefs	Coral reefs	tropical, coastal to montane	Urban greenspace
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	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 is finer than that of the Environmental Sub-class	Other or unclear (comment)	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-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Guild or Assemblage	Guild or Assemblage	Not applicable	Not applicable	Species

Taxonomic level and name of organisms or groups identified

EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
None Available	None Available	other Shrimp	Genus Sebastes sp. Lophelia sp. Species Lophelia pertusa	None Available	None Available	None Available

EnviroAtlas URL

EM-99	EM-104	EM-106	EM-319	EM-450	EM-630	EM-936
None Available	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	National Hydrography Dataset Plus (NHD PlusV2)	Average Annual Precipitation	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2)

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-104	EM-106	EM-319	EM-450	EM-630	EM-936
[Ecosystem] Provisioning Nutrition Biomass Cultivated crops	[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 Mediation of flows Liquid flows Flood protection	None	[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

<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-104	EM-106	EM-319	EM-450	EM-630	EM-936
Agroecosystems (22) Flora (5) food crops Stock Indicator	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	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator	None	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:

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

EM Modeling Approach

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)