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-416
EM-712
EM-784
EM-968
EM-981

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
EM Short Name em.detail.shortNameHelp ?	Sed. denitrification, St. Louis River, MN/WI, USA	ESII Tool method	Wildflower mix supporting bees, Florida, USA	EPA Stormwater Manamgement Model	Atlantis ecosystem biology submodel
EM Full Name em.detail.fullNameHelp ?	Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA	ESII (Ecosystem Services Identification & Inventory) Tool method	Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA	Storm Water Management Model User's Manual Version 5.2	Calibrating process-based marine ecosystem models: An example case using Atlantis
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	None	None	US EPA	None
EM Source Document ID	333	391 ? Comment: Website for online user support	400	452	459
Document Author em.detail.documentAuthorHelp ?	Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill	EcoMetrix Solutions Group (ESG)	Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters	Rossman, L. A., M., Simon	Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E.
Document Year em.detail.documentYearHelp ?	2014	2016	2015	2022	2019
Document Title em.detail.sourceIdHelp ?	Sediment nitrification and denitrification in a Lake Superior estuary	ESII Tool	Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States	Storm Water Management Model User's Manual Version 5.2	Calibrating process-based marine ecosystem models: An example case using Atlantis
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Other or unclear (explain in Comment)	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Website	Published journal manuscript	Published EPA report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	https://www.esiitool.com/	Not applicable	https://www.epa.gov/water-research/storm-water-management-model-swmm	https://noaa-fisheries-integrated-toolbox.github.io/Atlantis
Contact Name em.detail.contactNameHelp ?	Brent J. Bellinger	Not reported	Neal Williams	David Burden	Heidi R. Pethybridge
Contact Address	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	Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616	U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198	CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia
Contact Email	bellinger.brent@epa.ogv	Not reported	nmwilliams@ucdavis.edu	burden.david@epa.gov	Heidi.Pethybridge@csiro.au

EM Description

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ? Comment: BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model.	AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance."	Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm	EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ? Comment: The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual.	Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identrified	NA	N/A
Biophysical Context	Estuarine system	Not applicable	field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca	NA	Marine ecosystem
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Varied wildflower planting mixes of annuals and perennials	NA	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	Method Only	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised 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-416	EM-712	EM-784	EM-968	EM-981
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	None	Doc-456
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-713	EM-796 \| EM-797 \| EM-804 \| EM-805 \| EM-806 \| EM-812 \| EM-814	EM-971	EM-978 \| EM-983 \| EM-985 \| EM-990 \| EM-991

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
EM Temporal Extent em.detail.tempExtentHelp ?	2011 - 2012	Not applicable	2011-2012	Not applicable	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	past time	both	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	continuous	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
Bounding Type em.detail.boundingTypeHelp ?	Watershed/Catchment/HUC	Not applicable	Point or points ? Comment: This is a guess based on information in the document. 3 field sites were separated by up to 9km	No location (no locational reference given)	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	St. Louis River estuary	Not applicable	Agricultural plots	Not applicable	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	Not applicable	10-100 km^2	Not applicable	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially lumped (in all cases)	spatially distributed (in at least some cases) ? Comment: map units delineated by user based on project.	spatially lumped (in all cases)	spatially distributed (in at least some cases)	Not applicable
Spatial Grain Type em.detail.spGrainTypeHelp ?	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	area, for pixel or radial feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	Not applicable	map units	Not applicable	mm	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
Model Calibration Reported? em.detail.calibrationHelp ?	No	Not applicable	No	Not applicable	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Not applicable	No	Not applicable	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Not applicable	No	Not applicable	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Not applicable	No	Not applicable	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Not applicable	No	Not applicable	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	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-416	EM-712	EM-784	EM-968	EM-981
North America United States Minnesota Wisconsin	None	North America United States Florida	None	None

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

EM-416	EM-712	EM-784	EM-968	EM-981
None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
Centroid Latitude em.detail.ddLatHelp ?	46.75	Not applicable	29.4	Not applicable	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-92.08	Not applicable	-82.18	Not applicable	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	Not applicable	WGS84	Not applicable	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Not applicable	Provided	Not applicable	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-416	EM-712	EM-784	EM-968	EM-981
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Open Ocean and Seas
Specific Environment Type em.detail.specificEnvTypeHelp ?	Freshwater estuary	Not applicable	Agricultural landscape	User-defined catchments	Multiple
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale corresponds to the Environmental Sub-class	Not applicable	Ecological scale corresponds to 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-416	EM-712	EM-784	EM-968	EM-981
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-416	EM-712	EM-784	EM-968	EM-981
None Available	None Available	other Bees Species Lupinus succulentus Trifolium obtusiflorum Helianthus bolaneri Eschscholzia californica Lotus scoparius Rudbeckia hirta Trifolium willdenovii Phacelia tanacetifolia Phacelia californica Trifolium fucatum Helianthus bolanderi Grindelia camporum Lupinus formosus Nemophila menziesii Trichostema lanceolatum Achillea millefolium Clarkia unguinculata lupinus densiflorus	None Available	None Available

EnviroAtlas URL

EM-416	EM-712	EM-784	EM-968	EM-981
Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	GAP Ecological Systems, The National Hydrography Dataset (NHD), Average Annual Precipitation, Agricultural water use (million gallons/day)	None Available	None Available	None Available

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

EM-416	EM-712	EM-784	EM-968	EM-981
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Micro and regional climate regulation Global climate regulation by reduction of greenhouse gas concentrations Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance Gaseous / air flows Ventilation and transpiration Mass flows Buffering and attenuation of mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals Mediation by ecosystems Mediation of smell/noise/visual impacts Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Provisioning Materials Water Ground water for non-drinking purposes Surface water for non-drinking purposes [Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[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

<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-416	EM-712	EM-784	EM-968	EM-981
None	Rivers and Streams (111) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Forests (21) Composite (8) Presence of environmental class/subclass Stock Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Stock Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Stock Indicator Lakes and Ponds (112) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Wetlands (12) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Stock Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Stock Indicator Soil (2) Specific types of soil Quality Indicator	Agroecosystems (22) Fauna (4) Pollinators Site Indicator	Rivers and Streams (111) Composite (8) Regulation of extreme events Flow Indicator Groundwater Water (3) Liquid water Flow Indicator Lakes and Ponds (112) Composite (8) Regulation of extreme events Flow Indicator Urban/Surburban (27) Composite (8) Regulation of extreme events Flow Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Stock Indicator Open Oceans and Seas (114) Composite (8) Presence of environmental class/subclass 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:

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)