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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EM-88
EM-224
EM-333
EM-492
EM-598
EM-948

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
EM Short Name em.detail.shortNameHelp ?	Area and hotspots of carbon storage, South Africa	FORCLIM v2.9, West Cascades, OR, USA	Evoland v3.5 (unbounded growth), Eugene, OR, USA	EnviroAtlas - Restorable wetlands	DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland	Global forest stock, biomass and carbon downscaled
EM Full Name em.detail.fullNameHelp ?	Area and hotspots of carbon storage, South Africa	FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA	Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA	US EPA EnviroAtlas - Percent potentially restorable wetlands, USA	DeNitrification-DeComposition simulation of N2O flux Ireland	Global forest growing stock, biomass and carbon downscaled map
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	US EPA	Envision	US EPA \| EnviroAtlas	None	None
EM Source Document ID	271	23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	262	358	442
Document Author em.detail.documentAuthorHelp ?	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	US EPA Office of Research and Development - National Exposure Research Laboratory	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner
Document Year em.detail.documentYearHelp ?	2008	2007	2008	2013	2010	2008
Document Title em.detail.sourceIdHelp ?	Mapping ecosystem services for planning and management	Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	EnviroAtlas - National	Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture	A global forest growing stock, biomass and carbon map based on FAO statistics
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published on US EPA EnviroAtlas website	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	http://evoland.bioe.orst.edu/	https://www.epa.gov/enviroatlas	http://www.dndc.sr.unh.edu	Not applicable
Contact Name em.detail.contactNameHelp ?	Benis Egoh	Richard T. Busing	Michael R. Guzy	EnviroAtlas Team	M. Abdalla	Georg Kindermann
Contact Address	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA	Oregon State University, Dept. of Biological and Ecological Engineering	Not reported	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	International Institute for Applied Systems Analysis, Laxenburg, Austria
Contact Email	Not reported	rtbusing@aol.com	Not reported	enviroatlas@epa.gov	abdallm@tcd.ie	kinder(at)iiasa.ac.at

EM Description

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot."	ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire."	Note: A more recent version of this model exists. See Related EMs below for links to related models/applications. ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses."	DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map."	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC.	ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None Identified	Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…"	None Identified	climate change	None identified
Biophysical Context	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions	No additional description provided	No additional description provided	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Two scenarios modelled, forests with and without fire	Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves.	No scenarios presented	fertilization	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	New or revised model	New or revised 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-88	EM-224	EM-333	EM-492	EM-598	EM-948
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-271	Doc-22 \| Doc-23	Doc-183 \| Doc-47 \| Doc-313 \| Doc-314 ? Comment: Doc 183 is a secondary source for the Evoland model.	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-85 \| EM-86 \| EM-87	EM-146 \| EM-208 \| EM-186	EM-12 \| EM-369	None	EM-593	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	>650 yrs	1990-2050	2006-2013	1961-1990	1999-2005
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-dependent	time-stationary	time-dependent	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	past time	future time	Not applicable	both	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	discrete	Not applicable	discrete	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	2	Not applicable	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Year	Not applicable	Day	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Geopolitical	Geopolitical	Point or points	No location (no locational reference given)
Spatial Extent Name em.detail.extentNameHelp ?	South Africa	West Cascades, Oregon	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	conterminous United States	Oak Park Research centre	Global
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	10-100 km^2	>1,000,000 km^2	1-10 ha	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
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 distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed across catchments with average size of 65,000 ha	0.08 ha	varies	irregular	Not applicable	0.5 x 0.5 degrees

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Numeric	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	Other or unclear (comment)	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Unclear	No	Yes	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	Yes ? Comment: Actual value was not given, just that results were very poor. Simulation results were 258% of observed	Yes ? Comment: For the 0.5 grid level equation where the country forest level is missing.
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	r squared \| 00.0 \| unitless	r2 \| Varies by run; view runs to see values \| unitless
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	No	Yes	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
Africa South Africa	North America United States Oregon	North America United States Oregon	North America United States	Europe Ireland	None

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

EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
Centroid Latitude em.detail.ddLatHelp ?	-30	44.24	44.11	39.5	52.86	44.51
Centroid Longitude em.detail.ddLongHelp ?	25	-122.24	-123.09	-98.35	6.54	-123.51
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	None provided	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Provided	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Forests	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Agroecosystems	Agroecosystems	Forests
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	Primarily conifer forest	Agricultural-urban interface at river junction	Terrestrial	farm pasture	Forests
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 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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Species	Not applicable	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
None Available	Species Pinus ponderosa Abies amabilis Tsuga mertensiana Thuja plicata Picea sitchensis Abies procera Abies grandis Chamaecyparis nootkatensis Tsuga heterophylla Other (trees) Pseudotsuga menziesii	other Fish Species Red-Legged Frog Osprey Western Terrestrial Garter Snake Little Brown Myotis Belted Kingfisher Wood Duck Northern River Otter Great Blue Heron Roughskin Newt Pacific Water Shrew American Beaver Mink Western Pond Turtle Killdeer Northwestern Salamander	None Available	None Available	None Available

EnviroAtlas URL

EM-88	EM-224	EM-333	EM-492	EM-598	EM-948
None Available	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions	Dasymetric Allocation of Population, GAP Ecological Systems, The National Hydrography Dataset (NHD), Percent Natural Land Cover in Stream Buffer , Hectares of Vegetable Crops, Employment Rate	GAP Ecological Systems, The Watershed Boundary Dataset (WBD)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems

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-88	EM-224	EM-333	EM-492	EM-598	EM-948
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Provisioning Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing [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	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	None

<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-88	EM-224	EM-333	EM-492	EM-598	EM-948
None	None	Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	None	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Comment:

Comment:

Comment:

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

Comment:

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)