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.

Compare Criteria:

Show Criteria

Hide Criteria

Comparing:

EM-80
EM-92
EM-132
EM-177
EM-340
EM-417
EM-652

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
EM Short Name em.detail.shortNameHelp ?	Agronomic ES and plant traits, Central French Alps	Runoff potential of pesticides, Europe	Flood regulation capacity, Etropole, Bulgaria	Salmon habitat values, west coast of Canada	InVEST crop pollination, Costa Rica	SWAT, Guanica Bay, Puerto Rico, USA	Savannah Sparrow density, CREP, Iowa, USA
EM Full Name em.detail.fullNameHelp ?	Agronomic ecosystem service estimated from plant functional traits, Central French Alps	Runoff potential of pesticides, Europe	Flood regulation capacity of landscapes, Municipality of Etropole, Bulgaria	Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada	InVEST crop pollination, Costa Rica	SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA	Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	None	EU Biodiversity Action 5	None	InVEST	US EPA	None
EM Source Document ID	260	254	248	286	279	334	372
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Schriever, C. A., and Liess, M.	Nedkov, S., Burkhard, B.	Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M	Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf	Hu, W. and Y. Yuan	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever
Document Year em.detail.documentYearHelp ?	2011	2007	2012	2003	2009	2013	2010
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Mapping ecological risk of agricultural pesticide runoff	Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria	Valuing freshwater salmon habitat on the west coast of Canada	Modelling pollination services across agricultural landscapes	Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt
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 EPA report	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	http://www.naturalcapitalproject.org/models/crop_pollination.html	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Carola Alexandra Schriever	Stoyan Nedkov	Duncan Knowler	Eric Lonsdorf	Yongping Yuan	David Otis
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany	National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria	School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6	Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA	USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University
Contact Email	sandra.lavorel@ujf-grenoble.fr	carola.schriever@ufz.de	snedkov@abv.bg	djk@sfu.ca	ericlonsdorf@lpzoo.org	Yuan.Yongping@epa.gov	dotis@iastate.edu

EM Description

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions."	ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception."	ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. Based on spatial land cover units originating from CORINE and further data sets, these regulating ecosystem services were quantified and mapped. Resulting maps show the ecosystems’ flood regulating service capacities in the case study area of the Malki Iskar river basin above the town of Etropole in the northern part of Bulgaria...The resulting map of flood regulation supply capacities shows that the Etropole municipality’s area has relatively high capacities for flood regulation. Areas of high and very high relevant capacities cover about 34% of the study area." AUTHOR'S DESCRIPTION: "The capacities of the identified spatial units were assessed on a relative scale ranging from 0 to 5 (after Burkhard et al., 2009). A 0-value indicates that there is no relevant capacity to supply flood regulating services and a 5-value indicates the highest relevant capacity for the supply of these services in the case study region. Values of 2, 3 and 4 represent respective intermediate supply capacities. Of course it depends on the observer’s estimation and knowledge which function–service relations in general are supposed to be relevant. But, this scale offers an alternative relative evaluation scheme, avoiding the presentation of monetary or normative value-transfer results. The 0–5 capacity values’ classifications for the different land cover types were based on the spatial analyses of different biogeophysical and land use data combined with hydrological modeling as described before…The supply capacities of the land cover classes and soil types in the study area were assigned to every unit in their databases. GIS map layers, containing information about the capacity to supply flood regulation for every polygon, were created. The map of supply capacities of flood regulating ecosystem services was elaborated by overlaying the GIS map layers of the land cover and the soils’ capacities."	ABSTRACT: "In this paper, we present a framework for valuing benefits for fisheries from protecting areas from degradation, using the example of the Strait of Georgia coho salmon fishery in southern British Columbia, Canada. Our study improves upon previous methods used to value fish habitat in two major respects. First, we use a bioeconomic model of the coho fishery to derive estimates of value that are consistent with economic theory. Second, we estimate the value of changing the quality of fish habitat by using empirical analyses to link fish population dynamics with indices of land use in surrounding watersheds."	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: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..."	AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008).	ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 bbspath + 0.01024 grass3200 + 0.0255867 * hay3200)
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	European Commission Water Framework Directive (WFD, Directive 2000/60/EC)	None identified	None identified	None identified	None Identified	None identified
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	Not applicable	Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak.	No additional description provided	No additional description provided	Need to fill in	Prairie pothole region of north-central Iowa
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Habitat quality	No scenarios presented	Planting type, fertilizing rate, harvest rate	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
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	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	Application of existing model ? Comment: Models developed by Quamen (2007).

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

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-270	Doc-255 \| Doc-256 \| Doc-257	Doc-248 \| Doc-251 \| Doc-252	None	Doc-279	None	Doc-372
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-81 \| EM-82 \| EM-83	None	EM-130 \| EM-133	EM-179 \| EM-183 \| EM-180 \| EM-181	EM-338 \| EM-339	None	EM-648 \| EM-649 \| EM-650 \| EM-651

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	2000	Not reported	1989-1999	2001-2002	1981-2004	1992-2007
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	Not applicable	Not applicable	Not applicable	future time	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	Not applicable	Not applicable	Not applicable	discrete	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	Not applicable	Not applicable	Not applicable	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Day	Not applicable	Not applicable	Not applicable	Day	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Geopolitical	Physiographic or ecological	Other	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	EU-15	Municipality of Etropole	South Thompson watershed	Large coffee farm, Valle del General	Guanica Bay, Puerto Rico watersheds	CREP (Conservation Reserve Enhancement Program) wetland sites
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	>1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	10-100 km^2	100-1000 km^2	1-10 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
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)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	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	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	10 km x 10 km	Distributed by land cover and soil type polygons	Not applicable	30 m x 30 m	30m x 30m	multiple, individual, irregular shaped sites

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Yes	Unclear	Yes ? Comment: Used 1981 and 1982 data to calibrate hydrology.	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	No	No ? Comment: Calibration for both the stream flow and Sediment concentration of the mode	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	Nash-Sutcliffe \| 0.82 \| unitless Rsquared \| 0.89 \| unitless Rsquared for sed \| 0.15 \| unitless NSE for sed \| -0.02 \| unitless RME for sed \| 0.15 \| unitless	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	Yes	Yes ? Comment: Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	No	No	No	Unclear	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	No	Yes	Yes	Yes ? Comment: Yes for both runoff and sediment	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	No	Not applicable	No	No	No	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
Europe France	Europe United Kingdom Portugal Spain Greece Austria Netherlands Sweden Belgium Ireland Luxembourg Finland Denmark Italy France Germany	Europe Bulgaria	North America Canada	North America Costa Rica	North America United States Puerto Rico U.S. Caribbean Territories	North America United States Iowa

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

EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
None	None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
Centroid Latitude em.detail.ddLatHelp ?	45.05	50.01	42.8	49.29	9.13	18.19	42.62
Centroid Longitude em.detail.ddLongHelp ?	6.4	4.67	24	-123.8	-83.37	-66.76	-93.84
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Rivers and Streams \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland	Rivers and Streams \| Lakes and Ponds \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland	Rivers and Streams \| Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified) \| Agroecosystems	Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands \| Agroecosystems \| Grasslands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows.	Arable lands in near-stream environments	Mountainous flood-prone region	Rivers and streams	Cropland and surrounding landscape	watershed	Grassland buffering inland wetlands set in agricultural land
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	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-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Not applicable	Other (Comment) ? Comment: Coho salmon stock	Species	Not applicable	Species

Taxonomic level and name of organisms or groups identified

EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
None Available	None Available	None Available	Species Oncorhynchus kisutch	other Huge Black 2002 Genus Trigonisca sp. Species Apis mellifera Nannotrigona mellaria Trigona corvina Trigona (Tetragonisca) angustula Trigona fussipennis Trigona fulviventris Trigona dorsalis Plebia frontalis Melipona fasciata Plebeia jatiformis Trigona (Tetragona) clavipies Partamona cupira	None Available	Species Savannah Sparrow (Passerculus sandwichensis)

EnviroAtlas URL

EM-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
GAP Ecological Systems, Carbon storage by tree biomass (kg/m2)	Average Annual Precipitation, Stream Length Impaired by Pesticides, The Watershed Boundary Dataset (WBD), Hectares of Vegetable Crops	National Hydrography Dataset Plus (NHD PlusV2)	The National Hydrography Dataset (NHD), GAP Ecological Systems, Ecosystem Markets: Imperiled Species and Habitats, Big game hunting recreation demand	GAP Ecological Systems	National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD)	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-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
[Ecosystem] Provisioning Nutrition Biomass Cultivated crops Materials Biomass Materials from plants, algae and animals for agricultural use	None	[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 Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[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-80	EM-92	EM-132	EM-177	EM-340	EM-417	EM-652
Grasslands (23) Flora (5) Fodder Quality Indicator Green biomass Stock Indicator Agroecosystems (22) Flora (5) Fodder Quality Indicator Green biomass Stock Indicator Composite (8) Presence of environmental class/subclass Quality Indicator	None	Rivers and Streams (111) Composite (8) Regulation of extreme events Extreme Event Indicator Forests (21) Composite (8) Regulation of extreme events Extreme Event Indicator Grasslands (23) Composite (8) Regulation of extreme events Extreme Event Indicator Scrubland/Shrubland (24) Composite (8) Regulation of extreme events Extreme Event Indicator Agroecosystems (22) Composite (8) Regulation of extreme events Extreme Event Indicator	Rivers and Streams (111) Fauna (4) Fish Stock Indicator Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Agroecosystems (22) Fauna (4) Pollinators Stock Indicator	None	Grasslands (23) Fauna (4) Birds 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:

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

Comment:

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)