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-92
EM-224
EM-327
EM-339
EM-465
EM-493
EM-627
EM-657
EM-943

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
EM Short Name em.detail.shortNameHelp ?	Runoff potential of pesticides, Europe	FORCLIM v2.9, West Cascades, OR, USA	ARIES sediment regulation, Puget Sound Region, USA	InVEST crop pollination, NJ and PA, USA	Pharmaceutical product potential, St. Croix, USVI	EnviroAtlas-Carbon sequestered by trees	N removal by wetland restoration, Midwest, USA	REQI (River Ecosystem Quality Index), Italy	Visitation to natural areas, New England, USA
EM Full Name em.detail.fullNameHelp ?	Runoff potential of pesticides, Europe	FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA	ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA	InVEST crop pollination, New Jersey and Pennsylvania, USA	Relative pharmaceutical product potential (on reef), St. Croix, USVI	US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA	Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA	REQI (River Ecosystem Quality Index), Marecchia River, Italy	Estimating natural area use with cell phone data, Narragansett Beach, New England, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	US EPA	ARIES	InVEST	US EPA	US EPA \| EnviroAtlas \| i-Tree	None	None	US EPA
EM Source Document ID	254	23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	302	279	335	223 ? Comment: Additional source: I-tree Eco (doc# 345).	370 ? Comment: Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006.	378	436
Document Author em.detail.documentAuthorHelp ?	Schriever, C. A., and Liess, M.	Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.	Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.	Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf	Yee, S. H., Dittmar, J. A., and L. M. Oliver	US EPA Office of Research and Development - National Exposure Research Laboratory	Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers	Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi	Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin
Document Year em.detail.documentYearHelp ?	2007	2007	2014	2009	2014	2013	2006	2014	2020
Document Title em.detail.sourceIdHelp ?	Mapping ecological risk of agricultural pesticide runoff	Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model	From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments	Modelling pollination services across agricultural landscapes	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	EnviroAtlas - Featured Community	Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins	Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy)	Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA
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	Neither peer reviewed nor published (explain in Comment)	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 on US EPA EnviroAtlas website	Published report	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	http://aries.integratedmodelling.org/	http://www.naturalcapitalproject.org/models/crop_pollination.html	Not applicable	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	https://github.com/USEPA/Recreation_Benefits.git
Contact Name em.detail.contactNameHelp ?	Carola Alexandra Schriever	Richard T. Busing	Ken Bagstad	Eric Lonsdorf	Susan H. Yee	EnviroAtlas Team	William G. Crumpton	Elisa Morri	Nathaniel Merrill
Contact Address	Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany	U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA	Geosciences and Environmental Change Science Center, US Geological Survey	Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Not reported	Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011	Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy	Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America,
Contact Email	carola.schriever@ufz.de	rtbusing@aol.com	kjbagstad@usgs.gov	ericlonsdorf@lpzoo.org	yee.susan@epa.gov	enviroatlas@epa.gov	crumpton@iastate.edu	elisa.morri@uniurb.it	merrill.nathaniel@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
Summary Description em.detail.summaryDescriptionHelp ?	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: "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."	ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape."	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."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…When data on sponge diversity is unavailable, benthic habitat coverages may be used to estimate relative magnitudes of sponge diversity and abundance as an indicator of potential pharmaceutical production (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to potential pharmaceutical production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Pharmaceutical product potential = ΣiciMi where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mi is the relative magnitude of sponge diversity associated with each habitat."	The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group."	ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r	ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed."	ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	European Commission Water Framework Directive (WFD, Directive 2000/60/EC)	None Identified	None identified	None identified	None identified	None identified	None identified	None identified	None identified
Biophysical Context	Not applicable	West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions	No additional description provided	No additional description provided	No additional description provided	No additional description provided	No additional description provided	No additional description provided	Natural area water bodies
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Two scenarios modelled, forests with and without fire	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	More conservative, average and less conservative nitrate loss rate	No scenarios presented	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
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	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist)	Method + Application	Method + Application
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	Application of existing model	New or revised model	New or revised 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-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-255 \| Doc-256 \| Doc-257	Doc-22 \| Doc-23	Doc-303 \| Doc-305	Doc-279	None	Doc-345	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-146 \| EM-208 \| EM-186	None	EM-340 \| EM-338	None	None	None	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
EM Temporal Extent em.detail.tempExtentHelp ?	2000	>650 yrs	1971-2005	2000-2002	2006-2007, 2010	2010-2013	1973-1999	1996-2003 ? Comment: All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map.	2017
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	past time	Not applicable	Not applicable	Not applicable	Not applicable	future time	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	discrete	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	1	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	1
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Day	Year	Not applicable	Not applicable	Not applicable	Not applicable	Day	Not applicable	Day

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Physiographic or ecological	Other	Physiographic or ecological	Geopolitical	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Point or points
Spatial Extent Name em.detail.extentNameHelp ?	EU-15	West Cascades, Oregon	Puget Sound Region	Central New Jersey and east-central Pennsylvania	Coastal zone surrounding St. Croix	Durham NC and vicinity	Upper Mississippi River and Ohio River basins	Marecchia river catchment	Cape Cod
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	10,000-100,000 km^2	1000-10,000 km^2.	100-1000 km^2	100-1000 km^2	>1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
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 distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: Census block groups	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	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	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)
Spatial Grain Size em.detail.spGrainSizeHelp ?	10 km x 10 km	0.08 ha	200m x 200m	30 m x 30 m	10 m x 10 m	irregular	1 km2	500 m x 1000 m	water feature edge (beach)

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Analytic	Analytic	Analytic	Numeric	Numeric	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Yes	Unclear	Yes	No	No	Not applicable	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	No	No	No	Not applicable	Yes ? Comment: Random forest model performance statistics
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	None	None	None	Mean error \| -3.78 \| unitless RMSE \| 262.48 \| unitless Mean absolute error \| 154.84 \| unitless
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	Yes ? Comment: Aggregate native bee abundance on watermelon flowers was measured at 23 sites in 2005. Species richness was measured using the specimens collected from watermelon flowers at the end of the sampling period.	Yes	No	No ? Comment: However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data.	Yes ? Comment: R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF.	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes	No	No	No	No	No	No	Not applicable	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Yes	No	No	No	No	No	No	Not applicable	Yes
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	No	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Unclear

EM Locations, Environments, Ecology

Location of EM Application

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

EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
Europe United Kingdom Portugal Spain Greece Austria Netherlands Sweden Belgium Ireland Luxembourg Finland Denmark Italy France Germany	North America United States Oregon	North America United States	North America United States New Jersey Pennsylvania	None	North America United States North Carolina	North America United States North Carolina New York Indiana Tennessee Kentucky South Dakota Minnesota Pennsylvania Virgina West Virginia Iowa Illinois Missouri Ohio Maryland Wisconsin	Europe Italy	North America United States Rhode Island Massachusetts

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

EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
Centroid Latitude em.detail.ddLatHelp ?	50.01	44.24	48	40.2	17.73	35.99	40.6	43.89	41.72
Centroid Longitude em.detail.ddLongHelp ?	4.67	-122.24	-123	-74.8	-64.77	-78.96	-88.4	12.3	-70.29
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	None provided	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland	Forests	Rivers and Streams \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified) \| Agroecosystems	Near Coastal Marine and Estuarine	Created Greenspace \| Atmosphere	Rivers and Streams \| Inland Wetlands \| Agroecosystems	Rivers and Streams \| Inland Wetlands \| Terrestrial Environment (sub-classes not fully specified)	Lakes and Ponds \| Near Coastal Marine and Estuarine
Specific Environment Type em.detail.specificEnvTypeHelp ?	Arable lands in near-stream environments	Primarily conifer forest	Terrestrial environment surrounding a large estuary	Cropland and surrounding landscape	Coral reefs	Urban and vicinity	Agroecosystems and associated drainage and wetlands	Riparian zone along major river	beaches
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 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 is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Species	Not applicable	Species	Guild or Assemblage	Not applicable	Not applicable	Species ? Comment: Bird species for faunistic index of conservation.	Not applicable

Taxonomic level and name of organisms or groups identified

EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
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	None Available	Species L. Dialictus cressonii L. Dialictus oblongum L. Dialictus nymphaerum L. Dialictus laevissimum Agapostemon sericeus Hylaeus affinis L. Dialictus pilosum Halictus ligatus Peponapis pruinosa L. Dialictus imitatum Xylocopa virginica Bombus perplexus Halictus rubicundus Bombus impatiens Melissodes bimaculata Ptilothrix bombiformis L. Dialictus versatum L. Dialictus illinoense Calliopsis andreniformis L. Dialictus admirandum Bombus fervidus Ceratina calcarata or dupla Lasioglossum coriaceum Agapostemon virescens Bombus bimaculatus Bombus vagans L. Dialictus rohweri Hylaeus modestus Augochlorella aurata Ceratina strenua Halictus confusus Augochloropsis metallica Augochlora pura L. Dialictus bruneri Megachile mendica L. Dialictus tegulare L. Dialictus zephyrum	other Sponges	None Available	None Available	None Available	None Available

EnviroAtlas URL

EM-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
Average Annual Precipitation, Stream Length Impaired by Pesticides, The Watershed Boundary Dataset (WBD), Hectares of Vegetable Crops	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions	GAP Ecological Systems, Average Annual Precipitation, Waterbody area	GAP Ecological Systems	None Available	Carbon Storage by Tree Biomass	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Total Annual Nitrogen Deposition	Ecosystem Markets: Imperiled Species and Habitats	Average Annual Precipitation

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-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
None	[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 Mediation of flows Mass flows Buffering and attenuation of mass flows	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[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 Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters 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 Water conditions Chemical condition of freshwaters	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings

<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-92	EM-224	EM-327	EM-339	EM-465	EM-493	EM-627	EM-657	EM-943
None	None	None	Agroecosystems (22) Fauna (4) Pollinators Stock Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	None	Rivers and Streams (111) Water (3) Liquid water Quality Indicator	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Site Indicator

EcoService Models Library (ESML)

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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)