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-80
EM-133
EM-419
EM-453
EM-654
EM-668
EM-774
EM-843
EM-893
EM-972

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
EM Short Name em.detail.shortNameHelp ?	Agronomic ES and plant traits, Central French Alps	Flood regulation supply-demand, Etropole, Bulgaria	ARIES viewsheds, Puget Sound Region, USA	Reef density of E. striatus, St. Croix, USVI	Forest recreation, Wisconsin, USA	Fish nutrient cycling , Ohio, USA	Plant-pollinator networks at reclaimed mine, USA	Mourning dove abundance, Piedmont region, USA	HWB indicator-ADI, Great Lakes, USA	NC HUC-12 conservation prioritization tool
EM Full Name em.detail.fullNameHelp ?	Agronomic ecosystem service estimated from plant functional traits, Central French Alps	Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria	ARIES (Artificial Intelligence for Ecosystem Services) Scenic viewsheds for homeowners, Puget Sound Region, Washington, USA	Relative density of Epinephelus striatus (on reef), St. Croix, USVI	Forest recreation, Wisconsin, USA	Nutrient Cycling by gizzard shad, Ohio, USA	Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA	Mourning dove abundance, Piedmont ecoregion, USA	Human well being indicator- Area Deprivation Index (ADI) , Great Lakes waterfront, USA	NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	ARIES	US EPA	None	None	None	None	US EPA	None
EM Source Document ID	260	248	302	335	376	385	397	405	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers	443 ? Comment: Doc 444 is an additional source for this EM
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Nedkov, S., Burkhard, B.	Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Qiu, J. and M. G. Turner	Vanni, M.J., Bowling, A.M., Dickman, E.M., Hale, R.S., Higgins, K.A., Horgan, M.J., Knoll, L.B., Renwick, W.H., and R.A. Stein	Cusser, S. and K. Goodell	Riffel, S., Scognamillo, D., and L. W. Burger	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick	Warnell, K., I. Golden, and C. Canfield
Document Year em.detail.documentYearHelp ?	2011	2012	2014	2014	2013	2006	2013	2008	None	2023
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria	From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Spatial interactions among ecosystem services in an urbanizing agricultural watershed	Nutrient cycling by fish supports relatively more primary production as lake productivity increases	Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Human well-being and natural capital indictors for Great Lakes waterfront revitalization	Conservation planning tools for NC's people & nature
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	Peer reviewed and published	Peer reviewed but unpublished (explain in Comment)	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Journal manuscript submitted or in review	Webpage

Software and Access

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	http://aries.integratedmodelling.org/	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Stoyan Nedkov	Ken Bagstad	Susan H. Yee	Monica G. Turner	Michael Vanni	Sarah Cusser ? Comment: Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A.	Sam Riffell	Ted Angradi	Katie Warnell
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria	Geosciences and Environmental Change Science Center, US Geological Survey	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Not reported	Dept of Environmental toxocology, C.emson Univ. Pendleton, South Carolina 29670, USA	Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A.	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804	Not reported
Contact Email	sandra.lavorel@ujf-grenoble.fr	snedkov@abv.bg	kjbagstad@usgs.gov	yee.susan@epa.gov	turnermg@wisc.edu	vannimj@muohio.edu	sarah.cusser@gmail.com	sriffell@cfr.msstate.edu	tedangradi@gmail.com	katie.warnell@duke.edu

EM Description

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
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: "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. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa."	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: "Within a given viewshed, our models quantified the contribution of viewshed source features such as mountains and water bodies and sinks that detract from view quality, including obstructions or visual blight such as industrial or commercial development. Source, sink, and use locations were linked by a flow model that computed visibility along lines of sight from use locations to scenic viewshed features. The model includes a distance decay function that accounts for changes with distance in the value of views. We then computed the ratio of actual to theoretical provision of scenic views to compare the values accruing to homeowners relative to those for the entire landscape."	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...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij 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 Mij is the magnitude associated with each habitat for a given metric j: density of E. striatus"	AUTHOR'S DESCRIPTION (from Supporting Information): "Forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area. Several assumptions were made for this assessment approach: larger areas and places with more recreational opportunities would provide more recreational service; areas near large population centers would be visited and used more than remote areas; and proximity to major roads would increase access and thus recreational use of an area… we quantified forest recreation service for each 30-m grid cells using the equation below: FRSi = Ai Σ(Oppti + Popi +Roadi), where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5,200) to a range of 0–100, with 0 representing no forest recreation service and 100 representing highest service.	ABSTRACT: "Animals can be important in nutrient cycling in particular ecosystems, but few studies have examined how this importance varies along environmental gradients. In this study we quantified the nutrient cycling role of an abundant detritivorous fish species, the gizzard shad (Dorosoma cepedianum), in reservoir ecosystems along a gradient of ecosystem productivity. Gizzard shad feed mostly on sediment detritus and excrete sediment-derived nutrients into the water column, thereby mediating a cross-habitat translocation of nutrients to phytoplankton. We quantified nitrogen and phosphorus cycling (excretion) rates of gizzard shad, as well as nutrient demand by phytoplankton, in seven lakes over a four-year period (16 lake-years). The lakes span a gradient of watershed land use (the relative amounts of land used for agriculture vs. forest) and productivity. As the watersheds of these lakes became increasingly dominated by agricultural land, primary production rates, lake trophic state indicators (total phosphorus and chlorophyll concentrations), and nutrient flux through gizzard shad populations all increased. Nutrient cycling by gizzard shad supported a substantial proportion of primary production in these ecosystems, and this proportion increased as watershed agriculture (and ecosystem productivity) increased. In the four productive lakes with agricultural watersheds (.78% agricultural land), gizzard shad supported on average 51% of phytoplankton primary production (range 27–67%). In contrast, in the three relatively unproductive lakes in forested or mixed-land-use watersheds (.47% forest, ,52% agricultural land), gizzard shad supported 18% of primary production (range 14–23%). Thus, along a gradient of forested to agricultural landscapes, both watershed nutrient inputs and nutrient translocation by gizzard shad increase, but our data indicate that the importance of nutrient translocation by gizzard shad increases more rapidly. Our results therefore support the hypothesis that watersheds and gizzard shad jointly regulate primary production in reservoir ecosystems "	ABSTRACT: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context."	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. "	ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context."	ABSTRACT: "Conservation organizations and land trusts in North Carolina are increasingly focused on how their work can contribute to both human and ecosystem resilience and adaptation to climate change, as well as directly mitigate climate change through carbon storage and sequestration. Recent state executive and legislative actions also underscore the importance of natural systems for climate adaptation and mitigation, and may provide additional funding for conservation and restoration for those purposes in the near term. To make it more efficient for conservation organizations working in North Carolina to consider a broad suite of conservation benefits in their work, the Conservation Trust for North Carolina and the Nicholas Institute for Energy, Environment & Sustainability at Duke University have developed two online tools for identifying priority areas for conservation action and estimating benefit metrics for specific properties. The conservation prioritization tool finds the sub-watersheds in North Carolina with the greatest potential to provide a set of user-selected conservation benefits. It allows users to identify priority areas for future conservation work within the entire state or a defined region. This high-level tool allows for quick and easy exploration without the need for spatial analysis expertise."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified	None identified	None identified	None identified	None reported	None identified	Allows users to prioritize HUCs within their area of interest based on their conservation goals.
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	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	No additional description provided	Lakes	The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils.	Conservation Reserve Program lands left to go fallow	Waterfront districts on south Lake Michigan and south lake Erie	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	Lake productivity	No scenarios presented	N/A	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised 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-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-270	Doc-248	Doc-303 \| Doc-305	None	None	None	None	Doc-405	Doc-422	Doc-444 ? Comment: The secondary source, document 444, is the website for running the tool.
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	EM-130 \| EM-132	None	None	None	None	None	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-844 \| EM-845 \| EM-846 \| EM-847	EM-886 \| EM-888 \| EM-889 \| EM-890 \| EM-891 \| EM-894 \| EM-895	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	Not reported	1992-2006	2006-2007, 2010	2000-2006	2000-2003	2009-2010	2008	2022	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Watershed/Catchment/HUC	Physiographic or ecological	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Physiographic or ecological	Geopolitical	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Municipality of Etropole	Puget Sound Region	Coastal zone surrounding St. Croix	Yahara Watershed, Wisconsin	Lakes in Ohio	The Wilds	Piedmont Ecoregion	Great Lakes waterfront	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	100-1000 km^2	10,000-100,000 km^2	100-1000 km^2	1000-10,000 km^2.	100,000-1,000,000 km^2	1-10 km^2	100,000-1,000,000 km^2	1000-10,000 km^2.	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: Distributed by land cover and soil type polygons	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 lumped (in all cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	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	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	Not applicable	Not applicable	map scale, for cartographic feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	Distributed by irregular land cover and soil type polygons	200m x 200m	10 m x 10 m	30m x 30m	Not applicable	10 m radius	Not applicable	Not applicable	HUC 12

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic	Numeric	Other or unclear (comment)
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Other or unclear (comment)

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Yes	No	Yes ? Comment: Nitrogen and Phosphorus excretion rates were calibrated by lake and fish size class.	Not applicable	Yes	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	No	No	Not applicable	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	Yes	No	No	Yes	No	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	Yes	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	Yes	Yes	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Unclear	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-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
Europe France	Europe Bulgaria	North America United States Washington	None	North America United States Wisconsin	North America United States Ohio	North America United States Ohio	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	North America United States Illinois Minnesota Ohio Wisconsin	None

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

EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
Centroid Latitude em.detail.ddLatHelp ?	45.05	42.8	48	17.73	43.1	40.15	39.82	36.23	42.26	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	24	-123	-64.77	-89.4	-82.95	-81.75	-81.9	-87.84	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Provided	Estimated	Provided	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Rivers and Streams \| Lakes and Ponds \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland	Lakes and Ponds \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands	Lakes and Ponds	Grasslands	Grasslands	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows.	Mountainous flood-prone region	Terrestrial environment surrounding a large estuary	Coral reefs	Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape	Reservoirs	Grassland	grasslands	Lake Michigan & Lake Erie waterfront	Terrestrial and freshwater aquatic
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 corresponds to 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 corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is coarser 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-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Species	Species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
None Available	None Available	None Available	Species Epinephelus striatus	None Available	Species Dorosoma cepedianum (Gizzard Shad)	Species Planted plant species Pollinator species	Species Mourning dove	None Available	None Available

EnviroAtlas URL

EM-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
GAP Ecological Systems, Carbon storage by tree biomass (kg/m2)	None Available	GAP Ecological Systems, Waterbody area	None Available	Dasymetric Allocation of Population	Average Annual Precipitation, Waterbody area, The Watershed Boundary Dataset (WBD)	GAP Ecological Systems	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	GAP Ecological Systems, Enabling Conditions	GAP Ecological Systems, Green Space per Capita, Ecosystem Markets: Imperiled Species and Habitats, Percent GAP Status 1 & 2, Carbon Storage by Tree Biomass

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-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
[Ecosystem] Provisioning Nutrition Biomass Cultivated crops Materials Biomass Materials from plants, algae and animals for agricultural use	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Intellectual and representative interactions Aesthetic Entertainment	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings	[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	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	None	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-80	EM-133	EM-419	EM-453	EM-654	EM-668	EM-774	EM-843	EM-893	EM-972
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	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 Lakes and Ponds (112) Composite (8) Regulation of extreme events Extreme Event Indicator Agroecosystems (22) Composite (8) Regulation of extreme events Extreme Event Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Site Indicator Forests (21) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Site Indicator Lakes and Ponds (112) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Site Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	None	None	None	Grasslands (23) Fauna (4) Birds Stock Indicator	None	None

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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

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)