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-65
EM-71
EM-87
EM-193
EM-260
EM-414
EM-493
EM-541
EM-684
EM-889
EM-1010

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
EM Short Name em.detail.shortNameHelp ?	Green biomass production, Central French Alps	Community flowering date, Central French Alps	Area & hotspots of soil accumulation, South Africa	Cultural ecosystem services, Bilbao, Spain	Coral taxa and land development, St.Croix, VI, USA	SAV occurrence, St. Louis River, MN/WI, USA	EnviroAtlas-Carbon sequestered by trees	InVEST fisheries, lobster, South Africa	Beach visitation, Barnstable, MA, USA	HWB poor health, Great Lakes waterfront, USA	ORVal, Valuing recreational sites, W. Norwich, UK
EM Full Name em.detail.fullNameHelp ?	Green biomass production, Central French Alps	Community weighted mean flowering date, Central French Alps	Area and hotspots of soil accumulation, South Africa	Cultural ecosystem services, Bilbao, Spain	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA	Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa	Beach visitation, Barnstable, Massachusetts, USA	Human well being indicator-poor health, Great Lakes waterfront, USA	ORVal short case study: number 1 valuing recreational sites in West Norwich (version 2.0)
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	None	None ? Comment: EU Mapping Studies	US EPA	US EPA	US EPA \| EnviroAtlas \| i-Tree	InVEST	US EPA	None	None
EM Source Document ID	260	260	271	191	96	330	223 ? Comment: Additional source: I-tree Eco (doc# 345).	349 ? Comment: Supplemented with the InVEST Users Guide fisheries.	386	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers	476
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	US EPA Office of Research and Development - National Exposure Research Laboratory	Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby	Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick	Day, B., & Smith
Document Year em.detail.documentYearHelp ?	2011	2011	2008	2013	2011	2013	2013	2018	2018	None	None
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Mapping ecosystem services for planning and management	Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning	Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	EnviroAtlas - Featured Community	Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals	Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts	Human well-being and natural capital indictors for Great Lakes waterfront revitalization	ORVal short case study: number 1 valuing recreational sites in West Norwich (version 2.0)
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 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 on US EPA EnviroAtlas website	Published journal manuscript	Published journal manuscript	Journal manuscript submitted or in review	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://www.epa.gov/enviroatlas	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	https://www.leep.exeter.ac.uk/orval/
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	Benis Egoh	Izaskun Casado-Arzuaga	Leah Oliver	Ted R. Angradi	EnviroAtlas Team	Michelle Ward	Kate K, Mulvaney	Ted Angradi	Brett Day
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain	National Health and Environmental Research Effects Laboratory	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	Not reported	ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia	Not reported	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804	None provided
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	Not reported	izaskun.casado@ehu.es	leah.oliver@epa.gov	angradi.theodore@epa.gov	enviroatlas@epa.gov	m.ward@uq.edu.au	Mulvaney.Kate@EPA.gov	tedangradi@gmail.com	Brett.Day@exeter.ac.uk

EM Description

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."	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: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables."	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover."	ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management."	AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294)	ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."	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."	AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area."	ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression.	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."	The ORVal Tool is a web application (accessed at https://www.leep.exeter.ac.uk/orval) developed by the Land, Environment, Economics and Policy (LEEP) Institute at the University of Exeter with support from DEFRA. ORVal’s primary purpose is to help quantify the benefits that are derived from accessible outdoor recreation areas in England. Those outdoor recreation areas, or greenspaces, include an array of features such as beaches, parks, nature reserves and country paths This short case study uses ORVal to explore the visits and welfare values that are generated by existing greenspaces in West Norwich. It provides both practical details as to how tasks are performed in ORVal and explanations as to how the numbers reported by the Tool should be interpreted.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	Land management, ecosystem management, response to EU 2020 Biodiversity Strategy	Not applicable	None identified	None identified	Future rock lobster fisheries management	To assess the number of people who would be impacted by beach closures.	None identified	None provided
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	Northern Spain; Bizkaia region	nearshore; <1.5 km offshore; <12 m depth	submerged aquatic vegetation	No additional description provided	No additional description provided	Four separate beaches within the community of Barnstable	Waterfront districts on south Lake Michigan and south lake Erie	Urban greenspaces in the UK
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	Not applicable	No scenarios presented	No scenarios presented	Fisheries exploitation; fishing vulnerability (of age classes)	No scenarios presented	N/A	NA

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	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	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-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-260 \| Doc-269	Doc-271	None	None	None	Doc-345	None	Doc-386 \| Doc-387	Doc-422	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-85 \| EM-86 \| EM-88	None	None	None	None	None	EM-682 \| EM-685 \| EM-683 \| EM-686	EM-886 \| EM-888 \| EM-890 \| EM-891 \| EM-893 \| EM-894 \| EM-895	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2007-2008	Not reported	2000 - 2007	2006-2007	2010 - 2012	2010-2013	1986-2115	2011 - 2016	2022	2016-2018
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-dependent	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	future time	past time	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	discrete	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	1	1	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	Year	Day	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Geopolitical	Geopolitical	Physiographic or ecological	Geopolitical	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	South Africa	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	St. Louis River Estuary	Durham NC and vicinity	Table Mountain National Park Marine Protected Area	Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach)	Great Lakes waterfront	West Norwich
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	>1,000,000 km^2	100-1000 km^2	10-100 km^2	10-100 km^2	100-1000 km^2	100-1000 km^2	10-100 ha	1000-10,000 km^2.	1-10 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases) ? Comment: BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively.	spatially distributed (in at least some cases) ? Comment: Census block groups	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	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	Not applicable	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	length, for linear feature (e.g., stream mile)	Not applicable	map scale, for cartographic feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	Distributed across catchments with average size of 65,000 ha	2 m x 2 m	Not applicable	0.07 m^2 to 0.70 m^2	irregular	Not applicable	by beach site	Not applicable	Not reported

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric	Numeric	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	None	Conventional (frequentist) 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-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	No	Yes	Yes	No	No	Yes	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	No	No	Yes	Yes	No	No	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 69.9 \| Not applicable	R squared \| 45.2 \| Not applicable	None	None	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	Yes	No	Yes	No	Yes ? Comment: A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data.	No	No	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	Yes	No	No	No	No	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	No	Yes	Yes	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
Europe France	Europe France	Africa South Africa	Europe Spain	None	North America United States Minnesota Wisconsin	North America United States North Carolina	None	North America United States Massachusetts	North America United States Illinois Minnesota Ohio Wisconsin	Europe United Kingdom

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

EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	Temperate South Africa South Africa Benguela Namaqua	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	-30	43.25	17.75	46.72	35.99	-34.18	41.64	42.26	52.62
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	25	-2.92	-64.75	-96.13	-78.96	18.35	-70.29	-87.84	1.25
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	NAD83	WGS84	None provided	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Estimated	Provided	Estimated	Estimated	Estimated	Provided	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Created Greenspace \| Atmosphere	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Created Greenspace
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	Not applicable	none	stony coral reef	Freshwater estuarine system	Urban and vicinity	Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf	Saltwater beach	Lake Michigan & Lake Erie waterfront	Urban Greenspaces
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Not applicable	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	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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Not applicable	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Individual or population, within a species	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
None Available	None Available	None Available	None Available	Genus Acropora Porites spp. Diploria spp. Species Mycetophylia lamarckiana Mycetophylia ferox Montastrea cavernosa Dichocoenia stokesii Meandrina meandrites Agaricia tenuifolia Agaricia fragilis Montastrea annularis Agaricia agaricites Dendrogyra cylindricus Millepora complanata Montastrea faveolata Porites astreoides Montastrea franksii	None Available	None Available	Species West Coast Rock Lobster (Jasus lalandii)	None Available	None Available	None Available

EnviroAtlas URL

EM-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
GAP Ecological Systems	None Available	None Available	Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	Average Annual Precipitation	Carbon Storage by Tree Biomass	Big game hunting recreation demand	Average Annual Precipitation	Enabling Conditions	Dasymetric Allocation of Population, Green Space per Capita, Employment Rate

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-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
None	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[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 Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Maintenance of physical, chemical, abiotic conditions By natural chemical and physical processes Not applicable	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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] 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-65	EM-71	EM-87	EM-193	EM-260	EM-414	EM-493	EM-541	EM-684	EM-889	EM-1010
Grasslands (23) Flora (5) Green biomass Stock Indicator Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None	None	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	None	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Site Indicator	None	None

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

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)