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-79
EM-193
EM-260
EM-337
EM-414
EM-840
EM-941
EM-968

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
EM Short Name em.detail.shortNameHelp ?	Green biomass production, Central French Alps	Community flowering date, Central French Alps	Divergence in flowering date, Central French Alps	Cultural ecosystem services, Bilbao, Spain	Coral taxa and land development, St.Croix, VI, USA	Rate of Fire Spread	SAV occurrence, St. Louis River, MN/WI, USA	Eastern bluebird abundance, Piedmont region, USA	ESTIMAP - Pollination potential, Iran	EPA Stormwater Manamgement Model
EM Full Name em.detail.fullNameHelp ?	Green biomass production, Central French Alps	Community weighted mean flowering date, Central French Alps	Functional divergence in flowering date, Central French Alps	Cultural ecosystem services, Bilbao, Spain	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Rate of Fire Spread	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Eastern bluebird abundance, Piedmont ecoregion, USA	ESTIMAP - Pollination potential, Iran	Storm Water Management Model User's Manual Version 5.2
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	EU Biodiversity Action 5	None ? Comment: EU Mapping Studies	US EPA	None	US EPA	None	None	US EPA
EM Source Document ID	260	260	260	191	96	306	330	405	434	452
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.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Rothermel, Richard C.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	Riffel, S., Scognamillo, D., and L. W. Burger	Rahimi, E., Barghjelveh, S., and P. Dong	Rossman, L. A., M., Simon
Document Year em.detail.documentYearHelp ?	2011	2011	2011	2013	2011	1972	2013	2008	2020	2022
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	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	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	A Mathematical model for predicting fire spread in wildland fuels	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran)	Storm Water Management Model User's Manual Version 5.2
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	Documented, not peer reviewed	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published USDA Forest Service report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published EPA report

Software and Access

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	http://firelab.org/project/farsite	Not applicable	Not applicable	Not applicable	https://www.epa.gov/water-research/storm-water-management-model-swmm
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	Sandra Lavorel	Izaskun Casado-Arzuaga	Leah Oliver	Charles McHugh	Ted R. Angradi	Sam Riffell	Ehsan Rahini	David Burden
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	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	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	RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808	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	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran	U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	izaskun.casado@ehu.es	leah.oliver@epa.gov	cmchugh@fs.fed.us	angradi.theodore@epa.gov	sriffell@cfr.msstate.edu	ehsanrahimi666@gmail.com	burden.david@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
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."	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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date 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."	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: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.	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."	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 positivelyrelated 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: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors."	EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ? Comment: The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual.
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	None reported	None reported	NA
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	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	Northern Spain; Bizkaia region	nearshore; <1.5 km offshore; <12 m depth	Not applicable	submerged aquatic vegetation	Conservation Reserve Program lands left to go fallow	None additional	NA
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	N/A	N/A	NA

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method Only	Method + Application	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	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model

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

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-260 \| Doc-269	Doc-260 \| Doc-269	None	None	None	None	Doc-405	Doc-432	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-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-80 \| EM-81 \| EM-82 \| EM-83	None	None	None	None	EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-846 \| EM-847	EM-939	EM-971

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2007-2008	2007-2008	2000 - 2007	2006-2007	Not applicable	2010 - 2012	2008	2020	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	Not applicable	time-stationary	time-stationary	time-stationary	time-dependent
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	both
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	continuous
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-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Physiographic or Ecological	Not applicable	Physiographic or ecological	Physiographic or ecological	Geopolitical	No location (no locational reference given)
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	Central French Alps	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	Not applicable	St. Louis River Estuary	Piedmont Ecoregion	Iran	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	10-100 km^2	100-1000 km^2	10-100 km^2	Not applicable	10-100 km^2	100,000-1,000,000 km^2	>1,000,000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
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)	Not applicable	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 lumped (in all cases)	spatially distributed (in at least some cases) ? Comment: Varies by inputs, but results are for areas of country	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	Not applicable	Not applicable	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	20 m x 20 m	2 m x 2 m	Not applicable	Not applicable	0.07 m^2 to 0.70 m^2	Not applicable	ha^2	mm

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	None	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-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	No	Yes	Not applicable	Yes	Yes	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	Yes	No	Yes	Not applicable	Yes	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 69.9 \| Not applicable	R squared \| 45.2 \| Not applicable	R squared \| 35.5 \| Not applicable	None	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	Yes	No	No	Yes	No	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	Yes	Not applicable	No	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	Not applicable	No	Yes	No	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-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
Europe France	Europe France	Europe France	Europe Spain	None	None	North America United States Minnesota Wisconsin	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	Europe ? Comment: Model for Iran - no form preset id for country	None

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

EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	45.05	43.25	17.75	-9999	46.72	36.23	32.29	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	6.4	-2.92	-64.75	-9999	-96.13	-81.9	53.68	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	NAD83	Not applicable	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Provided	Provided	Estimated	Not applicable	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	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	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Grasslands	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	Subalpine terraces, grasslands, and meadows	none	stony coral reef	Not applicable	Freshwater estuarine system	grasslands	terrestrial land types	User-defined catchments
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Not applicable	Ecological scale is coarser than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale 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 is finer than that of the Environmental Sub-class	Other or unclear (comment)

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Community	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
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 Eastern bluebird	Species Apis mellifera Apis florea	None Available

EnviroAtlas URL

EM-65	EM-71	EM-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
GAP Ecological Systems	None Available	None Available	Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	Average Annual Precipitation	Average Annual Precipitation	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Precipitation, Waterbody area	None Available

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-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
None	None	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 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	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Provisioning Materials Water Ground water for non-drinking purposes Surface water for non-drinking purposes [Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection

<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-79	EM-193	EM-260	EM-337	EM-414	EM-840	EM-941	EM-968
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	Grasslands (23) Fauna (4) Birds Stock Indicator	Forests (21) Fauna (4) Wildlife Stock Indicator Scrubland/Shrubland (24) Fauna (4) Wildlife Stock Indicator Urban/Surburban (27) Fauna (4) Wildlife Stock Indicator	Rivers and Streams (111) Composite (8) Regulation of extreme events Flow Indicator Groundwater Water (3) Liquid water Flow Indicator Lakes and Ponds (112) Composite (8) Regulation of extreme events Flow Indicator Urban/Surburban (27) Composite (8) Regulation of extreme events Flow Indicator

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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)