EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.

This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:

Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
Intermediate (Computed) Variables
Response Variables
- Computed Response Variables
- Measured Response Variables

EM Variables by Category

A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:

Policy Regarding Use or Management of Ecosystem Resources
Land Surface (or Water Body Bed) Cover, Use or Substrate
Human Demographic Data
Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
Monetary Values

Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.

All EM Descriptors

This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:

EM Identity and Description
EM Modeling Approach
EM Locations, Environments, Ecology
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

EM Descriptors by Modeling Concepts

This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:

Conceptual Model
Modeling Objective
Modeling Context
Potential for Model Linkage
Feasibility of Model Use
Model Certainty
Model Structural Information

Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.

Compare Criteria:

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Comparing:

EM-65
EM-71
EM-103
EM-154
EM-260
EM-414
EM-418
EM-944

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
EM Short Name em.detail.shortNameHelp ?	Green biomass production, Central French Alps	Community flowering date, Central French Alps	Birds in estuary habitats, Yaquina Estuary, WA, USA	Mangrove development, Tampa Bay, FL, USA	Coral taxa and land development, St.Croix, VI, USA	SAV occurrence, St. Louis River, MN/WI, USA	SIRHI, St. Croix, USVI	COBRA v 4.1
EM Full Name em.detail.fullNameHelp ?	Green biomass production, Central French Alps	Community weighted mean flowering date, Central French Alps	Bird use of estuarine habitats, Yaquina Estuary, WA, USA	Mangrove wetland development, Tampa Bay, FL, USA	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	SIRHI (SImplified Reef Health Index), St. Croix, USVI	COBRA (CO–Benefits Risk Assessment) v 4.1
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	US EPA	US EPA	US EPA	US EPA	US EPA	US EPA
EM Source Document ID	260	260	275	97	96	330	335	437 ? Comment: User's manual is provided at the webpage.
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.	Frazier, M. R., Lamberson, J. O. and Nelson, W. G.	Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L.	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	Yee, S. H., Dittmar, J. A., and L. M. Oliver	US EPA
Document Year em.detail.documentYearHelp ?	2011	2011	2014	2012	2011	2013	2014	2021
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	Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary	Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence	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	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA)
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
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	Webpage

Software and Access

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://www.epa.gov/cobra
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	M. R. Frazier ? Comment: Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA	Michael Osland	Leah Oliver	Ted R. Angradi	Susan H. Yee	Emma Zinsmeister
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	Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365	U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561	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	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	frazier@nceas.ucsb.edu	mosland@usgs.gov	leah.oliver@epa.gov	angradi.theodore@epa.gov	yee.susan@epa.gov	zinsmeister.emma@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
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: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds."	ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses."	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."	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 indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati	Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	Not applicable	Not applicable	None identified	None identified	None identified
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	Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh	mangrove forest,Salt marsh, estuary, sea level,	nearshore; <1.5 km offshore; <12 m depth	submerged aquatic vegetation	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Not applicable	Not applicable	No scenarios presented	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
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 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	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-103	EM-154	EM-260	EM-414	EM-418	EM-944
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-260 \| Doc-269	None	None	None	None	None	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	None	None	None	None	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2007-2008	December 2007 - November 2008	1990-2010	2006-2007	2010 - 2012	2006-2007, 2010	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	Not applicable
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	future time	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	continuous	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
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	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-103	EM-154	EM-260	EM-414	EM-418	EM-944
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Physiographic or ecological	Physiographic or Ecological	Physiographic or Ecological	Physiographic or ecological	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	Yaquina Estuary (intertidal), Oregon, USA	Tampa Bay	St.Croix, U.S. Virgin Islands	St. Louis River Estuary	Coastal zone surrounding St. Croix	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	1-10 km^2	100-1000 km^2	10-100 km^2	10-100 km^2	100-1000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
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)	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 (habitat type)	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	map scale, for cartographic feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	0.87-104.29 ha	m^2	Not applicable	0.07 m^2 to 0.70 m^2	10 m x 10 m	user defined

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Unclear	No	Yes	Yes	Yes	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	No	No	Yes	Yes	No	Not applicable
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
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	No	No	Yes	Yes	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Yes	Yes	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	Yes	No	No	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	No	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-103	EM-154	EM-260	EM-414	EM-418	EM-944
Europe France	Europe France	North America United States Oregon	North America United States Florida	None	North America United States Minnesota Wisconsin	None	None

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

EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
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 Floridian ? Comment: Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	44.62	27.8	17.75	46.72	17.73	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	-124.06	-82.4	-64.75	-96.13	-64.77	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	None provided	WGS84	NAD83	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Provided	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	Estuarine intertidal	Created Mangrove wetlands	stony coral reef	Freshwater estuarine system	Coral reefs	Not applicable
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Not applicable	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale 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-103	EM-154	EM-260	EM-414	EM-418	EM-944
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Guild or Assemblage	Not applicable	Guild or Assemblage	Not applicable	Guild or Assemblage	Not applicable

Taxonomic level and name of organisms or groups identified

EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
None Available	None Available	other Pelicans/cormorants Raptors Shorebirds Songbirds Loons/grebes Genus Geese Family Rails Corvids Herons/egrets Alcids Terns/gulls Ducks	Species Laguncularia racemosa Avicennia germinans Rhizophora mangle Spartina alterniflora	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	other snapper Family grouper parrotfish surgeonfish	None Available

EnviroAtlas URL

EM-65	EM-71	EM-103	EM-154	EM-260	EM-414	EM-418	EM-944
GAP Ecological Systems	None Available	None Available	None Available	None Available	Average Annual Precipitation	None Available	Total Annual Nitrogen Deposition

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-103	EM-154	EM-260	EM-414	EM-418	EM-944
None	None	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[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 Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems

<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-103	EM-154	EM-260	EM-414	EM-418	EM-944
Grasslands (23) Flora (5) Green biomass Stock Indicator Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Birds Stock Indicator Ducks Stock Indicator Geese Stock Indicator	Near Coastal Marine/Estuarine (113) Flora (5) Trees Stock Indicator Seaweed Stock Indicator Composite (8) Regulation of extreme events Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	None	None	None

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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)