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-184
EM-306
EM-428
EM-446
EM-651
EM-821
EM-846
EM-862
EM-895

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
EM Short Name em.detail.shortNameHelp ?	ROS (Recreation Opportunity Spectrum), Europe	Urban Temperature, Baltimore, MD, USA	Retained rainwater, Guánica Bay, Puerto Rico	CRPI, St. Croix, USVI	Dickcissel density, CREP, Iowa, USA	Aquatic vertebrate IBI for Western streams, USA	Indigo bunting abund, Piedmont region, USA	Recreational fishery index, USA	HWB indicator-College degree, Great Lakes, USA
EM Full Name em.detail.fullNameHelp ?	ROS (Recreation Opportunity Spectrum), Europe	Urban Air Temperature Change, Baltimore, MD, USA	Retained rainwater, Guánica Bay, Puerto Rico, USA	CRPI (Coral Reef Protection Index, St. Croix, USVI	Dickcissel population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Development of an aquatic vertebrate index of biotic integrity (IBI) for Western streams, USA	Indigo bunting abundance, Piedmont ecoregion, USA	Recreational fishery index for streams and rivers, USA	Human well being indicator-College degree, Great Lakes waterfront, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	i-Tree \| USDA Forest Service	US EPA	US EPA	None	None	None	US EPA	US EPA
EM Source Document ID	293	217	338	335	372	404	405	414	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers
Document Author em.detail.documentAuthorHelp ?	Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G.	Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I.	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever	Pont, D., Hughes, R.M., Whittier, T.R., and S. Schmutz.	Riffel, S., Scognamillo, D., and L. W. Burger	Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick
Document Year em.detail.documentYearHelp ?	2014	2016	2017	2014	2010	2009	2008	2021	None
Document Title em.detail.sourceIdHelp ?	Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU	Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	A Predictive Index of Biotic Integrity Model for A predictive index of biotic integrity model foraquatic-vertebrate assemblages of Western U.S. Streams	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers.	Human well-being and natural capital indictors for Great Lakes waterfront revitalization
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed but unpublished (explain in Comment)
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Journal manuscript submitted or in review

Software and Access

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Maria Luisa Paracchini	Gordon M. Heisler	Susan H. Yee	Susan H. Yee	David Otis	Didier Pont	Sam Riffell	Gregg Lomnicky	Ted Angradi
Contact Address	Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy	5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Centre d’E´ tude du Machinisme Agricole et du Genie Rural, des Eaux et Foreˆts (Cemagref), Unit HYAX Hydrobiologie, 3275 Route de Ce´zanne, Le Tholonet, 13612 Aix en Provence, France	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	200 SW 35th St., Corvallis, OR, 97333	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804
Contact Email	luisa.paracchini@jrc.ec.europa.eu	gheisler@fs.fed.us	yee.susan@epa.gov	yee.susan@epa.gov	dotis@iastate.edu	didier.pont@cemagref.fr	sriffell@cfr.msstate.edu	lomnicky.gregg@epa.gov	tedangradi@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU."	An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation."	AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…"	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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...An alternative index has been developed specifically for coral reefs, the Coral Reef Protection Index (CRPI), that accounts for the continuity of the reef and distance from shore in addition to reef habitat type (Burke et al., 2008): CRPI = ((Reef type + Reef distribution + Reef distance)/10) x 4 where the scaled magnitude of coastal protection due to each factor ranges from 0 (no protection) to 4 (very high protection; Table 2)."	ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Dickcissel (Spiza americana)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: DICK density = 1-1/1+e^(-6.811334 + 1.889878 * bbspath) * e^(-1.831015 + 0.0312571 * hay400)	ABSTRACT: "Because of natural environmental and faunal differences and scientific perspectives, numerous indices of biological integrity (IBIs) have been developed at local, state, and regional scales in the USA. These multiple IBIs, plus different criteria for judging impairment, hinder rigorous national and multistate assessments. Many IBI metrics are calibrated for water body size, but none are calibrated explicitly for other equally important natural variables such as air temperature, channel gradient, or geology. We developed a predictive aquatic-vertebrate IBI model using a total of 871 stream sites (including 162 least-disturbed and 163 most-disturbed sites) sampled as part of the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program survey of 12 conterminous western U.S. states. The selected IBI metrics (calculated from both fish and aquatic amphibians) were vertebrate species richness, benthic native species richness, assemblage tolerance index, proportion of invertivore–piscivore species, and proportion of lithophilic-reproducing species. Mean model IBI scores differed significantly between least-disturbed and most-disturbed sites as well as among ecoregions. Based on a model IBI impairment criterion of 0.44 (risks of type I and II errors balanced), an estimated 34.7% of stream kilometers in the western USA were deemed impaired, compared with 18% for a set of traditional IBIs. Also, the model IBI usually displayed less variability than the traditional IBIs, presumably because it was better calibrated for natural variability. "	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds."	ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.]	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. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Meeting water demands for agriculture and domestic purposes.	None identified	None identified	None reported	None reported	None identified	None identified
Biophysical Context	No additional description provided	One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site.	No additional descriptions provided	No additional description provided	Prairie pothole region of north-central Iowa	Wadeable and boatable streams in 12 western USA states	Conservation Reserve Program lands left to go fallow	None	Waterfront districts on south Lake Michigan and south lake Erie
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	not applicable	N/A	N/A	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application View EM Runs	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	Application of existing model	Application of existing model	Application of existing model ? Comment: Models developed by Quamen (2007).	New or revised model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-290 \| Doc-291 \| Doc-289	Doc-220 \| Doc-219 \| Doc-218	None	None	Doc-372	Doc-403	Doc-405	None	Doc-422
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None	EM-652 \| EM-650 \| EM-649 \| EM-648	EM-820 \| EM-826	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-847	None	EM-886 \| EM-888 \| EM-889 \| EM-890 \| EM-891 \| EM-893 \| EM-894

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	May 5-Sept 30 2006	2006 - 2012	2006-2007, 2010	1992-2007	2004-2005	2008	2013-2014	2022
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	Not applicable	Not applicable	Not applicable	past time	Not applicable	past time	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Hour	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Year	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Geopolitical	Watershed/Catchment/HUC	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)	Geopolitical	Physiographic or ecological	Geopolitical	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	European Union countries	Baltimore, MD	Guanica Bay watershed	Coastal zone surrounding St. Croix	CREP (Conservation Reserve Enhancement Program) wetland sites	Western 12 states	Piedmont Ecoregion	United States	Great Lakes waterfront
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	100-1000 km^2	1-10 km^2	>1,000,000 km^2	100,000-1,000,000 km^2	>1,000,000 km^2	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: 871 total sites surveyed for this work	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially lumped (in all 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	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	length, for linear feature (e.g., stream mile)	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	100 m x 100 m	10m x 10m	30 m x 30 m	10 m x 10 m	multiple, individual, irregular shaped sites	stream reach	Not applicable	stream reach (site)	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Machine learning

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	No	Yes	Unclear	No	Yes	No	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	No	No	No	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	Adjusted R squared \| 0.50 \| unitless	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	Yes	Unclear	Yes ? Comment: Compared to another journal manuscript IBI scores (Whittier et al)	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	Yes	Yes	No	Yes
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Yes	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-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia Bulgaria France Lithuania Croatia Romania Hungary United Kingdom Spain Czech Republic Belgium Finland Denmark Italy Germany Estonia	North America United States Maryland	North America U.S. Caribbean Territories	None	North America United States Iowa	North America United States Oregon Montana Wyoming Colorado South Dakota California North Dakota Arizona Idaho Utah Washington Nevada	Oceania	North America United States North Carolina Indiana Wyoming Northern Mariana Islands Utah Guam Arizona Montana Kentucky California Kansas Delaware Florida Pennsylvania Iowa Mississippi Illinois Texas Connecticut Georgia Maryland Idaho Oregon Vermont Puerto Rico Maine Oklahoma Tennessee American Samoa Alabama Arkansas South Carolina Washington Nebraska West Virginia Colorado Massachusetts Missouri Alaska U.S. Virgin Islands Washington D.C. North Dakota Wisconsin Nevada New York Rhode Island Hawaii South Dakota Minnesota New Jersey Michigan Virgina New Mexico New Hampshire Louisiana Ohio	North America United States Illinois Minnesota Ohio Wisconsin

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

EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Centroid Latitude em.detail.ddLatHelp ?	48.2	39.28	17.96	17.73	42.62	44.2	36.23	36.21	42.26
Centroid Longitude em.detail.ddLongHelp ?	16.35	-76.62	-67.02	-64.77	-93.84	-113.07	-81.9	-113.76	-87.84
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified) \| Created Greenspace \| Atmosphere	Inland Wetlands \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland \| Barren	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Rivers and Streams	Grasslands	Rivers and Streams	Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland \| Barren \| Tundra \| Ice and Snow \| Atmosphere
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	Urban landscape and surrounding area	13 LULC were used	Coral reefs	Grassland buffering inland wetlands set in agricultural land	wadeable and boatable streams	grasslands	reach	Lake Michigan & Lake Erie waterfront
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale 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 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-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Not applicable	Community	Species	Guild or Assemblage	Species	Guild or Assemblage	Not applicable

Taxonomic level and name of organisms or groups identified

EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
None Available	None Available	None Available	None Available	Species Dickcissel (Spiza americana)	other fish amphibians	Species Indigo bunting	None Available	None Available

EnviroAtlas URL

EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Dasymetric Allocation of Population, GAP Ecological Systems, Ecosystem Markets: Imperiled Species and Habitats	Average Annual Precipitation, Percent Impervious Area	The Watershed Boundary Dataset (WBD)	None Available	GAP Ecological Systems	Total Annual Reduced Nitrogen Deposition, U.S. EPA (Omernik) ecoregions	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	None Available	Dasymetric Allocation of Population, GAP Ecological Systems, Enabling Conditions

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-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
[Ecosystem] Cultural Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings] Spiritual and/or emblematic Symbolic 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 Entertainment	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[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 Maintaining nursery populations and habitats	[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 Experiential use of plants, animals and land-/seascapes in different environmental settings	None

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

EM-184	EM-306	EM-428	EM-446	EM-651	EM-821	EM-846	EM-862	EM-895
Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Ice and Snow (26) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Tundra (25) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Lakes and Ponds (112) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Wetlands (12) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Atmosphere Atmosphere (1) Air Site Characteristic Indicator	None	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	Rivers and Streams (111) Fauna (4) Fish Site Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	None

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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)