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-63
EM-337
EM-461
EM-542
EM-660
EM-698
EM-709
EM-788
EM-892

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
EM Short Name em.detail.shortNameHelp ?	EnviroAtlas - Natural biological nitrogen fixation	Rate of Fire Spread	Presence of Euchema sp., St. Croix, USVI	Coastal protection in Belize	RUM: Valuing fishing quality, Michigan, USA	Fish species richness, St. Croix, USVI	Pollinators on landfill sites, United Kingdom	Wild bees over 26 yrs of restored prairie, IL, USA	VELMA v. 2.1 contaminant modeling
EM Full Name em.detail.fullNameHelp ?	US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA	Rate of Fire Spread	Relative presence of Euchema sp. (on reef), St. Croix, USVI	Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize:	Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA	Fish Species Richness, Buck Island, St. Croix , USVI	Pollinating insects on landfill sites, East Midlands, United Kingdon	Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA	VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA \| EnviroAtlas	None	US EPA	InVEST	None	None	None	None	US EPA
EM Source Document ID	262 ? Comment: EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM.	306	335	350	382 ? Comment: Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010.	355	389	401	423 ? Comment: Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430.
Document Author em.detail.documentAuthorHelp ?	US EPA Office of Research and Development - National Exposure Research Laboratory	Rothermel, Richard C.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes	Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson	Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco	Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin	Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree	McKane
Document Year em.detail.documentYearHelp ?	2013	1972	2014	2016	2014	2007	2013	2017	None
Document Title em.detail.sourceIdHelp ?	EnviroAtlas - National	A Mathematical model for predicting fire spread in wildland fuels	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience	Valuing recreational fishing quality at rivers and streams	Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean	Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects	Wild bee community change over a 26-year chronosequence of restored tallgrass prairie	Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Documented, not peer reviewed	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 on US EPA EnviroAtlas website	Published USDA Forest Service report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published EPA report

Software and Access

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.epa.gov/enviroatlas	http://firelab.org/project/farsite	Not applicable	Not identified in paper	Not applicable	Not applicable	Not applicable	Not applicable	https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355
Contact Name em.detail.contactNameHelp ?	EnviroAtlas Team ? Comment: Additional contact: Jana Compton, EPA	Charles McHugh	Susan H. Yee	Greg Guannel	Richard Melstrom	Simon Pittman	Sam Tarrant	Sean R. Griffin	Robert B. McKane
Contact Address	Not reported	RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	The Nature Conservancy, Coral Gables, FL. USA	Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA	1305 East-West Highway, Silver Spring, MD 20910, USA	RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K.	Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A.	US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333
Contact Email	enviroatlas@epa.gov	cmchugh@fs.fed.us	yee.susan@epa.gov	greg.guannel@gmail.com	melstrom@okstate.edu	simon.pittman@noaa.gov	sam.tarrant@rspb.org.uk	srgriffin108@gmail.com	mckane.bob@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
Summary Description em.detail.summaryDescriptionHelp ?	DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM."	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: "...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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(4) density of Euchema sp. seaweed,"	AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems."	ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. "	ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems."	ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each	ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities."	ABSTRACT: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None Identified	None identified	None identified	Future rock lobster fisheries management	None identified	None provided	None identified	None identified	None identified
Biophysical Context	No additional description provided	Not applicable	No additional description provided	barrier reef and fringing reef in nearshore coastal marine system	stream and river reaches of Michigan	Hard and soft benthic habitat types approximately to the 33m isobath	No additional description provided	The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season.	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions	targeted sport fish biomass	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method Only	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-346 \| Doc-347 ? Comment: EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM.	None	None	None	None	Doc-355	Doc-389	None	Doc-430
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None	None	EM-590 \| EM-699	EM-697	None	EM-883 \| EM-884 \| EM-887

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
EM Temporal Extent em.detail.tempExtentHelp ?	2006-2010	Not applicable	2006-2007, 2010	2005-2013	2008-2010	2000-2005	2007-2008	1988-2014	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	Not applicable	time-stationary	time-dependent	time-stationary	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
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	Not applicable	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable	1
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Second	Not applicable	Not applicable	Not applicable	Not applicable	Day

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Not applicable	Physiographic or ecological	Geopolitical	Watershed/Catchment/HUC	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	counterminous United States	Not applicable	Coastal zone surrounding St. Croix	Coast of Belize	HUCS in Michigan	SW Puerto Rico,	East Midlands	Nachusa Grasslands	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	Not applicable	100-1000 km^2	100-1000 km^2	100,000-1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	10-100 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Watersheds (12-digit HUCs).	Not applicable	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)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	area, for pixel or radial feature	length, for linear feature (e.g., stream mile)	other (specify), for irregular (e.g., stream reach, lake basin)	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)	volume, for 3-D feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	irregular	Not applicable	10 m x 10 m	1 meter	reach in HUC	not reported	multiple unrelated locations	Area varies by site	user defined

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
Model Calibration Reported? em.detail.calibrationHelp ?	No	Not applicable	Yes	No	No	No	Not applicable	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Not applicable	No	No	Yes	Yes	Not applicable	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	R squared \| 0.552 \| unitless	R squared \| 0.48 \| unitless Cohen's Kappa statistic \| 0.46 \| unitless AUC \| 0.76 \| unitless	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Yes	No ? Comment: Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666.	No	Yes	Not applicable	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Not applicable	No	No	No	No	Not applicable	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Not applicable	No	No	No	Yes	Not applicable	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	No	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-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
North America United States	None	None	North America Belize	North America United States Michigan	None	Europe United Kingdom	North America United States Illinois	None

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

EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Western Caribbean	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
Centroid Latitude em.detail.ddLatHelp ?	39.5	-9999	17.73	18.63	45.12	17.79	52.22	41.89	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-98.35	-9999	-64.77	-88.22	85.18	-64.62	-0.91	-89.34	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Not applicable	Estimated	Estimated	Estimated	Estimated	Estimated	Provided	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Rivers and Streams	Near Coastal Marine and Estuarine	Created Greenspace \| Grasslands	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Terrestrial	Not applicable	Coral reefs	coral reefs	stream reaches	shallow coral reefs	restored landfills and grasslands	Restored prairie, prairie remnants, and cropland	Terrestrial environment
EM Ecological Scale em.detail.ecoScaleHelp ?	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 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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Species	Guild or Assemblage	Not applicable	Guild or Assemblage	Individual or population, within a species	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
None Available	None Available	Genus Euchema	None Available	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye	other (fish class)	Genus Calliopum Lasioglossum Species Apis mellifera Bombus lapidarius Oedemera nobilis Episyrphus balteatus Bombus terrestris/lucorum Bombus pascuorum	other Bees	None Available

EnviroAtlas URL

EM-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD)	Average Annual Precipitation	None Available	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate	None Available	GAP Ecological Systems	GAP Ecological Systems	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-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	None	[Ecosystem] Provisioning Nutrition Biomass Wild plants, algae and their outputs	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection Mass flows Mass stabilisation and control of erosion rates	[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 Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Pollination and seed dispersal	[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-63	EM-337	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-892
Atmosphere Soil (2) Specific types of soil Quality Indicator	None	Near Coastal Marine/Estuarine (113) Flora (5) Seaweed Stock Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator Flow Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	None	None	None

EcoService Models Library (ESML)

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EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

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

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