EcoService Models Library (ESML)
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Which comparison is best for me?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:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719 
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EM Short Name
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Cultural ES and plant traits, Central French Alps | RHyME2, Upper Mississippi River basin, USA | EnviroAtlas - Restorable wetlands | WTP for a beach day, Massachusetts, USA | Seed mix for native plant establishment, IA, USA |
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EM Full Name
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Cultural ecosystem service estimated from plant functional traits, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA | Cost-effective seed mix design for native plant establishment, Iowa, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | EnviroAtlas | US EPA | None |
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EM Source Document ID
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260 | 123 | 262 | 386 | 394 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | US EPA Office of Research and Development - National Exposure Research Laboratory | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Meissen, J. |
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Document Year
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2011 | 2013 | 2013 | 2018 | 2018 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | EnviroAtlas - National | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Cost-effective seed mix design and first-year management |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published report |
Software and Access
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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| Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | Liem Tran | EnviroAtlas Team | Kate K, Mulvaney | Justin Meissen |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | Not reported | Not reported | Tallgrass Prairie Center, University of Northern Iowa |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | enviroatlas@epa.gov | Mulvaney.Kate@EPA.gov | Not reported |
EM Description
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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Summary Description
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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: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "We used existing studies in a meta-analysis to estimate appropriate benefit transfer values of consumer surplus per beach visit for Barnstable. The studies we include in the model are for beaches across the United States, allowing the metaregression model to be more broadly applicable to other beaches and for values to be adjusted based on appropriate site attributes...To identify relevant studies, we selected 25 studies of beach use and swimming from the Recreation Use Values Database (RUVD), where consumer surplus values are presented as value per day in 2016 dollars...We added beach length and history of closures to contextualize the model for our application by proxying water quality and site quality." Equation 1, page 11, provides the meta-regression. | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." |
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Specific Policy or Decision Context Cited
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None identified | Not reported | None Identified | Economic value of protecting coastal beach water quality from contamination caused closures. | Seed mix design and management practices for native plant restoration |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | No additional description provided | Four separate beaches within the community of Barnstable | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | 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
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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Document ID for related EM
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None | Doc-123 | None | Doc-386 | Doc-387 | Doc-395 |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-82 | EM-83 | None | None | EM-684 | EM-685 | EM-683 | EM-686 | EM-728 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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EM Temporal Extent
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Not reported | 1987-1997 | 2006-2013 | July 1, 2011 to June 31, 2016 | 2015-2017 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Year |
EM Spatial Extent
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Other |
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Spatial Extent Name
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Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | conterminous United States | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Iowa State University Northeast Research and Demonstration Farm |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 10-100 ha | <1 ha |
Spatial Distribution of Computations
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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EM Spatial Distribution
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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) |
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Spatial Grain Type
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area, for pixel or radial feature | NHDplus v1 | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature |
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Spatial Grain Size
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20 m x 20 m | NHDplus v1 | irregular | by beach site | 20 ft x 28 ft |
EM Structure and Computation Approach
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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EM Computational Approach
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Analytic | Numeric | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | stochastic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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Model Calibration Reported?
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No | Yes | No | Yes | Not applicable |
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Model Goodness of Fit Reported?
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No | Yes | No | Yes | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None |
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None |
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Model Operational Validation Reported?
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No | No | No | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
No |
Yes ?Comment:p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables. |
Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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| None | None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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Centroid Latitude
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45.05 | 42.5 | 39.5 | 41.64 | 42.93 |
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Centroid Longitude
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6.4 | -90.63 | -98.35 | -70.29 | -92.57 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Agroecosystems | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | None | Terrestrial | Saltwater beach | Research farm in historic grassland |
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EM Ecological Scale
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Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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EM Organismal Scale
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Community | Not applicable | Not applicable | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
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| EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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| None Available | None Available | None Available | None Available | None Available |
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)
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| EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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None |
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<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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-81 | EM-91 | EM-492 | EM-682 |
EM-719
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| None | None | None |
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