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-83 | EM-93 |
EM-102 
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EM-103 | EM-457 | EM-890 |
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EM Short Name
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Soil carbon and plant traits, Central French Alps | Stream nitrogen removal, Mississippi R. basin, USA | Fish species habitat value, Tampa Bay, FL, USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | Visitation to reef dive sites, St. Croix, USVI | HWB Blood pressure, Great Lakes waterfront, USA |
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EM Full Name
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Soil carbon potential estimated from plant functional traits, Central French Alps | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Fish species habitat value, Tampa Bay, FL, USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | Visitation to dive sites (reef), St. Croix, USVI | Human well being indicator- Blood pressure, Great Lakes waterfront, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | US EPA | US EPA | None |
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EM Source Document ID
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260 | 52 | 187 | 275 | 335 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
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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. | Hill, B. and Bolgrien, D. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
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Document Year
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2011 | 2011 | 2016 | 2014 | 2014 | None |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
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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 | Peer reviewed but unpublished (explain in Comment) |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review |
Software and Access
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | Brian Hill | Richard Fulford |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Susan H. Yee | Ted Angradi |
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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 | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | hill.brian@epa.gov | Fulford.Richard@epa.gov | frazier@nceas.ucsb.edu | yee.susan@epa.gov | tedangradi@gmail.com |
EM Description
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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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 soil carbon 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 the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | 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." |
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Specific Policy or Decision Context Cited
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None identified | Not applicable | None identifed | None identified | None identified | None identified |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Agricultural landuse , 1st-10th order streams | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | No additional description provided | Waterfront districts on south Lake Michigan and south lake Erie |
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EM Scenario Drivers
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No scenarios presented | Not applicable | No scenarios presented | No scenarios presented | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application |
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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 | Application of existing 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-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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Document ID for related EM
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Doc-260 | Doc-154 | Doc-155 | None | None | None | Doc-422 |
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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-81 | EM-82 | None | None | None | None | EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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EM Temporal Extent
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Not reported | 2000-2008 | 2006-2011 | December 2007 - November 2008 | 2006-2007, 2010 | 2022 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | 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 | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
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Central French Alps | Upper Mississippi, Ohio and Missouri River sub-basins | Tampa Bay | Yaquina Estuary (intertidal), Oregon, USA | Coastal zone surrounding St. Croix | Great Lakes waterfront |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 1-10 km^2 | 100-1000 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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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) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (habitat type) | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 1 km | 1 km^2 | 0.87-104.29 ha | 10 m x 10 m | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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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-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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Model Calibration Reported?
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No | No | No | Unclear | Yes | No |
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Model Goodness of Fit Reported?
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No | No | No | No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | No | Yes | No |
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Model Uncertainty Analysis Reported?
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No | Yes | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | Unclear | No | No | No | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | 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-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
| None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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Centroid Latitude
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45.05 | 36.98 | 27.74 | 44.62 | 17.73 | 42.26 |
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Centroid Longitude
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6.4 | -89.13 | -82.57 | -124.06 | -64.77 | -87.84 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Provided | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Not applicable | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Estuarine intertidal | Coral reefs | Lake Michigan & Lake Erie waterfront |
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EM Ecological Scale
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Zone within an ecosystem | 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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EM Organismal Scale
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Community | Not applicable | Species | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
| None Available | None Available |
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None Available | None Available |
EnviroAtlas URL
| EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
| None Available | National Hydrography Dataset Plus (NHD PlusV2), Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition | Big game hunting recreation demand | None Available | None Available | 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.detail.cicesHelp
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| EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-83 | EM-93 |
EM-102
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EM-103 | EM-457 | EM-890 |
| None |
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None |