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-65 | EM-71 | EM-453 | EM-650 |
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EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | Reef density of E. striatus, St. Croix, USVI | Sedge Wren density, CREP, Iowa, USA |
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EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Relative density of Epinephelus striatus (on reef), St. Croix, USVI | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | None |
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EM Source Document ID
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260 | 260 | 335 | 372 |
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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. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever |
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Document Year
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2011 | 2011 | 2014 | 2010 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt |
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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 |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report |
Software and Access
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EM ID
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EM-65 | EM-71 | EM-453 | EM-650 |
| Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | Susan H. Yee | David Otis |
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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 | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | yee.susan@epa.gov | dotis@iastate.edu |
EM Description
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EM ID
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EM-65 | EM-71 | EM-453 | EM-650 |
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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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | 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)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity 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: density of E. striatus" | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | Prairie pothole region of north-central Iowa |
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EM Scenario Drivers
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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-65 | EM-71 | EM-453 | EM-650 |
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Method Only, Application of Method or Model Run
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Method + Application | 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 | Application of existing model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
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-65 | EM-71 | EM-453 | EM-650 |
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Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | None | Doc-372 |
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EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | EM-652 | EM-651 | EM-649 | EM-648 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-65 | EM-71 | EM-453 | EM-650 |
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EM Temporal Extent
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2007-2009 | 2007-2008 | 2006-2007, 2010 | 1992-2007 |
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EM Time Dependence
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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 |
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EM Time Continuity
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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 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-65 | EM-71 | EM-453 | EM-650 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) |
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Spatial Extent Name
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Central French Alps | Central French Alps | Coastal zone surrounding St. Croix | CREP (Conservation Reserve Enhancement Program) wetland sites |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 1-10 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-65 | EM-71 | EM-453 | EM-650 |
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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) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 10 m x 10 m | multiple, individual, irregular shaped sites |
EM Structure and Computation Approach
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EM ID
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EM-65 | EM-71 | EM-453 | EM-650 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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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-65 | EM-71 | EM-453 | EM-650 |
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Model Calibration Reported?
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No | No | Yes | Unclear |
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Model Goodness of Fit Reported?
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Yes | Yes | No | No |
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Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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Yes | No | Yes | Unclear |
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Model Uncertainty Analysis Reported?
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No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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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-65 | EM-71 | EM-453 | EM-650 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-65 | EM-71 | EM-453 | EM-650 |
| None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-65 | EM-71 | EM-453 | EM-650 |
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Centroid Latitude
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45.05 | 45.05 | 17.73 | 42.62 |
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Centroid Longitude
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6.4 | 6.4 | -64.77 | -93.84 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Provided | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-65 | EM-71 | EM-453 | EM-650 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Coral reefs | Grassland buffering inland wetlands set in agricultural land |
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EM Ecological Scale
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Not applicable | Not applicable | 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-65 | EM-71 | EM-453 | EM-650 |
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EM Organismal Scale
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Community | Community | Guild or Assemblage | Species |
Taxonomic level and name of organisms or groups identified
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| EM-65 | EM-71 | EM-453 | EM-650 |
| None Available | None Available |
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EnviroAtlas URL
| EM-65 | EM-71 | EM-453 | EM-650 |
| GAP Ecological Systems | None Available | None Available | GAP Ecological Systems |
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-65 | EM-71 | EM-453 | EM-650 |
| None | 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-65 | EM-71 | EM-453 | EM-650 |
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None |
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