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-70 | EM-337 |
EM-359 
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EM-701 |
EM-774
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EM Short Name
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Plant species diversity, Central French Alps | Rate of Fire Spread | InVEST (v1.004) sediment retention, Indonesia | Blue-winged Teal recruits, CREP wetlands, IA, USA | Plant-pollinator networks at reclaimed mine, USA |
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EM Full Name
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Plant species diversity, Central French Alps | Rate of Fire Spread | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | Blue-winged Teal duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | None | InVEST | None | None |
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EM Source Document ID
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260 | 306 | 309 |
372 ?Comment:Document 373 is a secondary source for this EM. |
397 |
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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. | Rothermel, Richard C. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | 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 | Cusser, S. and K. Goodell |
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Document Year
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2011 | 1972 | 2014 | 2010 | 2013 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | A Mathematical model for predicting fire spread in wildland fuels | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine |
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Document Status
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Peer reviewed and published | Documented, not peer reviewed | 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 USDA Forest Service report | Published journal manuscript | Published report | Published journal manuscript |
Software and Access
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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| Not applicable | http://firelab.org/project/farsite | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | Charles McHugh | Nirmal K. Bhagabati | David Otis |
Sarah Cusser ?Comment:Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A. |
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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 | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A. |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | cmchugh@fs.fed.us | nirmal.bhagabati@wwfus.org | dotis@iastate.edu | sarah.cusser@gmail.com |
EM Description
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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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: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | 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.** | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | ABSTRACT: "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…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | ABSTRACT: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context." |
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Specific Policy or Decision Context Cited
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None identified | None identified | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Not applicable | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Prairie Pothole Region of Iowa | The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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Method Only, Application of Method or Model Run
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Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | 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 | Application of existing 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-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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Document ID for related EM
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Doc-260 | None | Doc-338 | Doc-372 | Doc-373 | None |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | EM-435 | EM-705 | EM-704 | EM-703 | EM-702 | EM-700 | EM-632 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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EM Temporal Extent
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2007-2009 | Not applicable | 2008-2020 | 1987-2007 | 2009-2010 |
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EM Time Dependence
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time-stationary | Not applicable | 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 |
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EM Time Continuity
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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 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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Bounding Type
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Physiographic or Ecological | Not applicable | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological |
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Spatial Extent Name
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Central French Alps | Not applicable | central Sumatra | CREP (Conservation Reserve Enhancement Program | The Wilds |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | Not applicable | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 1-10 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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EM Spatial Distribution
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spatially distributed (in at least some cases) | Not applicable | 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 | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
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20 m x 20 m | Not applicable | 30 m x 30 m | multiple, individual, irregular sites | 10 m radius |
EM Structure and Computation Approach
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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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-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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Model Calibration Reported?
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No | Not applicable | No | Unclear | Not applicable |
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Model Goodness of Fit Reported?
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Yes | Not applicable | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | No | Yes |
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Model Uncertainty Analysis Reported?
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No | Not applicable | No | No | Yes |
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Model Sensitivity Analysis Reported?
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No | Not applicable | No | No | No |
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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-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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Centroid Latitude
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45.05 | -9999 | 0 | 42.62 | 39.82 |
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Centroid Longitude
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6.4 | -9999 | 102 | -93.84 | -81.75 |
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Centroid Datum
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WGS84 | Not applicable | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Not applicable | Provided | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Agroecosystems | Grasslands | Grasslands |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Not applicable | 104 land use land cover classes | Wetlands buffered by grassland within agroecosystems | Grassland |
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EM Ecological Scale
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Not applicable | 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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EM Organismal Scale
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Community | Not applicable | Community | Individual or population, within a species | Species |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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| None Available | None Available | None Available |
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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)
em.detail.cicesHelp
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| EM-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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| 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-70 | EM-337 |
EM-359
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EM-701 |
EM-774
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| None | None | None |
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None |