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-86 | EM-337 | EM-459 | EM-604 |
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EM Short Name
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Area and hotspots of soil retention, South Africa | Rate of Fire Spread | Reef density of S. gigas, St. Croix, USVI | Chinook salmon value (household), Yaquina Bay, OR |
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EM Full Name
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Area and hotspots of soil retention, South Africa | Rate of Fire Spread | Relative density of Strombus gigas (on reef), St. Croix, USVI | Economic value of Chinook salmon per household method, Yaquina Bay, OR |
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EM Source or Collection
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None | None | US EPA | US EPA |
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EM Source Document ID
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271 | 306 | 335 | 324 |
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Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Rothermel, Richard C. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar |
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Document Year
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2008 | 1972 | 2014 | 2012 |
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Document Title
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Mapping ecosystem services for planning and management | A Mathematical model for predicting fire spread in wildland fuels | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management |
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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 |
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Comments on Status
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Published journal manuscript | Published USDA Forest Service report | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-86 | EM-337 | EM-459 | EM-604 |
| Not applicable | http://firelab.org/project/farsite | Not applicable | Not applicable | |
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Contact Name
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Benis Egoh | Charles McHugh | Susan H. Yee | Stephen Jordan |
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Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA |
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Contact Email
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Not reported | cmchugh@fs.fed.us | yee.susan@epa.gov | jordan.steve@epa.gov |
EM Description
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EM ID
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EM-86 | EM-337 | EM-459 | EM-604 |
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Summary Description
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AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(2) density of the queen conch Strombus gigas" | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. |
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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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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Not applicable | No additional description provided | Yaquina Bay estuary |
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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-86 | EM-337 | EM-459 | EM-604 |
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Method Only, Application of Method or Model Run
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Method + Application | Method Only | 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 | 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-86 | EM-337 | EM-459 | EM-604 |
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Document ID for related EM
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Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
None | None | Doc-324 |
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EM ID for related EM
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EM-85 | EM-87 | EM-88 | None | None | EM-603 | EM-397 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-86 | EM-337 | EM-459 | EM-604 |
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EM Temporal Extent
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Not reported | Not applicable | 2006-2007, 2010 | 2003-2008 |
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EM Time Dependence
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time-stationary | Not applicable | 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-86 | EM-337 | EM-459 | EM-604 |
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Bounding Type
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Geopolitical | Not applicable | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
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South Africa | Not applicable | Coastal zone surrounding St. Croix | Pacific Northwest |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | Not applicable | 100-1000 km^2 | >1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-86 | EM-337 | EM-459 | EM-604 |
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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 lumped (in all cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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Distributed across catchments with average size of 65,000 ha | Not applicable | 10 m x 10 m | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-86 | EM-337 | EM-459 | EM-604 |
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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-86 | EM-337 | EM-459 | EM-604 |
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Model Calibration Reported?
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No | Not applicable | Yes | No |
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Model Goodness of Fit Reported?
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No | Not applicable | No | No |
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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 | Yes | Yes |
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Model Uncertainty Analysis Reported?
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No | Not applicable | No | No |
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Model Sensitivity Analysis Reported?
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No | Not applicable | 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-86 | EM-337 | EM-459 | EM-604 |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-86 | EM-337 | EM-459 | EM-604 |
| None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-86 | EM-337 | EM-459 | EM-604 |
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Centroid Latitude
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-30 | -9999 | 17.73 | 44.62 |
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Centroid Longitude
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25 | -9999 | -64.77 | -124.02 |
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Centroid Datum
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WGS84 | Not applicable | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Not applicable | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-86 | EM-337 | EM-459 | EM-604 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | 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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Not reported | Not applicable | Coral reefs | Yaquina Bay estuary and ocean |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-86 | EM-337 | EM-459 | EM-604 |
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EM Organismal Scale
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Not applicable | Not applicable | Species | Other (multiple scales) |
Taxonomic level and name of organisms or groups identified
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| EM-86 | EM-337 | EM-459 | EM-604 |
| None Available | None Available |
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EnviroAtlas URL
| EM-86 | EM-337 | EM-459 | EM-604 |
| None Available | Average Annual Precipitation | None Available | Dasymetric Allocation of Population |
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-86 | EM-337 | EM-459 | EM-604 |
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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-86 | EM-337 | EM-459 | EM-604 |
| None | None |
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