EcoService Models Library (ESML)
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Compare EMs
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.
EM Identity and Description
EM ID
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EM-337 | EM-604 |
EM Short Name
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Rate of Fire Spread | Chinook salmon value (household), Yaquina Bay, OR |
EM Full Name
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Rate of Fire Spread | Economic value of Chinook salmon per household method, Yaquina Bay, OR |
EM Source or Collection
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None | US EPA |
EM Source Document ID
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306 | 324 |
Document Author
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Rothermel, Richard C. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar |
Document Year
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1972 | 2012 |
Document Title
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A Mathematical model for predicting fire spread in wildland fuels | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management |
Document Status
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Documented, not peer reviewed | Peer reviewed and published |
Comments on Status
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Published USDA Forest Service report | Published journal manuscript |
EM ID
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EM-337 | EM-604 |
http://firelab.org/project/farsite | Not applicable | |
Contact Name
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Charles McHugh | Stephen Jordan |
Contact Address
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RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA |
Contact Email
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cmchugh@fs.fed.us | jordan.steve@epa.gov |
EM ID
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EM-337 | EM-604 |
Summary Description
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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:"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. |
Specific Policy or Decision Context Cited
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None identified | None identified |
Biophysical Context
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Not applicable | Yaquina Bay estuary |
EM Scenario Drivers
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No scenarios presented | No scenarios presented |
EM ID
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EM-337 | EM-604 |
Method Only, Application of Method or Model Run
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Method Only | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-337 | EM-604 |
Document ID for related EM
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None | Doc-324 |
EM ID for related EM
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None | EM-603 | EM-397 |
EM Modeling Approach
EM ID
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EM-337 | EM-604 |
EM Temporal Extent
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Not applicable | 2003-2008 |
EM Time Dependence
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Not applicable | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable |
EM ID
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EM-337 | EM-604 |
Bounding Type
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Not applicable | Geopolitical |
Spatial Extent Name
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Not applicable | Pacific Northwest |
Spatial Extent Area (Magnitude)
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Not applicable | >1,000,000 km^2 |
EM ID
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EM-337 | EM-604 |
EM Spatial Distribution
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Not applicable | spatially lumped (in all cases) |
Spatial Grain Type
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Not applicable | Not applicable |
Spatial Grain Size
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Not applicable | Not applicable |
EM ID
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EM-337 | EM-604 |
EM Computational Approach
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Analytic | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-337 | EM-604 |
Model Calibration Reported?
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Not applicable | No |
Model Goodness of Fit Reported?
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Not applicable | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Yes |
Model Uncertainty Analysis Reported?
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Not applicable | No |
Model Sensitivity Analysis Reported?
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Not applicable | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-337 | EM-604 |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-337 | EM-604 |
None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-337 | EM-604 |
Centroid Latitude
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-9999 | 44.62 |
Centroid Longitude
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-9999 | -124.02 |
Centroid Datum
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Not applicable | WGS84 |
Centroid Coordinates Status
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Not applicable | Estimated |
EM ID
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EM-337 | EM-604 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Not applicable | Yaquina Bay estuary and ocean |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-337 | EM-604 |
EM Organismal Scale
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Not applicable | Other (multiple scales) |
Taxonomic level and name of organisms or groups identified
EM-337 | EM-604 |
None Available |
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EnviroAtlas URL
EM-337 | EM-604 |
Average Annual Precipitation | 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)
EM-337 | EM-604 |
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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-337 | EM-604 |
None |
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