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-63 | EM-337 | EM-698 | EM-892 |
EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Rate of Fire Spread | Fish species richness, St. Croix, USVI | VELMA v. 2.1 contaminant modeling |
EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Rate of Fire Spread | Fish Species Richness, Buck Island, St. Croix , USVI | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling |
EM Source or Collection
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US EPA | EnviroAtlas | None | None | US EPA |
EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
306 | 355 |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Rothermel, Richard C. | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | McKane |
Document Year
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2013 | 1972 | 2007 | None |
Document Title
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EnviroAtlas - National | A Mathematical model for predicting fire spread in wildland fuels | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation |
Document Status
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Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published USDA Forest Service report | Published journal manuscript | Published EPA report |
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
https://www.epa.gov/enviroatlas | http://firelab.org/project/farsite | Not applicable | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | |
Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Charles McHugh | Simon Pittman | Robert B. McKane |
Contact Address
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Not reported | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | 1305 East-West Highway, Silver Spring, MD 20910, USA | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 |
Contact Email
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enviroatlas@epa.gov | cmchugh@fs.fed.us | simon.pittman@noaa.gov | mckane.bob@epa.gov |
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | 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: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters." |
Specific Policy or Decision Context Cited
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None Identified | None identified | None provided | None identified |
Biophysical Context
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No additional description provided | Not applicable | Hard and soft benthic habitat types approximately to the 33m isobath | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
Method Only, Application of Method or Model Run
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Method + Application | Method Only | Method + Application | Method Only |
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
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
Document ID for related EM
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
None | Doc-355 | Doc-430 |
EM ID for related EM
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None | None | EM-590 | EM-699 | EM-883 | EM-884 | EM-887 |
EM Modeling Approach
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
EM Temporal Extent
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2006-2010 | Not applicable | 2000-2005 | Not applicable |
EM Time Dependence
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time-stationary | Not applicable | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Day |
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
Bounding Type
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Geopolitical | Not applicable | Physiographic or ecological | Not applicable |
Spatial Extent Name
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counterminous United States | Not applicable | SW Puerto Rico, | Not applicable |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | Not applicable | 100-1000 km^2 | Not applicable |
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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 | volume, for 3-D feature |
Spatial Grain Size
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irregular | Not applicable | not reported | user defined |
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
Model Calibration Reported?
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No | Not applicable | No | Not applicable |
Model Goodness of Fit Reported?
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No | Not applicable | Yes | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
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None |
Model Operational Validation Reported?
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No | No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | Not applicable | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | Not applicable | Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-63 | EM-337 | EM-698 | EM-892 |
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None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-63 | EM-337 | EM-698 | EM-892 |
None | None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
Centroid Latitude
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39.5 | -9999 | 17.79 | Not applicable |
Centroid Longitude
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-98.35 | -9999 | -64.62 | Not applicable |
Centroid Datum
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WGS84 | Not applicable | WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Not applicable | Estimated | Not applicable |
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
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 | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Terrestrial | Not applicable | shallow coral reefs | Terrestrial environment |
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 of differentiation of organisms modeled
EM ID
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EM-63 | EM-337 | EM-698 | EM-892 |
EM Organismal Scale
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Not applicable | Not applicable | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-63 | EM-337 | EM-698 | EM-892 |
None Available | None Available |
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None Available |
EnviroAtlas URL
EM-63 | EM-337 | EM-698 | EM-892 |
Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD) | Average Annual Precipitation | None Available | None Available |
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-63 | EM-337 | EM-698 | EM-892 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-63 | EM-337 | EM-698 | EM-892 |
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None |
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None |