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-337 |
EM-542 
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EM-698 |
EM-788
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EM-892 |
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EM Short Name
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Rate of Fire Spread | Coastal protection in Belize | Fish species richness, St. Croix, USVI | Wild bees over 26 yrs of restored prairie, IL, USA | VELMA v. 2.1 contaminant modeling |
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EM Full Name
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Rate of Fire Spread | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | Fish Species Richness, Buck Island, St. Croix , USVI | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling |
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EM Source or Collection
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None | InVEST | None | None | US EPA |
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EM Source Document ID
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306 | 350 | 355 | 401 |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
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Document Author
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Rothermel, Richard C. | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | McKane |
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Document Year
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1972 | 2016 | 2007 | 2017 | None |
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Document Title
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A Mathematical model for predicting fire spread in wildland fuels | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation |
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Document Status
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Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published USDA Forest Service report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report |
Software and Access
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EM ID
em.detail.idHelp
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EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
| http://firelab.org/project/farsite | Not identified in paper | Not applicable | Not applicable | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | |
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Contact Name
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Charles McHugh | Greg Guannel | Simon Pittman | Sean R. Griffin | Robert B. McKane |
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Contact Address
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RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | The Nature Conservancy, Coral Gables, FL. USA | 1305 East-West Highway, Silver Spring, MD 20910, USA | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 |
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Contact Email
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cmchugh@fs.fed.us | greg.guannel@gmail.com | simon.pittman@noaa.gov | srgriffin108@gmail.com | mckane.bob@epa.gov |
EM Description
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EM ID
em.detail.idHelp
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EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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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.** | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | 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: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | 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." |
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Specific Policy or Decision Context Cited
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None identified | Future rock lobster fisheries management | None provided | None identified | None identified |
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Biophysical Context
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Not applicable | barrier reef and fringing reef in nearshore coastal marine system | Hard and soft benthic habitat types approximately to the 33m isobath | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | 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-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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Method Only, Application of Method or Model Run
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Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
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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-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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Document ID for related EM
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None | None | Doc-355 | None | Doc-430 |
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EM ID for related EM
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None | None | EM-590 | EM-699 | None | EM-883 | EM-884 | EM-887 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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EM Temporal Extent
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Not applicable | 2005-2013 | 2000-2005 | 1988-2014 | Not applicable |
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EM Time Dependence
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Not applicable | time-dependent | time-stationary | time-stationary | time-dependent |
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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 | discrete | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Second | Not applicable | Not applicable | Day |
EM Spatial Extent
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EM ID
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EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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Bounding Type
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Not applicable | Geopolitical | Physiographic or ecological | Physiographic or ecological | Not applicable |
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Spatial Extent Name
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Not applicable | Coast of Belize | SW Puerto Rico, | Nachusa Grasslands | Not applicable |
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Spatial Extent Area (Magnitude)
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Not applicable | 100-1000 km^2 | 100-1000 km^2 | 10-100 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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EM Spatial Distribution
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Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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Not applicable | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | volume, for 3-D feature |
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Spatial Grain Size
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Not applicable | 1 meter | not reported | Area varies by site | user defined |
EM Structure and Computation Approach
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EM ID
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EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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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-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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Model Calibration Reported?
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Not applicable | No | No | No | Not applicable |
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Model Goodness of Fit Reported?
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Not applicable | No | Yes | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None |
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None | None |
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Model Operational Validation Reported?
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No |
No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
Yes | No | Not applicable |
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Model Uncertainty Analysis Reported?
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Not applicable | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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Not applicable | No | Yes | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No | 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-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
| None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
| None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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Centroid Latitude
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-9999 | 18.63 | 17.79 | 41.89 | Not applicable |
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Centroid Longitude
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-9999 | -88.22 | -64.62 | -89.34 | Not applicable |
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Centroid Datum
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Not applicable | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Not applicable | Estimated | Estimated | Provided | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Not applicable | coral reefs | shallow coral reefs | Restored prairie, prairie remnants, and cropland | Terrestrial environment |
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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 corresponds to 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-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
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EM Organismal Scale
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Not applicable | Guild or Assemblage | Guild or Assemblage | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
| None Available | None Available |
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None Available |
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-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
| 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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-337 |
EM-542
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EM-698 |
EM-788
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EM-892 |
| None |
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None | None |