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-788 ![]() |
EM-959 |
EM Short Name
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Rate of Fire Spread | Wild bees over 26 yrs of restored prairie, IL, USA | NC HUC-12 conservation prioritization tool |
EM Full Name
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Rate of Fire Spread | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA |
EM Source or Collection
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None | None | None |
EM Source Document ID
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306 | 401 |
443 ?Comment:Doc 444 is an additional source for this EM |
Document Author
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Rothermel, Richard C. | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Warnell, K., I. Golden, and C. Canfield |
Document Year
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1972 | 2017 | 2023 |
Document Title
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A Mathematical model for predicting fire spread in wildland fuels | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Conservation planning tools for NC's people & nature |
Document Status
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Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published USDA Forest Service report | Published journal manuscript | Webpage |
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
http://firelab.org/project/farsite | Not applicable | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | |
Contact Name
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Charles McHugh | Sean R. Griffin | Katie Warnell |
Contact Address
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RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Not reported |
Contact Email
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cmchugh@fs.fed.us | srgriffin108@gmail.com | katie.warnell@duke.edu |
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
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: "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: "Conservation organizations and land trusts in North Carolina are increasingly focused on how their work can contribute to both human and ecosystem resilience and adaptation to climate change, as well as directly mitigate climate change through carbon storage and sequestration. Recent state executive and legislative actions also underscore the importance of natural systems for climate adaptation and mitigation, and may provide additional funding for conservation and restoration for those purposes in the near term. To make it more efficient for conservation organizations working in North Carolina to consider a broad suite of conservation benefits in their work, the Conservation Trust for North Carolina and the Nicholas Institute for Energy, Environment & Sustainability at Duke University have developed two online tools for identifying priority areas for conservation action and estimating benefit metrics for specific properties. The conservation prioritization tool finds the sub-watersheds in North Carolina with the greatest potential to provide a set of user-selected conservation benefits. It allows users to identify priority areas for future conservation work within the entire state or a defined region. This high-level tool allows for quick and easy exploration without the need for spatial analysis expertise." |
Specific Policy or Decision Context Cited
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None identified | None identified | Allows users to prioritize HUCs within their area of interest based on their conservation goals. |
Biophysical Context
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Not applicable | 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 |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented |
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
Method Only, Application of Method or Model Run
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Method Only | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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New or revised model | 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-788 ![]() |
EM-959 |
Document ID for related EM
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None | None |
Doc-444 ?Comment:The secondary source, document 444, is the website for running the tool. |
EM ID for related EM
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None | None | None |
EM Modeling Approach
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
EM Temporal Extent
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Not applicable | 1988-2014 | Not applicable |
EM Time Dependence
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Not applicable | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
Bounding Type
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Not applicable | Physiographic or ecological | Not applicable |
Spatial Extent Name
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Not applicable | Nachusa Grasslands | Not applicable |
Spatial Extent Area (Magnitude)
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Not applicable | 10-100 km^2 | Not applicable |
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
EM Spatial Distribution
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Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature |
Spatial Grain Size
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Not applicable | Area varies by site | HUC 12 |
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
EM Computational Approach
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Analytic | Analytic | Other or unclear (comment) |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
Model Calibration Reported?
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Not applicable | No | Not applicable |
Model Goodness of Fit Reported?
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Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | Not applicable |
Model Uncertainty Analysis Reported?
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Not applicable | No | Not applicable |
Model Sensitivity Analysis Reported?
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Not applicable | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-337 |
EM-788 ![]() |
EM-959 |
None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-337 |
EM-788 ![]() |
EM-959 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
Centroid Latitude
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-9999 | 41.89 | Not applicable |
Centroid Longitude
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-9999 | -89.34 | Not applicable |
Centroid Datum
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Not applicable | WGS84 | Not applicable |
Centroid Coordinates Status
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Not applicable | Provided | Not applicable |
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Not applicable | Restored prairie, prairie remnants, and cropland | Terrestrial and freshwater aquatic |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-337 |
EM-788 ![]() |
EM-959 |
EM Organismal Scale
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Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-337 |
EM-788 ![]() |
EM-959 |
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-337 |
EM-788 ![]() |
EM-959 |
None |
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None |
<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-788 ![]() |
EM-959 |
None | None | None |