EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
EM Short Name
em.detail.shortNameHelp
?
|
FORCLIM v2.9, Western OR, USA | Wetland shellfish production, Gulf of Mexico, USA | Fish species richness, St. John, USVI, USA |
EM Full Name
em.detail.fullNameHelp
?
|
FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | Wetland shellfish production, Gulf of Mexico, USA | Fish species richness, St. John, USVI, USA |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
None |
EM Source Document ID
|
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
324 | 355 |
Document Author
em.detail.documentAuthorHelp
?
|
Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco |
Document Year
em.detail.documentYearHelp
?
|
2007 | 2012 | 2007 |
Document Title
em.detail.sourceIdHelp
?
|
Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean |
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
Not applicable | Not applicable | Not applicable | |
Contact Name
em.detail.contactNameHelp
?
|
Richard T. Busing | Stephen J. Jordan | Simon Pittman |
Contact Address
|
U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | 1305 East-West Highway, Silver Spring, MD 20910, USA |
Contact Email
|
rtbusing@aol.com | jordan.steve@epa.gov | simon.pittman@noaa.gov |
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e). | ABSTRACT: "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 … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | 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." |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None Identified | None identified | None provided |
Biophysical Context
|
Coastal to montane, Pacific Northwest US (Oregon) forests. | Estuarine environments and marsh-land interfaces | Hard and soft benthic habitat types approximately to the 33m isobath |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
Two scenarios modelled, forests with and without fire | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-22 | Doc-23 ?Comment:Related document ID 22 provides tree species specific parameters in appendix. |
None | Doc-355 |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-146 | EM-208 | EM-224 | EM-604 | EM-603 | EM-590 | EM-698 |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
>650 yrs | 1950 - 2050 | 2000-2005 |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
past time | future time | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | discrete | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
1 | Varies by Run | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Year | Year | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or ecological | Physiographic or ecological | Physiographic or ecological |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Western Oregon, north of 43.00 N to Washington border | Gulf of Mexico (estuarine and coastal) | SW Puerto Rico, |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10,000-100,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 |
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
0.08 ha | 55.2 km^2 | not reported |
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Yes | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | Yes |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Yes | No | Yes |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | Yes |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | No |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
None |
|
|
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
Centroid Latitude
em.detail.ddLatHelp
?
|
44.66 | 30.44 | 17.79 |
Centroid Longitude
em.detail.ddLongHelp
?
|
-122.56 | -87.99 | -64.62 |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Estimated |
EM ID
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Forests | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Primarily conifer forest | Submerged aquatic vegetation in estuaries and coastal lagoons | shallow coral reefs |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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
em.detail.idHelp
?
|
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Species | Species | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
|
|
|
EnviroAtlas URL
EM-186 ![]() |
EM-397 ![]() |
EM-699 |
GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions | Big game hunting recreation demand | 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-186 ![]() |
EM-397 ![]() |
EM-699 |
|
|
|
<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-186 ![]() |
EM-397 ![]() |
EM-699 |
None |
|
|