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-699 | EM-706 |
EM-863 ![]() |
EM Short Name
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Fish species richness, St. John, USVI, USA | WESP Method | SLAMM, Tampa Bay, FL, USA |
EM Full Name
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Fish species richness, St. John, USVI, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA |
EM Source or Collection
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None | None | None |
EM Source Document ID
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355 | 390 |
415 ?Comment:Secondary sources: Documents 412 and 413. |
Document Author
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Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Adamus, P. R. | Sherwood, E. T. and H. S. Greening |
Document Year
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2007 | 2016 | 2014 |
Document Title
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Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published report | Published journal manuscript |
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | |
Contact Name
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Simon Pittman | Paul R. Adamus | Edward T. Sherwood |
Contact Address
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1305 East-West Highway, Silver Spring, MD 20910, USA | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA |
Contact Email
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simon.pittman@noaa.gov | adamus7@comcast.net | esherwood@tbep.org |
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
Summary Description
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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." | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" |
Specific Policy or Decision Context Cited
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None provided | None identified | None identified |
Biophysical Context
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Hard and soft benthic habitat types approximately to the 33m isobath | None | No additional description provided |
EM Scenario Drivers
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No scenarios presented | N/A | Varying sea level rise (baseline - 2m), and two habitat adaption strategies |
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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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
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EM-699 | EM-706 |
EM-863 ![]() |
Document ID for related EM
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Doc-355 | None | Doc-412 | Doc-413 |
EM ID for related EM
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EM-590 | EM-698 | EM-718 | EM-857 |
EM Modeling Approach
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
EM Temporal Extent
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2000-2005 | Not applicable | 2002-2100 |
EM Time Dependence
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time-stationary | 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-699 | EM-706 |
EM-863 ![]() |
Bounding Type
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Physiographic or ecological | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
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SW Puerto Rico, | Not applicable | Tampa Bay estuary watershed |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | Not applicable | 1000-10,000 km^2. |
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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not reported | not reported | 10 x 10 m |
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
EM Computational Approach
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Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
Model Calibration Reported?
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No | Not applicable | No |
Model Goodness of Fit Reported?
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Yes | Not applicable | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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Yes | No | No |
Model Uncertainty Analysis Reported?
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No | Not applicable | No |
Model Sensitivity Analysis Reported?
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Yes | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
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No | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-699 | EM-706 |
EM-863 ![]() |
None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-699 | EM-706 |
EM-863 ![]() |
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None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
Centroid Latitude
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17.79 | Not applicable | 27.76 |
Centroid Longitude
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-64.62 | Not applicable | -82.54 |
Centroid Datum
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WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Estimated | Not applicable | Estimated |
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Inland Wetlands | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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shallow coral reefs | Wetlands | Esturary and associated urban and terrestrial environment |
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 finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-699 | EM-706 |
EM-863 ![]() |
EM Organismal Scale
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Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-699 | EM-706 |
EM-863 ![]() |
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None Available | None Available |
EnviroAtlas URL
EM-699 | EM-706 |
EM-863 ![]() |
None Available | None Available | National Hydrography Dataset Plus (NHD PlusV2) |
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-699 | EM-706 |
EM-863 ![]() |
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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-699 | EM-706 |
EM-863 ![]() |
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None |