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-106 | EM-450 | EM-845 |
EM Short Name
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Value of Habitat for Shrimp, Campeche, Mexico | Decrease in wave runup, St. Croix, USVI | Red-winged blackbird abun, Piedmont region, USA |
EM Full Name
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Value of Habitat for Shrimp, Campeche, Mexico | Decrease in wave runup (by reef), St. Croix, USVI | Red-winged blackbird abundance, Piedmont ecoregion, USA |
EM Source or Collection
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None | US EPA | None |
EM Source Document ID
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227 | 335 | 405 |
Document Author
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Barbier, E. B., and Strand, I. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Riffel, S., Scognamillo, D., and L. W. Burger |
Document Year
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1998 | 2014 | 2008 |
Document Title
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Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds |
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 journal manuscript | Published journal manuscript |
EM ID
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EM-106 | EM-450 | EM-845 |
Not applicable | Not applicable | Not applicable | |
Contact Name
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E.B. Barbier | Susan H. Yee | Sam Riffell |
Contact Address
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Environment Department, University of York, York YO1 5DD, UK | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA |
Contact Email
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Not reported | yee.susan@epa.gov | sriffell@cfr.msstate.edu |
EM ID
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EM-106 | EM-450 | EM-845 |
Summary Description
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AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153) | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." |
Specific Policy or Decision Context Cited
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None identified | None identified | None reported |
Biophysical Context
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Gulf of Mexico; mangrove-lagoon system | No additional description provided | Conservation Reserve Program lands left to go fallow |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A |
EM ID
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EM-106 | EM-450 | EM-845 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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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-106 | EM-450 | EM-845 |
Document ID for related EM
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None | Doc-335 | Doc-405 |
EM ID for related EM
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EM-185 | EM-319 | EM-447 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-846 | EM-847 |
EM Modeling Approach
EM ID
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EM-106 | EM-450 | EM-845 |
EM Temporal Extent
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1980-1990 | 2006-2007, 2010 | 2008 |
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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Year | Not applicable | Not applicable |
EM ID
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EM-106 | EM-450 | EM-845 |
Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Physiographic or ecological |
Spatial Extent Name
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Laguna de Terminos Mangrove system | Coastal zone surrounding St. Croix | Piedmont Ecoregion |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 |
EM ID
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EM-106 | EM-450 | EM-845 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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1 km x 1 km | 10 m x 10 m | Not applicable |
EM ID
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EM-106 | EM-450 | EM-845 |
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-106 | EM-450 | EM-845 |
Model Calibration Reported?
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Yes | Yes | Yes |
Model Goodness of Fit Reported?
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Yes | No | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Yes | No |
Model Uncertainty Analysis Reported?
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Yes | No | No |
Model Sensitivity Analysis Reported?
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Yes | No | Yes |
Model Sensitivity Analysis Include Interactions?
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Unclear | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-106 | EM-450 | EM-845 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-106 | EM-450 | EM-845 |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-106 | EM-450 | EM-845 |
Centroid Latitude
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18.61 | 17.73 | 36.23 |
Centroid Longitude
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-91.55 | -64.77 | -81.9 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated |
EM ID
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EM-106 | EM-450 | EM-845 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Grasslands |
Specific Environment Type
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Mangrove | Coral reefs | grasslands |
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 corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-106 | EM-450 | EM-845 |
EM Organismal Scale
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Guild or Assemblage | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-106 | EM-450 | EM-845 |
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None Available |
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EnviroAtlas URL
EM-106 | EM-450 | EM-845 |
GAP Ecological Systems, Big game hunting recreation demand | National Hydrography Dataset Plus (NHD PlusV2) | GAP Ecological Systems, U.S. EPA (Omernik) ecoregions |
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-106 | EM-450 | EM-845 |
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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-106 | EM-450 | EM-845 |
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