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-195 | EM-939 | EM-959 |
EM Short Name
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C Sequestration and De-N, Tampa Bay, FL, USA | ESTIMAP- Recreation, Europe | NC HUC-12 conservation prioritization tool |
EM Full Name
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Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | ESTIMAP- Recreation, Europe | NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA |
EM Source or Collection
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US EPA | None | None |
EM Source Document ID
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186 | 432 |
443 ?Comment:Doc 444 is an additional source for this EM |
Document Author
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Russell, M. and Greening, H. | Zulian, G., Parrachini, M.L., Maes, J., | Warnell, K., I. Golden, and C. Canfield |
Document Year
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2013 | 2013 | 2023 |
Document Title
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Estimating benefits in a recovering estuary: Tampa Bay, Florida | ESTIMAP: Ecosystem services mapping at the European scale | Conservation planning tools for NC's people & nature |
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 | Webpage |
EM ID
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EM-195 | EM-939 | EM-959 |
Not applicable | N.A. | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | |
Contact Name
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M. Russell | Grazia Zulian | Katie Warnell |
Contact Address
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US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy | Not reported |
Contact Email
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Russell.Marc@epamail.epa.gov | grazia.zulian@jrc.ec.europa.e | katie.warnell@duke.edu |
EM ID
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EM-195 | EM-939 | EM-959 |
Summary Description
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AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents " | 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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Restoration of seagrass | None | Allows users to prioritize HUCs within their area of interest based on their conservation goals. |
Biophysical Context
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Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | Continential Scale | No additional description provided |
EM Scenario Drivers
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Habitat loss or restoration in Tampa Bay Estuary | N.A. | No scenarios presented |
EM ID
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EM-195 | EM-939 | EM-959 |
Method Only, Application of Method or Model Run
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Method + Application | Method Only | 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-195 | EM-939 | 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 | EM-941 | None |
EM Modeling Approach
EM ID
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EM-195 | EM-939 | EM-959 |
EM Temporal Extent
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1982-2010 | Not applicable | Not applicable |
EM Time Dependence
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time-stationary | Not applicable | 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-195 | EM-939 | EM-959 |
Bounding Type
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Physiographic or Ecological | No location (no locational reference given) | Not applicable |
Spatial Extent Name
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Tampa Bay Estuary | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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1000-10,000 km^2. | >1,000,000 km^2 | Not applicable |
EM ID
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EM-195 | EM-939 | EM-959 |
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 | map scale, for cartographic feature |
Spatial Grain Size
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1 ha | Pixel size | HUC 12 |
EM ID
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EM-195 | EM-939 | EM-959 |
EM Computational Approach
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Analytic | Numeric | 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-195 | EM-939 | EM-959 |
Model Calibration Reported?
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Yes | No | Not applicable |
Model Goodness of Fit Reported?
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No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | Unclear | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Unclear | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-195 | EM-939 | EM-959 |
None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-195 | EM-939 | EM-959 |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-195 | EM-939 | EM-959 |
Centroid Latitude
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27.95 | Not applicable | Not applicable |
Centroid Longitude
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-82.47 | Not applicable | Not applicable |
Centroid Datum
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WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
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Estimated | Not applicable | Not applicable |
EM ID
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EM-195 | EM-939 | EM-959 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Subtropical Estuary | Not applicable | Terrestrial and freshwater aquatic |
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 coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-195 | EM-939 | EM-959 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-195 | EM-939 | EM-959 |
None Available | None Available | 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-195 | EM-939 | EM-959 |
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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-195 | EM-939 | EM-959 |
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None | None |