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
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EM ID
em.detail.idHelp
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EM-890 | EM-1006 |
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EM Short Name
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HWB Blood pressure, Great Lakes waterfront, USA | Vista land-sea planning submodel |
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EM Full Name
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Human well being indicator- Blood pressure, Great Lakes waterfront, USA | A technical guide to the integrated land-sea planning toolkit |
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EM Source or Collection
em.detail.emSourceOrCollectionHelp
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None | None |
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EM Source Document ID
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422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
473 |
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Document Author
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Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., |
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Document Year
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None | 2009 |
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Document Title
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Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. |
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Document Status
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Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published |
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Comments on Status
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Journal manuscript submitted or in review | Published report |
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EM ID
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EM-890 | EM-1006 |
| Not applicable | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | |
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Contact Name
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Ted Angradi |
Patrick Crist ?Comment:No contact information provided |
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Contact Address
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USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | None provided |
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Contact Email
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tedangradi@gmail.com | patrick@planitfwd.com |
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EM ID
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EM-890 | EM-1006 |
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Summary Description
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ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | NatureServe Vista is a broad assessment and planning decision support tool focused on conservation of specific mapped features or “conservation elements.” It facilitates capturing spatial and non-spatial information and conservation requirements for elements, defining scenarios of land use, management, conservation, disturbance, etc., and evaluating the impacts of scenarios on the elements. Vista also contains powerful internal tools and interoperability with outside tools to facilitate mitigating site-level conflicts, offsite mitigation, and development of alternative scenarios. The primary objective (though not exclusive application) of the tool is to develop/mitigate alternative scenarios such that they meet explicit conservation goals for the elements. Vista can also support goal seeking for competing land uses while preventing development of scenarios that attempt to meet goals for conflicting things in the same place. The primary role of NatureServe Vista in this toolkit is to evaluate the impacts of land use scenarios on conservation elements in terrestrial, freshwater, and marine ecosystems. It does this through direct evaluation of land use scenarios from CommunityViz (augmented with other use, management, disturbance data) and interoperating with N-SPECT to evaluate water quality impacts on aquatic/marine elements. |
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Specific Policy or Decision Context Cited
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None identified | None provided |
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Biophysical Context
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Waterfront districts on south Lake Michigan and south lake Erie | Not applicable |
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EM Scenario Drivers
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N/A | No scenarios presented |
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EM ID
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EM-890 | EM-1006 |
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Method Only, Application of Method or Model Run
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Method + Application | Method Only |
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New or Pre-existing EM?
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New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
em.detail.idHelp
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EM-890 | EM-1006 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-422 | Doc-473 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 | EM-1003 | EM-1005 | EM-1007 | EM-1008 |
EM Modeling Approach
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EM ID
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EM-890 | EM-1006 |
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EM Temporal Extent
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2022 | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | other or unclear (comment) |
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EM Temporal Grain Size Value
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Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable |
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EM ID
em.detail.idHelp
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EM-890 | EM-1006 |
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Bounding Type
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Geopolitical | Not applicable |
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Spatial Extent Name
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Great Lakes waterfront | Not applicable |
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Spatial Extent Area (Magnitude)
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1000-10,000 km^2. | Not applicable |
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EM ID
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EM-890 | EM-1006 |
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EM Spatial Distribution
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spatially lumped (in all cases) | other or unclear (comment) |
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Spatial Grain Type
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Not applicable | Not applicable |
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Spatial Grain Size
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Not applicable | Not applicable |
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EM ID
em.detail.idHelp
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EM-890 | EM-1006 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic |
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EM Determinism
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deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
em.detail.idHelp
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EM-890 | EM-1006 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-890 | EM-1006 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-890 | EM-1006 |
| None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-890 | EM-1006 |
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Centroid Latitude
em.detail.ddLatHelp
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42.26 | Not applicable |
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Centroid Longitude
em.detail.ddLongHelp
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-87.84 | Not applicable |
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Centroid Datum
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WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Not applicable |
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EM ID
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EM-890 | EM-1006 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Not applicable |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Lake Michigan & Lake Erie waterfront | None |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-890 | EM-1006 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community |
Taxonomic level and name of organisms or groups identified
| EM-890 | EM-1006 |
| None Available | None Available |
EnviroAtlas URL
| EM-890 | EM-1006 |
| GAP Ecological Systems, Enabling Conditions | Watersheds, Ecosystem Markets: Imperiled Species and Habitats |
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-890 | EM-1006 |
| None |
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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-890 | EM-1006 |
| None | None |
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