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-105 | EM-185 | EM-959 |
EM Short Name
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Benthic habitat associations, Willapa Bay, OR, USA | Blue crabs and SAV, Chesapeake Bay, USA | NC HUC-12 conservation prioritization tool |
EM Full Name
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Benthic macrofaunal habitat associations, Willapa Bay, OR, USA | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | 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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39 |
292 ?Comment:Conference paper |
443 ?Comment:Doc 444 is an additional source for this EM |
Document Author
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Ferraro, S. P. and Cole, F. A. | Mykoniatis, N. and Ready, R. | Warnell, K., I. Golden, and C. Canfield |
Document Year
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2007 | 2013 | 2023 |
Document Title
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Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Conservation planning tools for NC's people & nature |
Document Status
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Peer reviewed and published | Not formally documented | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Conference proceedings | Webpage |
EM ID
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EM-105 | EM-185 | EM-959 |
Not applicable | Not applicable | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | |
Contact Name
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Steve Ferraro | Nikolaos Mykoniatis | Katie Warnell |
Contact Address
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U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365 | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | Not reported |
Contact Email
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ferraro.steven@epa.gov | Not reported | katie.warnell@duke.edu |
EM ID
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EM-105 | EM-185 | EM-959 |
Summary Description
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AUTHOR'S DESCRIPTION: "In this paper we report the results of 2 estuary-wide studies of benthic macrofaunal habitat associations in Willapa Bay, Washington, USA. This research is part of an effort to develop empirical models of biota-habitat associations that can be used to help identify critical habitats, prioritize habitats for environmental protection, index habitat suitability (U.S. Fish and Wildlife Service, 1980; Kapustka, 2003), perform habitat equivalency and compensatory restoration analyses (Fonseca et al., 2002; Kirsch et al., 2005), and as habitat value criteria in ecological risk assessments (Obery and Landis, 2002; Ferraro and Cole, 2004; Landis et al., 2004)." (491) | ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | 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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None identified | Not applicable | Allows users to prioritize HUCs within their area of interest based on their conservation goals. |
Biophysical Context
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benthic estuarine | Submerged Aquatic Vegetation (SAV), eelgrass | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Essential or Facultative habitat | No scenarios presented |
EM ID
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EM-105 | EM-185 | EM-959 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only |
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-105 | EM-185 | EM-959 |
Document ID for related EM
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None | Doc-227 |
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-106 | None |
EM Modeling Approach
EM ID
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EM-105 | EM-185 | EM-959 |
EM Temporal Extent
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1996,1998 | 1993-2011 | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | past time | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable |
EM ID
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EM-105 | EM-185 | EM-959 |
Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Not applicable |
Spatial Extent Name
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Willapa Bay | Chesapeake Bay | Not applicable |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10,000-100,000 km^2 | Not applicable |
EM ID
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EM-105 | EM-185 | EM-959 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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Not applicable | Not applicable | map scale, for cartographic feature |
Spatial Grain Size
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Not applicable | Not applicable | HUC 12 |
EM ID
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EM-105 | EM-185 | EM-959 |
EM Computational Approach
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Analytic | Analytic | 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-105 | EM-185 | EM-959 |
Model Calibration Reported?
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Yes | Yes | Not applicable |
Model Goodness of Fit Reported?
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Yes | Yes | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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Yes | Yes | 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 | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-105 | EM-185 | EM-959 |
None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-105 | EM-185 | EM-959 |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-105 | EM-185 | EM-959 |
Centroid Latitude
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46.24 | 36.99 | Not applicable |
Centroid Longitude
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-124.06 | -75.95 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Provided | Estimated | Not applicable |
EM ID
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EM-105 | EM-185 | EM-959 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | None | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Drowned river valley estuary | Yes | Terrestrial and freshwater aquatic |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Yes | Ecological scale is coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-105 | EM-185 | EM-959 |
EM Organismal Scale
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Species | Yes | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-105 | EM-185 | EM-959 |
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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-105 | EM-185 | EM-959 |
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None | 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-105 | EM-185 | EM-959 |
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None | None |