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-185 | EM-684 | EM-964 |
EM Short Name
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Blue crabs and SAV, Chesapeake Bay, USA | Beach visitation, Barnstable, MA, USA | EcoSim II - method |
EM Full Name
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Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | Beach visitation, Barnstable, Massachusetts, USA | EcoSim II - method |
EM Source or Collection
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None | US EPA | None |
EM Source Document ID
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292 ?Comment:Conference paper |
386 | 448 |
Document Author
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Mykoniatis, N. and Ready, R. | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell |
Document Year
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2013 | 2018 | 2000 |
Document Title
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Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II |
Document Status
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Not formally documented | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Conference proceedings | Published journal manuscript | Published journal manuscript |
EM ID
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EM-185 | EM-684 | EM-964 |
Not applicable | Not applicable | https://ecopath.org/downloads/ | |
Contact Name
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Nikolaos Mykoniatis | Kate K, Mulvaney | Carl Walters |
Contact Address
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Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | Not reported | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 |
Contact Email
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Not reported | Mulvaney.Kate@EPA.gov | c.walters@oceans.ubc.ca |
EM ID
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EM-185 | EM-684 | EM-964 |
Summary Description
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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: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " |
Specific Policy or Decision Context Cited
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Not applicable | To assess the number of people who would be impacted by beach closures. | None |
Biophysical Context
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Submerged Aquatic Vegetation (SAV), eelgrass | Four separate beaches within the community of Barnstable | None, Ocean ecosystems |
EM Scenario Drivers
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Essential or Facultative habitat | No scenarios presented | N/A |
EM ID
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EM-185 | EM-684 | EM-964 |
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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Application of existing 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-185 | EM-684 | EM-964 |
Document ID for related EM
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Doc-227 | Doc-386 | Doc-387 | None |
EM ID for related EM
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EM-106 | EM-682 | EM-685 | EM-683 | EM-686 | None |
EM Modeling Approach
EM ID
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EM-185 | EM-684 | EM-964 |
EM Temporal Extent
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1993-2011 | 2011 - 2016 | Not applicable |
EM Time Dependence
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time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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past time | past time | both |
EM Time Continuity
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discrete | discrete |
discrete ?Comment:Modeller dependent |
EM Temporal Grain Size Value
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1 | 1 | 1 |
EM Temporal Grain Size Unit
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Year | Day | Day |
EM ID
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EM-185 | EM-684 | EM-964 |
Bounding Type
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Physiographic or ecological | Physiographic or ecological | Other |
Spatial Extent Name
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Chesapeake Bay | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Not applicable |
Spatial Extent Area (Magnitude)
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10,000-100,000 km^2 | 10-100 ha | Not applicable |
EM ID
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EM-185 | EM-684 | EM-964 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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Not applicable | length, for linear feature (e.g., stream mile) | Not applicable |
Spatial Grain Size
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Not applicable | by beach site | Not applicable |
EM ID
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EM-185 | EM-684 | EM-964 |
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-185 | EM-684 | EM-964 |
Model Calibration Reported?
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Yes | Yes | No |
Model Goodness of Fit Reported?
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Yes | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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Yes | No | Not applicable |
Model Uncertainty Analysis Reported?
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Yes | No | Not applicable |
Model Sensitivity Analysis Reported?
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Yes | Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Yes | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-185 | EM-684 | EM-964 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-185 | EM-684 | EM-964 |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-185 | EM-684 | EM-964 |
Centroid Latitude
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36.99 | 41.64 | Not applicable |
Centroid Longitude
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-75.95 | -70.29 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Not applicable |
EM ID
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EM-185 | EM-684 | EM-964 |
EM Environmental Sub-Class
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None | Near Coastal Marine and Estuarine | Open Ocean and Seas |
Specific Environment Type
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Yes | Saltwater beach | Pelagic |
EM Ecological Scale
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Yes | Ecological scale corresponds to 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-185 | EM-684 | EM-964 |
EM Organismal Scale
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Yes | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Taxonomic level and name of organisms or groups identified
EM-185 | EM-684 | EM-964 |
None Available | None Available |
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EnviroAtlas URL
EM-185 | EM-684 | EM-964 |
None Available | Average Annual Precipitation | Big game hunting recreation demand |
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-185 | EM-684 | EM-964 |
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-185 | EM-684 | EM-964 |
None |
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