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-964 | EM-968 |
EM Short Name
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C Sequestration and De-N, Tampa Bay, FL, USA | EcoSim II - method | EPA Stormwater Manamgement Model |
EM Full Name
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Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | EcoSim II - method | Storm Water Management Model User's Manual Version 5.2 |
EM Source or Collection
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US EPA | None | US EPA |
EM Source Document ID
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186 | 448 | 452 |
Document Author
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Russell, M. and Greening, H. | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Rossman, L. A., M., Simon |
Document Year
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2013 | 2000 | 2022 |
Document Title
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Estimating benefits in a recovering estuary: Tampa Bay, Florida | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Storm Water Management Model User's Manual Version 5.2 |
Document Status
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Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published EPA report |
EM ID
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EM-195 | EM-964 | EM-968 |
Not applicable | https://ecopath.org/downloads/ | https://www.epa.gov/water-research/storm-water-management-model-swmm | |
Contact Name
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M. Russell | Carl Walters | David Burden |
Contact Address
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US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 |
Contact Email
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Russell.Marc@epamail.epa.gov | c.walters@oceans.ubc.ca | burden.david@epa.gov |
EM ID
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EM-195 | EM-964 | EM-968 |
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) | 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 " |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
Specific Policy or Decision Context Cited
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Restoration of seagrass | None | NA |
Biophysical Context
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Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | None, Ocean ecosystems | NA |
EM Scenario Drivers
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Habitat loss or restoration in Tampa Bay Estuary | N/A | NA |
EM ID
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EM-195 | EM-964 | EM-968 |
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-964 | EM-968 |
Document ID for related EM
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None | None | None |
EM ID for related EM
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None | None | EM-971 |
EM Modeling Approach
EM ID
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EM-195 | EM-964 | EM-968 |
EM Temporal Extent
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1982-2010 | Not applicable | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | both | both |
EM Time Continuity
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Not applicable |
discrete ?Comment:Modeller dependent |
continuous |
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 | Day | Not applicable |
EM ID
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EM-195 | EM-964 | EM-968 |
Bounding Type
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Physiographic or Ecological | Other | No location (no locational reference given) |
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. | Not applicable | Not applicable |
EM ID
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EM-195 | EM-964 | EM-968 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
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1 ha | Not applicable | mm |
EM ID
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EM-195 | EM-964 | EM-968 |
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-195 | EM-964 | EM-968 |
Model Calibration Reported?
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Yes | No | Not applicable |
Model Goodness of Fit Reported?
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No | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
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No | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
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No | Not applicable | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-195 | EM-964 | EM-968 |
None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-195 | EM-964 | EM-968 |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-195 | EM-964 | EM-968 |
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-964 | EM-968 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Open Ocean and Seas | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Subtropical Estuary | Pelagic | User-defined catchments |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Other or unclear (comment) |
Scale of differentiation of organisms modeled
EM ID
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EM-195 | EM-964 | EM-968 |
EM Organismal Scale
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Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-195 | EM-964 | EM-968 |
None Available |
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None Available |
EnviroAtlas URL
EM-195 | EM-964 | EM-968 |
Carbon Storage by Tree Biomass | Big game hunting recreation demand | None Available |
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-964 | EM-968 |
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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-195 | EM-964 | EM-968 |
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