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-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
EM Short Name
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C Sequestration and De-N, Tampa Bay, FL, USA | Coral taxa and land development, St.Croix, VI, USA | Human well-being index, Pensacola Bay, Florida | EcoSim II - method | CMAQ community multiscale air quality model |
EM Full Name
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Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Human well-being index (HWBI), Pensacola Bay, Florida | EcoSim II - method | Science Algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) Modeling System |
EM Source or Collection
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US EPA | US EPA | US EPA | None | US EPA |
EM Source Document ID
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186 | 96 | 418 | 448 | 478 |
Document Author
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Russell, M. and Greening, H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Yee, S.H., Paulukonis, E., Simmons, C., Russell, M., Fullford, R., Harwell, L., and L.M. Smith | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Byun, D.W. and J.K.S. Ching |
Document Year
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2013 | 2011 | 2021 | 2000 | 1999 |
Document Title
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Estimating benefits in a recovering estuary: Tampa Bay, Florida | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Projecting effects of land use change on human well being through changes in ecosystem services | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Science Algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) Modeling System. |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report |
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
Not applicable | Not applicable | Not applicable | https://ecopath.org/downloads/ | https://github.com/USEPA/CMAQ/blob/main/DOCS/Users_Guide/README.md | |
Contact Name
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M. Russell | Leah Oliver | Susan Yee | Carl Walters | D. W. BYUN |
Contact Address
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US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | National Health and Environmental Research Effects Laboratory | Gulf Ecosystem Measurement and Modeling Division, Center for Environmental Measurement and Modeling, US Environmental Prntection Agency, Gulf Breeze, FL 32561, USA | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | Atmospheric Modeling Division National Exposure Research Laboratory U.S. Environmental Protection Agency Research Triangle Park, NC 27711 |
Contact Email
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Russell.Marc@epamail.epa.gov | leah.oliver@epa.gov | yee.susan@epa.gov | c.walters@oceans.ubc.ca | bdx@hpcc.epa.gov |
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
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'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | ABSTRACT: "Changing patterns of land use, temperature, and precipitation are expected to impact ecosystem se1vices, including water quality and quantity, buffering of extreme events, soil quality, and biodiversity. Scenario ana lyses that link such impacts on ecosystem se1vices to human well-being may be valuable in anticipating potential consequences of change that are meaningful to people living in a community. Ecosystem se1vices provide munerous benefits to community well-being, including living standards, health, cultural fulfillment, education, and connection to nature. Yet assessments of impacts of ecosystem se1vices on human well-being have largely focused on human health or moneta1y benefits (e.g. market values). This study applies a human well-being modeling framework to demonsffate the potential impacts of alternative land use scenarios on multi-faceted components of human well-being through changes in ecosystem se1vices (i.e., ecological benefits functions). The modeling framework quantitatively defines these relationships in a way that can be used to project the influence of ecosystem se1vice flows on indicators of human well-being, alongside social se1vice flows and economic se1vice flows. Land use changes are linked to changing indicators of ecosystem se1vices through the application of ecological production functions. The approach is demonstrated for two future land use scenarios in a Florida watershed, representing different degrees of population growth and environmental resource protection. Increasing rates of land development were almost universally associated with declines in ecosystem se1vices indicators and associated indicators of well-being, as natural ecosystems were replaced by impe1vious surfaces that depleted the ability of ecosystems to buffer air pollutants, provide habitat for biodiversity, and retain rainwater. Scenarios with increases in indicators of ecosystem se1vices, however, did not necessarily translate into increases in indicators of well-being, due to cova1ying changes in social and economic se1vices indicators. The approach is broadly ffansferable to other communities or decision scenarios and se1ves to illustrate the potential impacts of changing land use on ecosystem se1vices and human well-being. " | 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 " | Models-3 is a flexible software system that provides a user-interface framework for CMAQ air quality modeling applications and tools for analysis, management of model input/output, and visualization of data. The Models-3 framework relies on two modeling systems to provide the meteorological and emissions data needed for air quality modeling. With this data, the Models-3 CMAQ modeling system can be used for urban and regional scale air quality simulation of tropospheric ozone, acid deposition, visibility, and particulate matter (PM2.5 and PM10). The meteorological and emissions modeling systems that are provided with the current release of Models-3 will be described in this document. However, CMAQ is designed as an open system where alternative models can be used to generate the data. |
Specific Policy or Decision Context Cited
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Restoration of seagrass | Not applicable | None identified | None | None provided |
Biophysical Context
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Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | nearshore; <1.5 km offshore; <12 m depth | N/A | None, Ocean ecosystems | NA |
EM Scenario Drivers
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Habitat loss or restoration in Tampa Bay Estuary | Not applicable | N/A | N/A | NA |
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | 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-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
Document ID for related EM
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None | None | None | None | None |
EM ID for related EM
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None | None | EM-882 | None | EM-1019 | EM-1020 | EM-1021 |
EM Modeling Approach
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
EM Temporal Extent
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1982-2010 | 2006-2007 | 2010 | Not applicable | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | both | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
discrete ?Comment:Modeller dependent |
continuous |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Day | Not applicable |
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Other | Geopolitical |
Spatial Extent Name
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Tampa Bay Estuary | St.Croix, U.S. Virgin Islands | Pensacola Bay Region | Not applicable | United States |
Spatial Extent Area (Magnitude)
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1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 | Not applicable | >1,000,000 km^2 |
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
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) | 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 | Not applicable | grid cells |
Spatial Grain Size
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1 ha | Not applicable | county | Not applicable | 32km |
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
Model Calibration Reported?
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Yes | Yes | Unclear | No | Not applicable |
Model Goodness of Fit Reported?
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No | Yes | Not applicable | No |
Yes ?Comment:For particulate matter goodness of fit |
Goodness of Fit (metric| value | unit)
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None |
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None | None |
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Model Operational Validation Reported?
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No | No | No | Not applicable | Yes |
Model Uncertainty Analysis Reported?
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No | Yes | Yes | Not applicable | Unclear |
Model Sensitivity Analysis Reported?
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No | No | Yes | Not applicable | Unclear |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
Centroid Latitude
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27.95 | 17.75 | 30.05 | Not applicable | 38.79 |
Centroid Longitude
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-82.47 | -64.75 | -87.61 | Not applicable | 106.53 |
Centroid Datum
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WGS84 | NAD83 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable | Estimated |
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Open Ocean and Seas | Atmosphere |
Specific Environment Type
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Subtropical Estuary | stony coral reef | Mixed | Pelagic | Land-sea-air interface |
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 finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Not applicable |
Scale of differentiation of organisms modeled
EM ID
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EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-195 | EM-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
None Available |
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None Available |
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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-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
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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-260 |
EM-880 ![]() |
EM-964 | EM-1012 |
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