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-65 | EM-260 | EM-944 |
EM Short Name
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Green biomass production, Central French Alps | Coral taxa and land development, St.Croix, VI, USA | COBRA v 4.1 |
EM Full Name
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Green biomass production, Central French Alps | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | COBRA (CO–Benefits Risk Assessment) v 4.1 |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA |
EM Source Document ID
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260 | 96 |
437 ?Comment:User's manual is provided at the webpage. |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | US EPA |
Document Year
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2011 | 2011 | 2021 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) |
Document Status
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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 | Webpage |
EM ID
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EM-65 | EM-260 | EM-944 |
Not applicable | Not applicable | https://www.epa.gov/cobra | |
Contact Name
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Sandra Lavorel | Leah Oliver | Emma Zinsmeister |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | National Health and Environmental Research Effects Laboratory | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | leah.oliver@epa.gov | zinsmeister.emma@epa.gov |
EM ID
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EM-65 | EM-260 | EM-944 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | 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) | Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition." |
Specific Policy or Decision Context Cited
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None identified | Not applicable | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | nearshore; <1.5 km offshore; <12 m depth | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Not applicable | No scenarios presented |
EM ID
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EM-65 | EM-260 | EM-944 |
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 | 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-65 | EM-260 | EM-944 |
Document ID for related EM
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Doc-260 | None | None |
EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None |
EM Modeling Approach
EM ID
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EM-65 | EM-260 | EM-944 |
EM Temporal Extent
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2007-2009 | 2006-2007 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | Not applicable |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-65 | EM-260 | EM-944 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Geopolitical |
Spatial Extent Name
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Central French Alps | St.Croix, U.S. Virgin Islands | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | Not applicable |
EM ID
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EM-65 | EM-260 | EM-944 |
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 | map scale, for cartographic feature |
Spatial Grain Size
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20 m x 20 m | Not applicable | user defined |
EM ID
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EM-65 | EM-260 | EM-944 |
EM Computational Approach
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Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | stochastic |
Statistical Estimation of EM
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EM ID
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EM-65 | EM-260 | EM-944 |
Model Calibration Reported?
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No | 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 |
Model Operational Validation Reported?
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Yes | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | Yes | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | 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-65 | EM-260 | EM-944 |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-260 | EM-944 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-65 | EM-260 | EM-944 |
Centroid Latitude
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45.05 | 17.75 | Not applicable |
Centroid Longitude
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6.4 | -64.75 | Not applicable |
Centroid Datum
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WGS84 | NAD83 | Not applicable |
Centroid Coordinates Status
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Provided | Estimated | Not applicable |
EM ID
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EM-65 | EM-260 | EM-944 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | stony coral reef | Not applicable |
EM Ecological Scale
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Not applicable | Ecological scale is finer than that of 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-65 | EM-260 | EM-944 |
EM Organismal Scale
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Community | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-260 | EM-944 |
None Available |
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None Available |
EnviroAtlas URL
EM-65 | EM-260 | EM-944 |
GAP Ecological Systems | None Available | Total Annual Nitrogen Deposition |
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-65 | EM-260 | EM-944 |
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-65 | EM-260 | EM-944 |
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None |