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-71 | EM-195 | EM-260 | EM-964 |
EM Short Name
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Community flowering date, Central French Alps | C Sequestration and De-N, Tampa Bay, FL, USA | Coral taxa and land development, St.Croix, VI, USA | EcoSim II - method |
EM Full Name
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Community weighted mean flowering date, Central French Alps | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | EcoSim II - method |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | None |
EM Source Document ID
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260 | 186 | 96 | 448 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Russell, M. and Greening, H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell |
Document Year
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2011 | 2013 | 2011 | 2000 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | 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 | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II |
Document Status
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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 |
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
Not applicable | Not applicable | Not applicable | https://ecopath.org/downloads/ | |
Contact Name
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Sandra Lavorel | M. Russell | Leah Oliver | Carl Walters |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | National Health and Environmental Research Effects Laboratory | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | Russell.Marc@epamail.epa.gov | leah.oliver@epa.gov | c.walters@oceans.ubc.ca |
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
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." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | 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: " 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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None identified | Restoration of seagrass | Not applicable | None |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | nearshore; <1.5 km offshore; <12 m depth | None, Ocean ecosystems |
EM Scenario Drivers
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No scenarios presented | Habitat loss or restoration in Tampa Bay Estuary | Not applicable | N/A |
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
Method Only, Application of Method or Model Run
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Method + Application | 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 | 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-71 | EM-195 | EM-260 | EM-964 |
Document ID for related EM
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Doc-260 | Doc-269 | None | None | None |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None |
EM Modeling Approach
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
EM Temporal Extent
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2007-2008 | 1982-2010 | 2006-2007 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | both |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
discrete ?Comment:Modeller dependent |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Day |
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Other |
Spatial Extent Name
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Central French Alps | Tampa Bay Estuary | St.Croix, U.S. Virgin Islands | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 1000-10,000 km^2. | 10-100 km^2 | Not applicable |
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
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20 m x 20 m | 1 ha | Not applicable | Not applicable |
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
Model Calibration Reported?
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No | Yes | Yes | No |
Model Goodness of Fit Reported?
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Yes | No | Yes | No |
Goodness of Fit (metric| value | unit)
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None |
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None |
Model Operational Validation Reported?
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No | No | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Yes | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-71 | EM-195 | EM-260 | EM-964 |
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None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-71 | EM-195 | EM-260 | EM-964 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
Centroid Latitude
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45.05 | 27.95 | 17.75 | Not applicable |
Centroid Longitude
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6.4 | -82.47 | -64.75 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | NAD83 | Not applicable |
Centroid Coordinates Status
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Provided | Estimated | Estimated | Not applicable |
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Open Ocean and Seas |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Subtropical Estuary | stony coral reef | Pelagic |
EM Ecological Scale
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Not applicable | 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 |
Scale of differentiation of organisms modeled
EM ID
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EM-71 | EM-195 | EM-260 | EM-964 |
EM Organismal Scale
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Community | Not applicable | Guild or Assemblage |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Taxonomic level and name of organisms or groups identified
EM-71 | EM-195 | EM-260 | EM-964 |
None Available | None Available |
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EnviroAtlas URL
EM-71 | EM-195 | EM-260 | EM-964 |
None Available | Carbon Storage by Tree Biomass | None Available | 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-71 | EM-195 | EM-260 | 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-71 | EM-195 | EM-260 | EM-964 |
None |
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