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-137 | EM-260 | EM-942 |
EM Short Name
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Green biomass production, Central French Alps | i-Tree Hydro v4.0 | Coral taxa and land development, St.Croix, VI, USA | Pollutant dispersion by vegetation barriers |
EM Full Name
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Green biomass production, Central French Alps | i-Tree Hydro v4.0 (default data option) | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Pollutant dispersion by vegetation barriers |
EM Source or Collection
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EU Biodiversity Action 5 | i-Tree | USDA Forest Service | US EPA | US EPA |
EM Source Document ID
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260 | 198 | 96 | 435 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | USDA Forest Service | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Hashad, K. B. Yang, J. T. Steffens, R. W. Baldauf, P. Deshmukh, K. M. Zhang |
Document Year
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2011 | Not Reported | 2011 | 2021 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | i-Tree Hydro User's Manual v. 4.0 | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Parameterizing pollutant dispersion downwind of roadside vegetation barriers |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) |
Comments on Status
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Published journal manuscript | Webpage | Published journal manuscript | Journal manuscript submitted or in review |
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
Not applicable | http://www.itreetools.org | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Not applicable | Leah Oliver | K. Max Zhang |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Not applicable | National Health and Environmental Research Effects Laboratory | Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | Not applicable | leah.oliver@epa.gov | kz33@cornell.edu |
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
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)." | ABSTRACT: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality." | 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: "Communities living and working in near-road environments are exposed to elevated levels of traffic-related air pollution (TRAP), causing adverse health effects. Roadside vegetation may help reduce TRAP through enhanced deposition and mixing….there are no studies that developed a dispersion model to characterize pollutant concentrations downwind of vegetation barriers. To account for the physical mechanisms, by which the vegetation barrier deposits and disperses pollutants, we propose a multi-region approach that describes the parameters of the standard Gaussian equations in each region. The four regions include the vegetation, a downwind wake, a transition, and a recovery zone. For each region, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition which is a major factor in pollutant reduction by vegetation barriers. We generated data from 75 (CFD)-based simulations, using the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model, to parameterize the Gaussian-based equations. The simulations used reflected a wide range of vegetation barriers, with heights from 2-10 m, and various densities, represented by leaf area index values from 4-11, and evaluated under different urban conditions, represented by wind speeds from 1-5 m/s. The CTAG model has been evaluated against two field measurements to ensure that it can properly represent the vegetation barrier’s pollutant deposition and dispersion. The proposed multi-region Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing deposition. The multi-region model’s normalized mean error (NME) ranged between 0.18-0.3, the fractional bias (FB) ranged between -0.12-0.09, and R2 value ranged from 0.47-0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable range. Even though the multi-region model is parameterized for coniferous trees, our sensitivity study indicates that the parameterized Gaussian-based model can provide useful predictions for hedge/bushes vegetative barriers as well." ADDITIONAL DESCRIPTION: Detailed variable relationships are described in the source document. The VRD associated with the ESML entry provides variables in a simplified form. |
Specific Policy or Decision Context Cited
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None identified | None identified | Not applicable | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | No additional description provided | nearshore; <1.5 km offshore; <12 m depth | Communities living and working in near-road environments |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Not applicable | None scenarios presented |
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
Method Only, Application of Method or Model Run
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Method + Application | Method Only | 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-65 | EM-137 | EM-260 | EM-942 |
Document ID for related EM
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Doc-260 | Doc-190 | Doc-223 | 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 | EM-109 | EM-142 | EM-51 | None | None |
EM Modeling Approach
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
EM Temporal Extent
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2007-2009 | Not applicable | 2006-2007 | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-stationary | Not applicable |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Hour | Not applicable | Not applicable |
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
Bounding Type
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Physiographic or Ecological | Not applicable | Physiographic or Ecological | Not applicable |
Spatial Extent Name
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Central French Alps | Not applicable | St.Croix, U.S. Virgin Islands | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | Not applicable | 10-100 km^2 | Not applicable |
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
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 distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | length, for linear feature (e.g., stream mile) |
Spatial Grain Size
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20 m x 20 m | 30 x 30 m | Not applicable | user defined |
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
EM Computational Approach
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Analytic | Numeric | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | stochastic |
Statistical Estimation of EM
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EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
Model Calibration Reported?
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No | Not applicable | Yes | Yes |
Model Goodness of Fit Reported?
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Yes | Not applicable | Yes | Not applicable |
Goodness of Fit (metric| value | unit)
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None |
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None |
Model Operational Validation Reported?
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Yes | Not applicable | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | Not applicable | Yes | Not applicable |
Model Sensitivity Analysis Reported?
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No | Not applicable | 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-65 | EM-137 | EM-260 | EM-942 |
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None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-137 | EM-260 | EM-942 |
None | None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
Centroid Latitude
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45.05 | -9999 | 17.75 | Not applicable |
Centroid Longitude
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6.4 | -9999 | -64.75 | Not applicable |
Centroid Datum
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WGS84 | Not applicable | NAD83 | Not applicable |
Centroid Coordinates Status
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Provided | Not applicable | Estimated | Not applicable |
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Created Greenspace | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Urban watersheds | stony coral reef | Communities living and working in near-road environments |
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 is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-65 | EM-137 | EM-260 | EM-942 |
EM Organismal Scale
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Community | Community | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-137 | EM-260 | EM-942 |
None Available | 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-65 | EM-137 | EM-260 | EM-942 |
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-137 | EM-260 | EM-942 |
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None |