EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
EM Short Name
em.detail.shortNameHelp
?
|
Community flowering date, Central French Alps | Fish species richness, Puerto Rico, USA | WMOST method |
EM Full Name
em.detail.fullNameHelp
?
|
Community weighted mean flowering date, Central French Alps | Fish species richness, Puerto Rico, USA | Watershed Management Optimization Support Tool (WMOST) v1 method |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
EU Biodiversity Action 5 | None | US EPA |
EM Source Document ID
|
260 | 355 | 477 |
Document Author
em.detail.documentAuthorHelp
?
|
Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | United States EPA |
Document Year
em.detail.documentYearHelp
?
|
2011 | 2007 | 2013 |
Document Title
em.detail.sourceIdHelp
?
|
Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Watershed Management Optimization Support Tool (WMOST) v1 User manual |
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published EPA report |
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
Not applicable | Not applicable | https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NHEERL&dirEntryId=262280 | |
Contact Name
em.detail.contactNameHelp
?
|
Sandra Lavorel | Simon Pittman | Naomi Detenbeck |
Contact Address
|
Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | 1305 East-West Highway, Silver Spring, MD 20910, USA | NHEERL, Atlantic Ecology Division Narragansett, RI 02882 |
Contact Email
|
sandra.lavorel@ujf-grenoble.fr | simon.pittman@noaa.gov | detenbeck.naomi@epa.gov |
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
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." | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT: "The Watershed Management Optimization Support Tool (WMOST) is intended to be used as a screening tool as part of an integrated watershed management process such as that described in EPA’s watershed planning handbook (EPA 2008).1 The objective of WMOST is to serve as a public-domain, efficient, and user-friendly tool for local water resources managers and planners to screen a widerange of potential water resources management options across their watershed or jurisdiction for costeffectiveness as well as environmental and economic sustainability (Zoltay et al 2010). Examples of options that could be evaluated with the tool include projects related to stormwater, water supply, wastewater and water-related resources such as Low-Impact Development (LID) and land conservation. The tool is intended to aid in evaluating the environmental and economic costs, benefits, trade-offs and co-benefits of various management options. In addition, the tool is intended to facilitate the evaluation of low impact development (LID) and green infrastructure as alternative or complementary management options in projects proposed for State Revolving Funds (SRF). WMOST is a screening model that is spatially lumped with a daily or monthly time step. The model considers water flows but does not yet consider water quality. The optimization of management options is solved using linear programming. The target user group for WMOST consists of local water resources managers, including municipal water works superintendents and their consultants. This document includes a user guide and presentation of two case studies as examples of how to apply WMOST. Theoretical documentation is provided in a separate report (EPA/600/R-13/151). " |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None provided | Not applicable |
Biophysical Context
|
Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Hard and soft benthic habitat types approximately to the 33m isobath | None |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | None |
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
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
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-260 | Doc-269 | Doc-355 | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-698 | EM-699 | None |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2007-2008 | 2000-2005 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable |
Not applicable ?Comment:method description |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Month |
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Physiographic or ecological | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Central French Alps | SW Puerto Rico, | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | 100-1000 km^2 | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
20 m x 20 m | not reported | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Numeric |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Yes | Yes | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
|
None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | No | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-71 | EM-590 | EM-1011 |
|
None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-71 | EM-590 | EM-1011 |
None |
|
None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | 17.9 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | 67.11 | Not applicable |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Estimated | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows. | shallow coral reefs | watershed |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Not applicable | 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
em.detail.idHelp
?
|
EM-71 | EM-590 | EM-1011 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-71 | EM-590 | EM-1011 |
None Available |
|
None Available |
EnviroAtlas URL
EM-71 | EM-590 | EM-1011 |
None Available | None Available | National Hydrography Dataset Plus (NHD PlusV2), Wetlands and Streams, Agricultural water use (million gallons/day) |
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-590 | EM-1011 |
None |
|
None |
<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-590 | EM-1011 |
None |
|
None |