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-80 | EM-650 |
EM-779 ![]() |
EM Short Name
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Agronomic ES and plant traits, Central French Alps | Sedge Wren density, CREP, Iowa, USA | Arthropod flower preference, CA, USA |
EM Full Name
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Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Arthropod flower type preference, California, USA |
EM Source or Collection
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EU Biodiversity Action 5 | None | None |
EM Source Document ID
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260 | 372 | 399 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Lundin, O., Ward, K.L., and N.M. Williams |
Document Year
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2011 | 2010 | 2018 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Indentifying native plants for coordinated hanbitat manegement of arthroppod pollinators, herbivores and natural enemies |
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 report | Published journal manuscript |
EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | David Otis | Ola Lundin |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Department of Ecology, Swedish Univ. of Agricultural Sciences, Uppsala, Sweden |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | dotis@iastate.edu | ola.lundin@slu.se |
EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
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: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | ABSTRACT: " Plant species differed in attractiveness for each arthropod functional group. Floral area of the focal plant species positively affected honeybee, predator, and parasitic wasp attractiveness. Later bloom period was associated with lower numbers of parasitic wasps. Flower type (actinomorphic, composite, or zygomorphic) predicted attractiveness for honeybees, which preferred actinomorphic over composite flowers and for parasitic wasps, which preferred composite flowers over actinomorphic flowers. 4. Across plant species, herbivore, predator, and parasitic wasp abundances were positively correlated, and honeybee abundance correlated negatively to herbivore abundance. 5. Synthesis and applications. We use data from our common garden experiment to inform evidence-based selection of plants that support pollinators and natural enemies without enhancing potential pests. We recommend selecting plant species with a high floral area per ground area unit, as this metric predicts the abundances of several groups of beneficial arthropods. Multiple correlations between functionally important arthropod groups across plant species stress the importance of a multifunctional approach to arthropod habitat management. " Changes in arthropod abundance were estimated for flower type (entered as separate runs); Actinomorphic, Composite, Zygomorphic. 43 plant species evaluated included Amsinckia intermedia, Calandrinia menziesii, Nemophila maculata, Nemophila menziesii, Phacelia ciliata, Achillea millefolium, Collinsia heterophylla, Fagopyrum esculentum, Lasthenia fremontii, Lasthenia glabrata, Limnanthes alba, Lupinus microcarpus densiflorus, Lupinus succelentus, Phacelia californica, Phacelia campanularia, Phacelia tanacetifolia, Salvia columbariae, Sphaeralcea ambigua, Trifolium fucatum, Trifolium gracilentum, Antirrhinum conutum, Clarkia purpurea, Clarkia unguiculata, Clarkia williamsonii, Eriophyllum lanatum, Eschscholzia californica, Monardella villosa, Scrophularia californica, Asclepia eriocarpa, Asclepia fascicularis, Camissoniopsis Cheiranthifolia, Eriogonum fasciculatum, Gilia capitata, Grindelia camporum, Helianthus annuus, Lupinus formosus, Malacothrix saxatilis, Oenothera elata, Helianthus bolanderi, Helianthus californicus, Madia elegans, Trichostema lanceolatum, Heterotheca grandiflora." |
Specific Policy or Decision Context Cited
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None identified | None identified | None reported |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Prairie pothole region of north-central Iowa | Mediteranean climate |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Arthropod groups |
EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
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-80 | EM-650 |
EM-779 ![]() |
Document ID for related EM
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Doc-260 | Doc-270 | Doc-372 | None |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-81 | EM-82 | EM-83 | EM-652 | EM-651 | EM-649 | EM-648 | None |
EM Modeling Approach
EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
EM Temporal Extent
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Not reported | 1992-2007 | 2015-2016 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary |
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-80 | EM-650 |
EM-779 ![]() |
Bounding Type
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Physiographic or Ecological | Multiple unrelated locations (e.g., meta-analysis) | Point or points |
Spatial Extent Name
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Central French Alps | CREP (Conservation Reserve Enhancement Program) wetland sites | Harry Laidlaw Jr. Honey Bee Research facility |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 1-10 km^2 | <1 ha |
EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
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) |
Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
Spatial Grain Size
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20 m x 20 m | multiple, individual, irregular shaped sites | Not applicable |
EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
EM Computational Approach
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Analytic | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
Model Calibration Reported?
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No | Unclear | Not applicable |
Model Goodness of Fit Reported?
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No | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | Unclear | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
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-80 | EM-650 |
EM-779 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-80 | EM-650 |
EM-779 ![]() |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
Centroid Latitude
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45.05 | 42.62 | 38.54 |
Centroid Longitude
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6.4 | -93.84 | -121.79 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Provided |
EM ID
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EM-80 | EM-650 |
EM-779 ![]() |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Grassland buffering inland wetlands set in agricultural land | Agricultural fields |
EM Ecological Scale
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to 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-80 | EM-650 |
EM-779 ![]() |
EM Organismal Scale
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Community | Species | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-80 | EM-650 |
EM-779 ![]() |
None Available |
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EnviroAtlas URL
EM-80 | EM-650 |
EM-779 ![]() |
GAP Ecological Systems, Carbon storage by tree biomass (kg/m2) | GAP Ecological Systems | GAP Ecological Systems |
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-80 | EM-650 |
EM-779 ![]() |
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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-80 | EM-650 |
EM-779 ![]() |
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