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-70 | EM-439 | EM-841 |
EM Short Name
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Plant species diversity, Central French Alps | WaSSI, Conterminous USA | Brown-headed cowbird abundance, Piedmont, USA |
EM Full Name
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Plant species diversity, Central French Alps | Water Supply Stress Index, Conterminous USA | Brown-headed cowbird abundance, Piedmont ecoregion, USA |
EM Source or Collection
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EU Biodiversity Action 5 |
USDA Forest Service ?Comment:While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. |
None |
EM Source Document ID
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260 | 341 | 405 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez | Riffel, S., Scognamillo, D., and L. W. Burger |
Document Year
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2011 | 2013 | 2008 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | WaSSI Ecosystem Services Model | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds |
Document Status
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Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published |
Comments on Status
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Published journal manuscript | While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. | Published journal manuscript |
EM ID
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EM-70 | EM-439 | EM-841 |
Not applicable | http://www.wassiweb.sgcp.ncsu.edu/ | Not applicable | |
Contact Name
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Sandra Lavorel | Ge Sun | Sam Riffell |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | gesun@fs.fed.us | sriffell@cfr.msstate.edu |
EM ID
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EM-70 | EM-439 | EM-841 |
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: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | AUTHORS DESCRIPTION: "WaSSI simulates monthly water and carbon dynamics at the Hydrologic Unit Code 8 level in the US. Three modules are integrated within the WaSSI model framework. The water balance module computes ecosystem water use, evapotranspiration and the water yield from each watershed. Water yield is sometimes referred to as runoff and can be thought of as the amount of streamflow at the outlet of each watershed due to hydrologic processes in each watershed in isolation without any flow contribution from upstream watersheds. The ecosystem productivity module simulates carbon gains and losses in each watershed or grid cell as functions of evapotranspiration. The water supply and demand module routes and accumulates the water yield through the river network according to topological relationships between adjacent watersheds, subtracts consumptive water use by humans from river flows, and compares water supply to water demand to compute the water supply stress index, or WaSSI." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " |
Specific Policy or Decision Context Cited
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None identified | WaSSI can be used to project the regional effects of forest land cover change, climate change, and water withdrawals on river flows, water supply stress, and ecosystem productivity (i.e., carbon sequestration).WaSSI can be used to evaluate trade-offs among management strategies that influence multiple ecosystem services | None reported |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Conterminous US | Conservation Reserve Program lands left to go fallow |
EM Scenario Drivers
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No scenarios presented |
No scenarios presented ?Comment:Model can be run from WaSSI website using a historic data set (1961 - 2010) or projections from various climate models representing different emissions scenarios and time periods from recent past to 2099. |
N/A |
EM ID
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EM-70 | EM-439 | EM-841 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model |
Application of existing model ?Comment:. |
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-70 | EM-439 | EM-841 |
Document ID for related EM
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Doc-260 | None | Doc-405 |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | EM-831 | EM-838 | EM-839 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 |
EM Modeling Approach
EM ID
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EM-70 | EM-439 | EM-841 |
EM Temporal Extent
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2007-2009 | 1961-2009 | 2008 |
EM Time Dependence
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time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Month | Not applicable |
EM ID
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EM-70 | EM-439 | EM-841 |
Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or ecological |
Spatial Extent Name
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Central French Alps | All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA | Piedmont Ecoregion |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 |
EM ID
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EM-70 | EM-439 | EM-841 |
EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is the HUC-8. A HUC-12 version is under development. Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
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 | Computations are at the 8-digit HUC scale. MostHUC-8 watersheds are within a range of 800-8000 km^2 (500-5000 mi^2) in size. | Not applicable |
EM ID
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EM-70 | EM-439 | EM-841 |
EM Computational Approach
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Analytic | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-70 | EM-439 | EM-841 |
Model Calibration Reported?
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No | No | Yes |
Model Goodness of Fit Reported?
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Yes | No | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | No | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-70 | EM-439 | EM-841 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-70 | EM-439 | EM-841 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-70 | EM-439 | EM-841 |
Centroid Latitude
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45.05 | 39.83 | 36.23 |
Centroid Longitude
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6.4 | -98.58 | -81.9 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Estimated |
EM ID
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EM-70 | EM-439 | EM-841 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands |
Lakes and Ponds ?Comment:Watershed model represents all land areas, major streams and rivers. Since leaf area index, LAI, is an important variable, forests, created greenspaces (e.g., urban forests) and scrub/shrub subclasses are included. |
Grasslands |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Not applicable | grasslands |
EM Ecological Scale
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Not applicable |
Ecological scale is coarser than that of the Environmental Sub-class ?Comment:Terrestrial characteristics are aggregated at a broad (HUC-8) scale; different types of aquatic sub-classes are not differentiated. |
Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-70 | EM-439 | EM-841 |
EM Organismal Scale
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Community | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-70 | EM-439 | EM-841 |
None Available | None Available |
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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-70 | EM-439 | EM-841 |
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-70 | EM-439 | EM-841 |
None |
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