EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779 
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EM-860 |
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EM Short Name
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Agronomic ES and plant traits, Central French Alps | Sed. denitrification, St. Louis River, MN/WI, USA | Land capability classification | Sedge Wren density, CREP, Iowa, USA | Arthropod flower preference, CA, USA | ARIES Sediment regulation, Santa Fe, NM |
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EM Full Name
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Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA | Land capability classification | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Arthropod flower type preference, California, USA | Artificial Intelligence for Ecosystem Services (ARIES); Sediment regulation, Santa Fe, New Mexico |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | None | None | None |
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EM Source Document ID
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260 | 333 | 340 | 372 | 399 | 411 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | United States Department of Agriculture - Natural Resources Conservation Service | 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 | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. |
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Document Year
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2011 | 2014 | 2013 | 2010 | 2018 | 2018 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Sediment nitrification and denitrification in a Lake Superior estuary | National Soil Survey Handbook - Part 622 - Interpretative Groups | 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 | Towards globally customizable ecosystem service models |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
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Contact Name
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Sandra Lavorel | Brent J. Bellinger | United States Department of Agriculture | David Otis | Ola Lundin | Javier Martinez-Lopez |
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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. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | Not reported | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Department of Ecology, Swedish Univ. of Agricultural Sciences, Uppsala, Sweden | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | bellinger.brent@epa.ogv | http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | dotis@iastate.edu | ola.lundin@slu.se | javier.martinez@bc3research.org |
EM Description
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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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: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ?Comment:BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model. |
AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] | 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." | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | None provided | None identified | None reported | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Estuarine system | No additional description provided | Prairie pothole region of north-central Iowa | Mediteranean climate | Watersheds surrounding Santa Fe and Albuquerque, New Mexico |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Arthropod groups | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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Document ID for related EM
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Doc-260 | Doc-270 | None | None | Doc-372 | None | Doc-411 |
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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 | None | None | EM-652 | EM-651 | EM-649 | EM-648 | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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EM Temporal Extent
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Not reported | 2011 - 2012 | Not applicable | 1992-2007 | 2015-2016 | 2011 |
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EM Time Dependence
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time-stationary | time-stationary | Not applicable | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Point or points | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | St. Louis River estuary | Not applicable | CREP (Conservation Reserve Enhancement Program) wetland sites | Harry Laidlaw Jr. Honey Bee Research facility | Santa Fe Fireshed |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | Not applicable | 1-10 km^2 | <1 ha | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | Not applicable | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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20 m x 20 m | Not applicable | Not applicable | multiple, individual, irregular shaped sites | Not applicable | 30 m |
EM Structure and Computation Approach
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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EM Computational Approach
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Analytic | Analytic | Not applicable | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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Model Calibration Reported?
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No | No | Not applicable | Unclear | Not applicable | Unclear |
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Model Goodness of Fit Reported?
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No | No | Not applicable | No | Not applicable | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | Unclear | Not applicable | No |
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Model Uncertainty Analysis Reported?
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No | No | Not applicable | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | Not applicable | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
| None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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Centroid Latitude
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45.05 | 46.75 | Not applicable | 42.62 | 38.54 | 35.86 |
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Centroid Longitude
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6.4 | -92.08 | Not applicable | -93.84 | -121.79 | -105.76 |
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Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Not applicable | Estimated | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Freshwater estuary | None identified | Grassland buffering inland wetlands set in agricultural land | Agricultural fields | watersheds |
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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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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EM Organismal Scale
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Community | Not applicable | Not applicable | Species | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
| None Available | 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.detail.cicesHelp
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| EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-80 | EM-416 | EM-434 | EM-650 |
EM-779
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EM-860 |
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None |
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None |