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
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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EM Short Name
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Divergence in flowering date, Central French Alps | Cultural ES and plant traits, Central French Alps | Reduction in pesticide runoff risk, Europe | CMAQ chemical transport model, UK |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | Cultural ecosystem service estimated from plant functional traits, Central French Alps | Reduction in pesticide runoff risk, Europe | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | None |
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EM Source Document ID
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260 | 260 | 255 | 483 |
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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. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Chemel, C., Fisher, B.E.A., Kong, X., Francis, X.V., Sokhi, R.S., Good, N., Collins, W.J. and Folberth, G.A. |
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Document Year
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2011 | 2011 | 2012 | 2014 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
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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 |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
| Not applicable | Not applicable | Not applicable | https://www.epa.gov/cmaq/download-cmaq | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | Sven Lautenbach | B.E.A. Fisher |
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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 | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | Little Beeches, Headley Road, Leatherhead KT22 8PT, UK. |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sven.lautenbach@ufz.de | None provided |
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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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. 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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date 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: "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 Cultural ecosystem service map was 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 cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions." | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | This paper shows how the advanced chemical transport model CMAQ can be used to estimate future levels of PM2.5 in the UK, the key air pollutant in terms of human health effects, but one which is largely made up from the formation of secondary particulate in the atmosphere. By adding the primary particulate contribution from typical urban roads and including a margin for error, it is concluded that the current indicative limit value for PM2.5 will largely be met in 2020 assuming 2006 meteorological conditions. Contributions to annual average regional PM2.5 concentration from wild fires in Europe in 2006 and from possible climate change between 2006 and 2020 are shown to be small compared with the change in PM2.5 concentration arising from changes in emissions between 2006 and 2020. The contribution from emissions from major industrial sources regulated in the UK is estimated from additional CMAQ calculations. The potential source strength of these emissions is a useful indicator of the linearity of the response of the atmosphere to changes in emissions. Uncertainties in the modelling of regional and local sources are taken into account based on previous evaluations of the models. Future actual trends in emissions mean that exceedences of limit values may arise, and these and further research into PM2.5 health effects will need to be part of the future strategy to manage PM2.5 concentrations. |
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Specific Policy or Decision Context Cited
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None identified | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Not applicable | United kingdom atmosphere |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | 2020 European emissions scenario |
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing 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-79 | EM-81 | EM-94 | EM-1021 |
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Document ID for related EM
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Doc-260 | Doc-269 | None |
Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-478 | Doc-481 | Doc-482 |
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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-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-82 | EM-83 | None | EM-1012 | EM-1019 | EM-1020 |
EM Modeling Approach
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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EM Temporal Extent
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2007-2008 | Not reported | 2000 | 2006-2020 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | both |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 14 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Year |
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical |
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Spatial Extent Name
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Central French Alps | Central French Alps | EU-27 | United Kingdom |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 |
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 10 km x 10 km | Not applicable |
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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Model Calibration Reported?
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No | No | No | Yes |
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Model Goodness of Fit Reported?
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Yes | No | No |
Yes ?Comment:Two versions of CMAQ (v4.6 and v4.7) were used to assess performance. Both values are provided here respectively. |
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Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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No | No | Yes | No |
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Model Uncertainty Analysis Reported?
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No | No | No | Unclear |
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Model Sensitivity Analysis Reported?
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No | No | No | Unclear |
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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-79 | EM-81 | EM-94 | EM-1021 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-79 | EM-81 | EM-94 | EM-1021 |
| None | None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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Centroid Latitude
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45.05 | 45.05 | 50.53 | 54 |
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Centroid Longitude
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6.4 | 6.4 | 7.6 | 4 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Provided | Estimated | Estimated |
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Atmosphere |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Streams and near upstream environments | United Kingdom atmosphere |
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EM Ecological Scale
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-79 | EM-81 | EM-94 | EM-1021 |
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EM Organismal Scale
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Community | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-79 | EM-81 | EM-94 | EM-1021 |
| None Available | None Available | None Available | None Available |
EnviroAtlas URL
| EM-79 | EM-81 | EM-94 | EM-1021 |
| None Available | GAP Ecological Systems | Stream Length Impaired by Pesticides | Average Annual Precipitation, Average Annual Daily Potential Wind Energy |
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-79 | EM-81 | EM-94 | EM-1021 |
| 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-79 | EM-81 | EM-94 | EM-1021 |
| None | None | None |
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