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-379 | EM-837 | EM-939 |
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EM Short Name
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Divergence in flowering date, Central French Alps | VELMA soil temperature, Oregon, USA | Bird species diversity on restored landfills, UK | ESTIMAP- Recreation, Europe |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Bird species diversity on restored landfills compared to paired reference sites, East Midlands, UK | ESTIMAP- Recreation, Europe |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | None |
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EM Source Document ID
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260 | 317 | 406 | 432 |
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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. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Zulian, G., Parrachini, M.L., Maes, J., |
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Document Year
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2011 | 2013 | 2011 | 2013 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | ESTIMAP: Ecosystem services mapping at the European scale |
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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 report |
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
| Not applicable | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | N.A. | |
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Contact Name
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Sandra Lavorel | Alex Abdelnour | Lutfor Rahman | Grazia Zulian |
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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 | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | abdelnouralex@gmail.com | lutfor.rahman@northampton.ac.uk | grazia.zulian@jrc.ec.europa.e |
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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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: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents " |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | Continential Scale |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | N.A. |
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | Method Only |
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New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised 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-379 | EM-837 | EM-939 |
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Document ID for related EM
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Doc-260 | Doc-269 | Doc-13 | Doc-317 | Doc-406 | None |
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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-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-836 | EM-941 |
EM Modeling Approach
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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EM Temporal Extent
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2007-2008 | 1969-2008 | Not applicable | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | Not applicable |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Not applicable |
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Not applicable | No location (no locational reference given) |
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Spatial Extent Name
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Central French Alps | H. J. Andrews LTER WS10 | Not applicable | Not applicable |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 ha | Not applicable | >1,000,000 km^2 |
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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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) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some 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 | volume, for 3-D feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
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20 m x 20 m | 30 m x 30 m surface pixel and 2-m depth soil column | multiple unrelated sites | Pixel size |
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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EM Computational Approach
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Analytic | Numeric | 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-379 | EM-837 | EM-939 |
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Model Calibration Reported?
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No | No | Not applicable | No |
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Model Goodness of Fit Reported?
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Yes | No | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | No | Not applicable | Unclear |
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Model Uncertainty Analysis Reported?
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No | No | Not applicable | No |
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Model Sensitivity Analysis Reported?
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No | No | Not applicable | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-79 | EM-379 | EM-837 | EM-939 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-79 | EM-379 | EM-837 | EM-939 |
| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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Centroid Latitude
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45.05 | 44.25 | Not applicable | Not applicable |
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Centroid Longitude
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6.4 | -122.33 | Not applicable | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | Not applicable | Not applicable |
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Centroid Coordinates Status
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Provided | Provided | Not applicable | Not applicable |
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Forests | Created Greenspace | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | restored landfills and conserved grasslands | Not applicable |
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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 finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-79 | EM-379 | EM-837 | EM-939 |
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EM Organismal Scale
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Community | Not applicable | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-79 | EM-379 | EM-837 | EM-939 |
| None Available | None Available |
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None Available |
EnviroAtlas URL
| EM-79 | EM-379 | EM-837 | EM-939 |
| None Available | Average Annual Precipitation | None Available | Dasymetric Allocation of Population, Ecosystem Markets: Imperiled Species and Habitats, Waterbody area |
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-379 | EM-837 | EM-939 |
| None | 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-379 | EM-837 | EM-939 |
| None | None | None | None |
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