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-12 |
EM-137 |
EM-380 |
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EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | i-Tree Hydro v4.0 | VELMA plant-soil, Oregon, USA |
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EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | i-Tree Hydro v4.0 (default data option) | VELMA (Visualizing Ecosystems for Land Management Assessments) plant-soil, Oregon, USA |
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EM Source or Collection
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Envision | i-Tree | USDA Forest Service | US EPA |
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EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
198 | 317 |
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Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | USDA Forest Service | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. |
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Document Year
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2008 | Not Reported | 2013 |
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Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | i-Tree Hydro User's Manual v. 4.0 | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment |
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Document Status
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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 | Webpage | Published journal manuscript |
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EM ID
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EM-12 |
EM-137 |
EM-380 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
http://www.itreetools.org | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | |
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Contact Name
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Michael R. Guzy | Not applicable | Alex Abdelnour |
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Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Not applicable | Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA |
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Contact Email
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Not reported | Not applicable | abdelnouralex@gmail.com |
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality." | 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 plant-soil model (Figure (A3)) that simulates ecosystem carbon storage and the cycling of C and N between a plant biomass layer and the active soil pools. Specifically, the plant-soil model simulates the interaction among aboveground plant biomass, soil organic carbon (SOC), soil nitrogen including dissolved nitrate (NO3), ammonium (NH4), and organic nitrogen, as well as DOC (equations (A7)–(A12)). Daily atmospheric inputs of wet and dry nitrogen deposition are accounted for in the ammonium pool of the shallow soil layer (equation (A13)). Uptake of ammonium and nitrate by plants is modeled using a Type II Michaelis-Menten function (equation (A14)). Loss of plant biomass is simulated through a density-dependent mortality. The mortality rate and the nitrogen uptake rate mimic the exponential increase in biomass mortality and the accelerated growth rate, respectively, as plants go through succession and reach equilibrium (equations (A14)–(A18)). Vertical transport of nutrients from one layer to another in a soil column is a function of water drainage (equations (A19)–(A22)). Decomposition of SOC follows first-order kinetics controlled by soil temperature and moisture content as described in the terrestrial ecosystem model (TEM) of Raich et al. [1991] (equations (A23)–(A26)). Nitrification (equations (A27)–(A30)) and denitrification (equations (A31)–(A34)) were simulated using the equations from the generalized model of N2 and N2O production of Parton et al. [1996, 2001] and Del Grosso et al. [2000]. [12] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water and nutrients (Figure (A1)). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow i |
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Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified |
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Biophysical Context
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No additional description provided | No additional description provided | 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. |
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EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | Forest management (harvest/no harvest) |
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application (multiple runs exist) View EM Runs |
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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 |
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-12 |
EM-137 |
EM-380 |
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Document ID for related EM
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Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-190 | Doc-223 | Doc-13 | Doc-317 |
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EM ID for related EM
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EM-333 | EM-369 | EM-109 | EM-142 | EM-51 | EM-375 | EM-379 | EM-884 | EM-883 | EM-887 |
EM Modeling Approach
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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EM Temporal Extent
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1990-2050 | Not applicable | 1969-2008 |
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EM Time Dependence
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time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | future time |
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EM Time Continuity
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discrete | discrete | discrete |
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EM Temporal Grain Size Value
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2 | 1 | 1 |
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EM Temporal Grain Size Unit
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Year | Hour | Day |
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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Bounding Type
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Geopolitical | Not applicable | Watershed/Catchment/HUC |
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Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Not applicable | H. J. Andrews LTER WS10 |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | Not applicable | 10-100 ha |
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
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varies | 30 x 30 m | 30 m x 30 m surface pixel and 2-m depth soil column |
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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EM Computational Approach
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Numeric | Numeric | Numeric |
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EM Determinism
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stochastic | deterministic | deterministic |
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Statistical Estimation of EM
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Comment:Agent based modeling results in response indices. |
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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Model Calibration Reported?
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Unclear | Not applicable | Yes |
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Model Goodness of Fit Reported?
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No | Not applicable | No |
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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 | Not applicable | No |
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Model Uncertainty Analysis Reported?
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No | Not applicable | No |
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Model Sensitivity Analysis Reported?
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No ?Comment:Sensitivity analysis performed for agent values only. |
Not applicable | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-12 |
EM-137 |
EM-380 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-12 |
EM-137 |
EM-380 |
| None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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Centroid Latitude
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44.11 | -9999 | 44.25 |
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Centroid Longitude
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-123.09 | -9999 | -122.33 |
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Centroid Datum
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WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
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Estimated | Not applicable | Provided |
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Ground Water | Created Greenspace | Rivers and Streams | Ground Water | Forests |
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Specific Environment Type
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Agricultural-urban interface at river junction | Urban watersheds | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). |
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EM Ecological Scale
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Ecological scale is finer 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 |
Scale of differentiation of organisms modeled
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EM ID
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EM-12 |
EM-137 |
EM-380 |
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EM Organismal Scale
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Not applicable | Community | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-12 |
EM-137 |
EM-380 |
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None Available | 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)
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EM-12 |
EM-137 |
EM-380 |
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None |
<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)
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EM-12 |
EM-137 |
EM-380 |
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None |
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