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-59 |
EM-208 |
EM-667 |
EM-991 |
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EM Short Name
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EnviroAtlas-Air pollutant removal | FORCLIM v2.9, Santiam watershed, OR, USA | Alewife derived nutrients, Connecticut, USA | Atlantis ecosystem harvest submodel |
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EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA | Alewife derived nutrients in stream food web, Connecticut, USA | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
US EPA | None | None |
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EM Source Document ID
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223 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
384 | 463 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Walters, A. W., R. T. Barnes, and D. M. Post | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
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Document Year
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2013 | 2007 | 2009 | 2011 |
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Document Title
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EnviroAtlas - Featured Community | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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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 on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | https://research.csiro.au/atlantis/home/links/ | |
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Contact Name
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EnviroAtlas Team | Richard T. Busing | Annika W. Walters | Elizabeth Fulton |
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Contact Address
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Not reported | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA | Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. |
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Contact Email
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enviroatlas@epa.gov | rtbusing@aol.com | annika.walters@yale.edu | beth.fulton@csiro.au |
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045. | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified |
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Biophysical Context
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No additional description provided | No additional description provided | Alewife spawning runs typically occur Mid March - May. | NA |
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EM Scenario Drivers
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No scenarios presented | Land Management (3); Climate Change (3) | No scenarios presented | No scenarios presented |
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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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 + Application (multiple runs exist) View EM Runs ?Comment:Runs differentiated by scenario combination. |
Method + Application (multiple runs exist) View EM Runs | Method Only |
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New or Pre-existing EM?
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Application of existing model | Application of existing model | 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-59 |
EM-208 |
EM-667 |
EM-991 |
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Document ID for related EM
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Doc-345 |
Doc-22 | Doc-23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Doc-383 | Doc-456 | Doc-459 | Doc-461 | Doc-463 |
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EM ID for related EM
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None | EM-146 | EM-186 | EM-224 | EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673 | EM-978 | EM-981 | EM-983 | EM-985 | EM-990 |
EM Modeling Approach
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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EM Temporal Extent
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2008-2010 | 1990-2050 | 1979-2009 | Not applicable |
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EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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future time | future time | Not applicable | Not applicable |
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EM Time Continuity
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discrete | discrete | Not applicable | continuous |
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EM Temporal Grain Size Value
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1 | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Hour | Year | Not applicable | Not applicable |
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Not applicable |
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Spatial Extent Name
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Durham NC and vicinity | South Santiam watershed | Bride Brook | Not applicable |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100-1000 km^2 | 1-10 ha | Not applicable |
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | Not applicable |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
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irregular | 0.08 ha | Not applicable | Not applicable |
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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EM Computational Approach
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Numeric | Numeric | Analytic | Analytic |
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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-59 |
EM-208 |
EM-667 |
EM-991 |
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Model Calibration Reported?
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Unclear | No |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
Not applicable |
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Model Goodness of Fit Reported?
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No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | N/A | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
| None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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Centroid Latitude
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35.99 | 44.24 | 41.32 | Not applicable |
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Centroid Longitude
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-78.96 | -122.24 | -72.24 | Not applicable |
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Centroid Datum
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None provided | None provided | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Provided | Provided | Not applicable |
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EM ID
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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EM Environmental Sub-Class
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Created Greenspace | Atmosphere | Forests | Rivers and Streams | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
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Specific Environment Type
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Urban and vicinity | primarily Conifer Forest | Coastal stream | Multiple |
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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 | 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-59 |
EM-208 |
EM-667 |
EM-991 |
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EM Organismal Scale
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Not applicable | Species | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
| None Available |
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None Available |
EnviroAtlas URL
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
| Average Annual Precipitation | GAP Ecological Systems, Average Annual Precipitation | The National Hydrography Dataset (NHD) | Big game hunting recreation demand |
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-59 |
EM-208 |
EM-667 |
EM-991 |
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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)
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EM-59 |
EM-208 |
EM-667 |
EM-991 |
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None |
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None |
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