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-684 |
EM-719 |
EM-964 | EM-982 |
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EM Short Name
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Beach visitation, Barnstable, MA, USA | Seed mix for native plant establishment, IA, USA | EcoSim II - method | Specific conductivity, USA |
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EM Full Name
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Beach visitation, Barnstable, Massachusetts, USA | Cost-effective seed mix design for native plant establishment, Iowa, USA | EcoSim II - method | Specific Conductivity, USA |
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EM Source or Collection
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US EPA | None | None | US EPA |
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EM Source Document ID
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386 | 394 | 448 | 460 |
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Document Author
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Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Meissen, J. | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Olson, J.R., and S.M. Cormier |
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Document Year
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2018 | 2018 | 2000 | 2019 |
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Document Title
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Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Cost-effective seed mix design and first-year management | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity |
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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 report | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
| Not applicable | Not applicable | https://ecopath.org/downloads/ | (https://edg.epa.gov/ metadata/catalog/main/home.page) | |
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Contact Name
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Kate K, Mulvaney | Justin Meissen | Carl Walters | John Olson |
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Contact Address
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Not reported | Tallgrass Prairie Center, University of Northern Iowa | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | California State Univ. Monterey Bay, 100 Campus Center, Seaside CA 93955 |
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Contact Email
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Mulvaney.Kate@EPA.gov | Not reported | c.walters@oceans.ubc.ca | joolson@csumb.edu |
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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Summary Description
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ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " | We developed a random forest model that predicts natural background specific conductivity (SC), a measure of total dissolved ions, for all stream segments in the contiguous United States at monthly time steps between the years 2001 to 2015. Models were trained using 11 796 observations made at 1785 minimally impaired stream segments and validated with observations from an additional 92 segments. Static predictors of SC included geology, soils, and vegetation parameters. Temporal predictors were related to climate and enabled the model to make predictions for different dates. The model explained 95% of the variation in SC among validation observations (mean absolute error = 29 μS/cm, Nash-Sutcliffe efficiency = 0.85). The model performed well across the period of interest but exhibited bias in Coastal Plain and Xeric regions (26 and 30%, respectively). National model predictions showed large spatial variation with the greatest SC predicted to occur in the desert southwest and plains. Model predictions also reflected changes at individual streams during drought. |
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Specific Policy or Decision Context Cited
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To assess the number of people who would be impacted by beach closures. | Seed mix design and management practices for native plant restoration | None | N/A |
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Biophysical Context
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Four separate beaches within the community of Barnstable | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. | None, Ocean ecosystems | Stream segment taken from StreamCat database |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A | N/A |
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | 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-684 |
EM-719 |
EM-964 | EM-982 |
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Document ID for related EM
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Doc-386 | Doc-387 | Doc-395 | None | None |
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EM ID for related EM
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EM-682 | EM-685 | EM-683 | EM-686 | EM-728 | EM-1055 | None |
EM Modeling Approach
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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EM Temporal Extent
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2011 - 2016 | 2015-2017 | Not applicable | 2001-2015 |
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EM Time Dependence
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time-dependent | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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past time | Not applicable | both | past time |
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EM Time Continuity
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discrete | discrete |
discrete ?Comment:Modeller dependent |
discrete |
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EM Temporal Grain Size Value
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1 | 1 | 1 | 3 |
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EM Temporal Grain Size Unit
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Day | Year | Day | Month |
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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Bounding Type
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Physiographic or ecological | Other | Other | Geopolitical |
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Spatial Extent Name
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Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Iowa State University Northeast Research and Demonstration Farm | Not applicable | Contiguous United States |
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Spatial Extent Area (Magnitude)
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10-100 ha | <1 ha | Not applicable | >1,000,000 km^2 |
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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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 lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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by beach site | 20 ft x 28 ft | Not applicable | 3.1 km2 |
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | stochastic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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Model Calibration Reported?
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Yes | Not applicable | No | Yes |
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Model Goodness of Fit Reported?
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No | Not applicable | No | Yes |
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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 | Yes |
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Model Uncertainty Analysis Reported?
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No | Not applicable | Not applicable | No |
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Model Sensitivity Analysis Reported?
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Yes | Not applicable | Not applicable | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Yes |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-684 |
EM-719 |
EM-964 | EM-982 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-684 |
EM-719 |
EM-964 | EM-982 |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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Centroid Latitude
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41.64 | 42.93 | Not applicable | 39.83 |
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Centroid Longitude
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-70.29 | -92.57 | Not applicable | 98.58 |
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Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
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Estimated | Provided | Not applicable | Estimated |
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EM ID
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EM-684 |
EM-719 |
EM-964 | EM-982 |
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EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Agroecosystems | Grasslands | Open Ocean and Seas | Rivers and Streams |
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Specific Environment Type
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Saltwater beach | Research farm in historic grassland | Pelagic | Stream segment |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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-684 |
EM-719 |
EM-964 | EM-982 |
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EM Organismal Scale
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Not applicable | Community |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-684 |
EM-719 |
EM-964 | EM-982 |
| None Available | None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
| EM-684 |
EM-719 |
EM-964 | EM-982 |
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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-684 |
EM-719 |
EM-964 | EM-982 |
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