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-712 |
EM-719 |
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EM Short Name
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Beach visitation, Barnstable, MA, USA | ESII Tool method | Seed mix for native plant establishment, IA, USA |
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EM Full Name
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Beach visitation, Barnstable, Massachusetts, USA | ESII (Ecosystem Services Identification & Inventory) Tool method | Cost-effective seed mix design for native plant establishment, Iowa, USA |
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EM Source or Collection
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US EPA | None | None |
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EM Source Document ID
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386 |
391 ?Comment:Website for online user support |
394 |
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Document Author
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Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | EcoMetrix Solutions Group (ESG) | Meissen, J. |
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Document Year
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2018 | 2016 | 2018 |
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Document Title
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Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | ESII Tool | Cost-effective seed mix design and first-year management |
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Document Status
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Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Website | Published report |
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EM ID
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EM-684 | EM-712 |
EM-719 |
| Not applicable | https://www.esiitool.com/ | Not applicable | |
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Contact Name
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Kate K, Mulvaney | Not reported | Justin Meissen |
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Contact Address
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Not reported | Not reported | Tallgrass Prairie Center, University of Northern Iowa |
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Contact Email
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Mulvaney.Kate@EPA.gov | Not reported | Not reported |
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EM ID
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EM-684 | EM-712 |
EM-719 |
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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. | AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance." | 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." |
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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. | None identified | Seed mix design and management practices for native plant restoration |
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Biophysical Context
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Four separate beaches within the community of Barnstable | Not applicable | 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. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented |
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EM ID
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EM-684 | EM-712 |
EM-719 |
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Method Only, Application of Method or Model Run
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Method + Application | 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-684 | EM-712 |
EM-719 |
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Document ID for related EM
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Doc-386 | Doc-387 | None | Doc-395 |
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EM ID for related EM
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EM-682 | EM-685 | EM-683 | EM-686 | EM-713 | EM-728 |
EM Modeling Approach
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EM ID
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EM-684 | EM-712 |
EM-719 |
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EM Temporal Extent
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2011 - 2016 | Not applicable | 2015-2017 |
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EM Time Dependence
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time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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past time | Not applicable | Not applicable |
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EM Time Continuity
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discrete | Not applicable | discrete |
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EM Temporal Grain Size Value
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1 | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Day | Not applicable | Year |
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EM ID
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EM-684 | EM-712 |
EM-719 |
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Bounding Type
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Physiographic or ecological | Not applicable | Other |
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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) | Not applicable | Iowa State University Northeast Research and Demonstration Farm |
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Spatial Extent Area (Magnitude)
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10-100 ha | Not applicable | <1 ha |
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EM ID
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EM-684 | EM-712 |
EM-719 |
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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:map units delineated by user based on project. |
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) | 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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by beach site | map units | 20 ft x 28 ft |
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EM ID
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EM-684 | EM-712 |
EM-719 |
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EM Computational Approach
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Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | stochastic |
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Statistical Estimation of EM
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EM ID
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EM-684 | EM-712 |
EM-719 |
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Model Calibration Reported?
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Yes | Not applicable | Not applicable |
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Model Goodness of Fit Reported?
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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 | Not applicable | No |
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Model Uncertainty Analysis Reported?
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No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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Yes | Not applicable | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-684 | EM-712 |
EM-719 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-684 | EM-712 |
EM-719 |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-684 | EM-712 |
EM-719 |
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Centroid Latitude
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41.64 | Not applicable | 42.93 |
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Centroid Longitude
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-70.29 | Not applicable | -92.57 |
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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-684 | EM-712 |
EM-719 |
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EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands |
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Specific Environment Type
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Saltwater beach | Not applicable | Research farm in historic grassland |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Not applicable | 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-684 | EM-712 |
EM-719 |
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EM Organismal Scale
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Not applicable | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
| EM-684 | EM-712 |
EM-719 |
| None Available | 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)
| EM-684 | EM-712 |
EM-719 |
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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-712 |
EM-719 |
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