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-91 | EM-1004 |
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EM Short Name
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EnviroAtlas-Air pollutant removal | RHyME2, Upper Mississippi River basin, USA | GI toolkit users guide |
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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 | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Green Infrastructure valuation toolkit users guide |
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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 |
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EM Source Document ID
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223 | 123 | 474 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Genecon LLP. |
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Document Year
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2013 | 2013 | 2010 |
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Document Title
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EnviroAtlas - Featured Community | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Building natural value for sustainable economic development The green infrastructure valuation toolkit user guide |
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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 on US EPA EnviroAtlas website | Published journal manuscript | Published report |
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EM ID
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EM-59 |
EM-91 | EM-1004 |
| https://www.epa.gov/enviroatlas | Not applicable | https://www.merseyforest.org.uk/services/gi-val/ | |
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Contact Name
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EnviroAtlas Team | Liem Tran | The Mercey Forest |
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Contact Address
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Not reported | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | Moss Ln, Woolston, Warrington WA3 6QX, United Kingdom |
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Contact Email
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enviroatlas@epa.gov | ltran1@utk.edu | mail@merseyforest.org.uk |
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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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: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | [The toolkit provides a very helpful introduction to the evidence demonstrating the benefits of green infrastructure interventions. It offers a structured argument that speaks the language of regeneration and economic developments. The 11 economic benefits structure provides a relatively simple high level means of presenting and communicating the benefits of green infrastructure projects in economic contexts, although it also brings some risks of double-counting (see Limitations below). The toolkit provides a structured approach to value green infrastructure benefits in monetary, quantitative and qualitative terms, with equal weight being applied to each of these three ways to present existing evidence. It can add value to and inform the decision-making process, particularly when used at an early stage to get broad brush figures and weigh pros and cons.The toolkit relies on current state-of-the-art evidence and valuation techniques for green infrastructure benefits. However, the toolkit also highlights the need for considerable improvement and expansion of the evidence base to enable future iterations to provide improved valuations. The toolkit helps make green infrastructure benefits ‘visible’ to potential funders. The inclusion of environmental benefits in cost benefit analysis is currently very difficult, often requiring professional assistance. Such assistance is frequently beyond the means of many groups seeking project funding. The toolkit is aimed at filling this gap, providing a means of scoping out the indicative benefits of green infrastructure using tools and approaches accessible to many projects and groups. However, whilst the toolkit provides a means of undertaking a broad Value for Money assessment, it must but emphasised that this is only indicative and cannot replace more rigorous formal project appraisal techniques.] |
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Specific Policy or Decision Context Cited
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None identified | Not reported | None |
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Biophysical Context
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No additional description provided | No additional description provided | N/A |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A |
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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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 | Method Only |
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New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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Document ID for related EM
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Doc-345 | Doc-123 | None |
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EM ID for related EM
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None | None | None |
EM Modeling Approach
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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EM Temporal Extent
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2008-2010 | 1987-1997 | Not applicable |
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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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future time | Not applicable | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Hour | Not applicable | Not applicable |
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Not applicable |
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Spatial Extent Name
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Durham NC and vicinity | Upper Mississippi River basin; St. Croix River Watershed | Not applicable |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100,000-1,000,000 km^2 | Not applicable |
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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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 distributed (in at least some cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | NHDplus v1 | area, for pixel or radial feature |
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Spatial Grain Size
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irregular | NHDplus v1 | Not reported |
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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EM Computational Approach
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Numeric | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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Model Calibration Reported?
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Unclear | Yes | Not applicable |
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Model Goodness of Fit Reported?
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No | Yes | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None |
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Model Operational Validation Reported?
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No | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
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])
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EM-59 |
EM-91 | EM-1004 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-59 |
EM-91 | EM-1004 |
| None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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Centroid Latitude
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35.99 | 42.5 | Not applicable |
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Centroid Longitude
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-78.96 | -90.63 | Not applicable |
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Centroid Datum
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None provided | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Not applicable |
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EM ID
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EM-59 |
EM-91 | EM-1004 |
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EM Environmental Sub-Class
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Created Greenspace | Atmosphere | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Not applicable |
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Specific Environment Type
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Urban and vicinity | None | Multiple |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecosystem | 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-59 |
EM-91 | EM-1004 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-59 |
EM-91 | EM-1004 |
| 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)
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EM-59 |
EM-91 | EM-1004 |
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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-59 |
EM-91 | EM-1004 |
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None | None |
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