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
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Rate of Fire Spread | RUM: Valuing fishing quality, Michigan, USA | Wild bees over 26 yrs of restored prairie, IL, USA | Recreational fishery index, USA |
EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Rate of Fire Spread | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Recreational fishery index for streams and rivers, USA |
EM Source or Collection
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US EPA | EnviroAtlas | None | None | None | US EPA |
EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
306 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
401 | 414 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Rothermel, Richard C. | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold |
Document Year
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2013 | 1972 | 2014 | 2017 | 2021 |
Document Title
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EnviroAtlas - National | A Mathematical model for predicting fire spread in wildland fuels | Valuing recreational fishing quality at rivers and streams | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. |
Document Status
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Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published USDA Forest Service report | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
https://www.epa.gov/enviroatlas | http://firelab.org/project/farsite | Not applicable | Not applicable | Not applicable | |
Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Charles McHugh | Richard Melstrom | Sean R. Griffin | Gregg Lomnicky |
Contact Address
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Not reported | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | 200 SW 35th St., Corvallis, OR, 97333 |
Contact Email
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enviroatlas@epa.gov | cmchugh@fs.fed.us | melstrom@okstate.edu | srgriffin108@gmail.com | lomnicky.gregg@epa.gov |
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] |
Specific Policy or Decision Context Cited
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None Identified | None identified | None identified | None identified | None identified |
Biophysical Context
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No additional description provided | Not applicable | stream and river reaches of Michigan | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | None |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | targeted sport fish biomass | No scenarios presented | N/A |
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
Method Only, Application of Method or Model Run
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Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application |
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 | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
Document ID for related EM
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
None | None | None | None |
EM ID for related EM
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None | None | None | None | None |
EM Modeling Approach
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
EM Temporal Extent
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2006-2010 | Not applicable | 2008-2010 | 1988-2014 | 2013-2014 |
EM Time Dependence
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time-stationary | Not applicable | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | past time |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Year |
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
Bounding Type
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Geopolitical | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical |
Spatial Extent Name
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counterminous United States | Not applicable | HUCS in Michigan | Nachusa Grasslands | United States |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | Not applicable | 100,000-1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 |
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) |
Spatial Grain Size
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irregular | Not applicable | reach in HUC | Area varies by site | stream reach (site) |
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
EM Computational Approach
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Analytic | Analytic | Numeric | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
Model Calibration Reported?
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No | Not applicable | No | No | No |
Model Goodness of Fit Reported?
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No | Not applicable | Yes | No | No |
Goodness of Fit (metric| value | unit)
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None | None |
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None | None |
Model Operational Validation Reported?
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No | No | No | No | No |
Model Uncertainty Analysis Reported?
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No | Not applicable | No | No | No |
Model Sensitivity Analysis Reported?
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No | Not applicable | No | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
Centroid Latitude
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39.5 | -9999 | 45.12 | 41.89 | 36.21 |
Centroid Longitude
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-98.35 | -9999 | 85.18 | -89.34 | -113.76 |
Centroid Datum
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WGS84 | Not applicable | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Not applicable | Estimated | Provided | Estimated |
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Agroecosystems | Grasslands | Rivers and Streams |
Specific Environment Type
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Terrestrial | Not applicable | stream reaches | Restored prairie, prairie remnants, and cropland | reach |
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 is finer than that of 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
EM ID
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EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Species | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
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-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
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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)
EM-63 | EM-337 |
EM-660 ![]() |
EM-788 ![]() |
EM-862 |
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None |
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None |
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