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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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EM Short Name
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Coral taxa and land development, St.Croix, VI, USA | SAV occurrence, St. Louis River, MN/WI, USA | HWB Blood pressure, Great Lakes waterfront, USA | Velma- 6PPD-Q concentrations, Seattle, WA |
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EM Full Name
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Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Human well being indicator- Blood pressure, Great Lakes waterfront, USA | VELMA: 6PPD-Quinone stormwater concentrations , Seattle, Washington |
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EM Source or Collection
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US EPA | US EPA | None | US EPA |
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EM Source Document ID
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96 | 330 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
465 |
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Document Author
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Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Halama JJ, McKane RB, Barnhart BL, Pettus PP, Brookes AF, Adams AK, Gockel CK, Djang KS, Phan V, Chokshi SM, Graham JJ, Tian Z, Peter KT and Kolodziej,EP |
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Document Year
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2011 | 2013 | None | 2024 |
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Document Title
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Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Watershed analysis of urban stormwater contaminant 6PPD-Quinone hotspots and stream concentrations using a process-based ecohydrological model |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review | Published journal manuscript |
Software and Access
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
| Not applicable | Not applicable | Not applicable | Not reported | |
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Contact Name
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Leah Oliver | Ted R. Angradi | Ted Angradi | Jonathan Halama |
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Contact Address
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National Health and Environmental Research Effects Laboratory | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | U.S. Environmental Protection Agency, Corvallis, OR |
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Contact Email
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leah.oliver@epa.gov | angradi.theodore@epa.gov | tedangradi@gmail.com | Halama.Jonathan@epa.gov |
EM Description
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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Summary Description
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AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | ABSTRACT: "Coho salmon (Oncorhynchus kisutch) are highly sensitive to 6PPD-Quinone (6PPD-Q). Details of the hydrological and biogeochemical processes controlling spatial and temporal dynamics of 6PPD-Q fate and transport from points of deposition to receiving waters (e.g., streams, estuaries) are poorly understood. To understand the fate and transport of 6PPD and mechanisms leading to salmon mortality Visualizing Ecosystem Land Management Assessments (VELMA), an ecohydrological model developed by US Environmental Protection Agency (EPA), was enhanced to better understand and inform stormwater management planning by municipal, state, and federal partners seeking to reduce stormwater contaminant loads in urban streams draining to the Puget Sound National Estuary. This work focuses on the 5.5 km2 Longfellow Creek upper watershed (Seattle, Washington, United States), which has long exhibited high rates of acute urban runoff mortality syndrome in coho salmon. We present VELMA model results to elucidate these processes for the Longfellow Creek watershed across multiple scales–from 5-m grid cells to the entire watershed. Our results highlight hydrological and biogeochemical controls on 6PPD-Q flow paths, and hotspots within the watershed and its stormwater infrastructure, that ultimately impact contaminant transport to Longfellow Creek and Puget Sound. Simulated daily average 6PPD-Q and available observed 6PPD-Q peak in-stream grab sample concentrations (ng/L) corresponds within plus or minus 10 ng/L. Most importantly, VELMA’s high-resolution spatial and temporal analysis of 6PPD-Q hotspots provides a tool for prioritizing the locations, amounts, and types of green infrastructure that can most effectively reduce 6PPD-Q stream concentrations to levels protective of coho salmon and other aquatic species. " |
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Specific Policy or Decision Context Cited
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Not applicable | None identified | None identified | Not reported |
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Biophysical Context
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nearshore; <1.5 km offshore; <12 m depth | submerged aquatic vegetation | Waterfront districts on south Lake Michigan and south lake Erie | 6PPD deposition from vehicle tire wear particles. |
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EM Scenario Drivers
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Not applicable | No scenarios presented | N/A | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | 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 | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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Document ID for related EM
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None | None | Doc-422 | Doc-366 | Doc-423 | Doc-430 |
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EM ID for related EM
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None | None | EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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EM Temporal Extent
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2006-2007 | 2010 - 2012 | 2022 | 9/2020-6/2021 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | past time |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Day |
EM Spatial Extent
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC |
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Spatial Extent Name
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St.Croix, U.S. Virgin Islands | St. Louis River Estuary | Great Lakes waterfront | Longfellow creek |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 1-10 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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EM Spatial Distribution
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spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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Not applicable | area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
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Not applicable | 0.07 m^2 to 0.70 m^2 | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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Model Calibration Reported?
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Yes | Yes | No | Yes |
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Model Goodness of Fit Reported?
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Yes | Yes | No | No |
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Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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No | Yes | No | Yes |
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Model Uncertainty Analysis Reported?
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Yes | No | No | Unclear |
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Model Sensitivity Analysis Reported?
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No | No | Yes | Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-260 | EM-414 | EM-890 | EM-993 |
| None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-260 | EM-414 | EM-890 | EM-993 |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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Centroid Latitude
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17.75 | 46.72 | 42.26 | 47.55 |
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Centroid Longitude
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-64.75 | -96.13 | -87.84 | 122.37 |
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Centroid Datum
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NAD83 | WGS84 | WGS84 | None provided |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams |
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Specific Environment Type
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stony coral reef | Freshwater estuarine system | Lake Michigan & Lake Erie waterfront | small stream |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-260 | EM-414 | EM-890 | EM-993 |
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EM Organismal Scale
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Guild or Assemblage | Not applicable | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
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| EM-260 | EM-414 | EM-890 | EM-993 |
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None Available | None Available |
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EnviroAtlas URL
| EM-260 | EM-414 | EM-890 | EM-993 |
| None Available | Average Annual Precipitation | GAP Ecological Systems, Enabling Conditions | Carbon Storage by Tree Biomass |
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-260 | EM-414 | EM-890 | EM-993 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-260 | EM-414 | EM-890 | EM-993 |
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None | None |
Comment:Model identifies toxicant concentrations relative to the known LC50 for coho juveniles which is 95ng/L (Spromber and Scholz, 2011; |