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-416 | EM-650 |
EM Short Name
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Sed. denitrification, St. Louis River, MN/WI, USA | Sedge Wren density, CREP, Iowa, USA |
EM Full Name
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Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA |
EM Source or Collection
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US EPA | None |
EM Source Document ID
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333 | 372 |
Document Author
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Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever |
Document Year
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2014 | 2010 |
Document Title
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Sediment nitrification and denitrification in a Lake Superior estuary | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published report |
EM ID
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EM-416 | EM-650 |
Not applicable | Not applicable | |
Contact Name
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Brent J. Bellinger | David Otis |
Contact Address
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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 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University |
Contact Email
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bellinger.brent@epa.ogv | dotis@iastate.edu |
EM ID
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EM-416 | EM-650 |
Summary Description
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ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ?Comment:BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model. |
ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) |
Specific Policy or Decision Context Cited
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None identified | None identified |
Biophysical Context
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Estuarine system | Prairie pothole region of north-central Iowa |
EM Scenario Drivers
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No scenarios presented | No scenarios presented |
EM ID
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EM-416 | EM-650 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-416 | EM-650 |
Document ID for related EM
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None | Doc-372 |
EM ID for related EM
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None | EM-652 | EM-651 | EM-649 | EM-648 |
EM Modeling Approach
EM ID
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EM-416 | EM-650 |
EM Temporal Extent
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2011 - 2012 | 1992-2007 |
EM Time Dependence
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time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable |
EM ID
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EM-416 | EM-650 |
Bounding Type
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Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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St. Louis River estuary | CREP (Conservation Reserve Enhancement Program) wetland sites |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 1-10 km^2 |
EM ID
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EM-416 | EM-650 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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Not applicable | multiple, individual, irregular shaped sites |
EM ID
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EM-416 | EM-650 |
EM Computational Approach
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Analytic | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-416 | EM-650 |
Model Calibration Reported?
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No | Unclear |
Model Goodness of Fit Reported?
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No | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Unclear |
Model Uncertainty Analysis Reported?
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No | No |
Model Sensitivity Analysis Reported?
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No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-416 | EM-650 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-416 | EM-650 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-416 | EM-650 |
Centroid Latitude
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46.75 | 42.62 |
Centroid Longitude
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-92.08 | -93.84 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated |
EM ID
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EM-416 | EM-650 |
EM Environmental Sub-Class
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Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Agroecosystems | Grasslands |
Specific Environment Type
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Freshwater estuary | Grassland buffering inland wetlands set in agricultural land |
EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-416 | EM-650 |
EM Organismal Scale
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Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-416 | EM-650 |
None Available |
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EnviroAtlas URL
EM-416 | EM-650 |
Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition | GAP Ecological Systems |
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-416 | EM-650 |
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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-416 | EM-650 |
None |
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