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-93 | EM-650 |
EM-660 ![]() |
EM Short Name
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Stream nitrogen removal, Mississippi R. basin, USA | Sedge Wren density, CREP, Iowa, USA | RUM: Valuing fishing quality, Michigan, USA |
EM Full Name
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Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA |
EM Source or Collection
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US EPA | None | None |
EM Source Document ID
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52 | 372 |
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. |
Document Author
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Hill, B. and Bolgrien, D. | 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 | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson |
Document Year
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2011 | 2010 | 2014 |
Document Title
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Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Valuing recreational fishing quality at rivers and streams |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published report | Published journal manuscript |
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
Not applicable | Not applicable | Not applicable | |
Contact Name
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Brian Hill | David Otis | Richard Melstrom |
Contact Address
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Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA |
Contact Email
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hill.brian@epa.gov | dotis@iastate.edu | melstrom@okstate.edu |
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
Summary Description
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ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | 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) | 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. " |
Specific Policy or Decision Context Cited
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Not applicable | None identified | None identified |
Biophysical Context
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Agricultural landuse , 1st-10th order streams | Prairie pothole region of north-central Iowa | stream and river reaches of Michigan |
EM Scenario Drivers
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Not applicable | No scenarios presented | targeted sport fish biomass |
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
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-93 | EM-650 |
EM-660 ![]() |
Document ID for related EM
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Doc-154 | Doc-155 | Doc-372 | None |
EM ID for related EM
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None | EM-652 | EM-651 | EM-649 | EM-648 | None |
EM Modeling Approach
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
EM Temporal Extent
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2000-2008 | 1992-2007 | 2008-2010 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
Bounding Type
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Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC |
Spatial Extent Name
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Upper Mississippi, Ohio and Missouri River sub-basins | CREP (Conservation Reserve Enhancement Program) wetland sites | HUCS in Michigan |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 |
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
EM Spatial Distribution
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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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length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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1 km | multiple, individual, irregular shaped sites | reach in HUC |
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
EM Computational Approach
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Analytic | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
Model Calibration Reported?
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No | Unclear | No |
Model Goodness of Fit Reported?
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No | No | Yes |
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 | Unclear | No |
Model Uncertainty Analysis Reported?
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Yes | No | No |
Model Sensitivity Analysis Reported?
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Unclear | No | No |
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-93 | EM-650 |
EM-660 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-93 | EM-650 |
EM-660 ![]() |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
Centroid Latitude
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36.98 | 42.62 | 45.12 |
Centroid Longitude
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-89.13 | -93.84 | 85.18 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated |
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
EM Environmental Sub-Class
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Rivers and Streams | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams |
Specific Environment Type
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Not applicable | Grassland buffering inland wetlands set in agricultural land | stream reaches |
EM Ecological Scale
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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 of differentiation of organisms modeled
EM ID
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EM-93 | EM-650 |
EM-660 ![]() |
EM Organismal Scale
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Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-93 | EM-650 |
EM-660 ![]() |
None Available |
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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-93 | EM-650 |
EM-660 ![]() |
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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-93 | EM-650 |
EM-660 ![]() |
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