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
em.detail.idHelp
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EM-93 |
EM-660 |
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EM Short Name
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Stream nitrogen removal, Mississippi R. basin, USA | RUM: Valuing fishing quality, Michigan, USA |
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EM Full Name
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Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA |
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EM Source or Collection
em.detail.emSourceOrCollectionHelp
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US EPA | None |
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EM Source Document ID
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52 |
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. |
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Document Author
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Hill, B. and Bolgrien, D. | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson |
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Document Year
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2011 | 2014 |
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Document Title
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Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Valuing recreational fishing quality at rivers and streams |
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Document Status
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Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript |
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
| Not applicable | Not applicable | |
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Contact Name
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Brian Hill | Richard Melstrom |
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Contact Address
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Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA |
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Contact Email
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hill.brian@epa.gov | melstrom@okstate.edu |
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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Summary Description
em.detail.summaryDescriptionHelp
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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 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. " |
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Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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Not applicable | None identified |
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Biophysical Context
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Agricultural landuse , 1st-10th order streams | stream and river reaches of Michigan |
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EM Scenario Drivers
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Not applicable | targeted sport fish biomass |
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EM ID
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EM-93 |
EM-660 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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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
em.detail.idHelp
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EM-93 |
EM-660 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-154 | Doc-155 | None |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None |
EM Modeling Approach
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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EM Temporal Extent
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2000-2008 | 2008-2010 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable |
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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Bounding Type
em.detail.boundingTypeHelp
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Watershed/Catchment/HUC | Watershed/Catchment/HUC |
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Spatial Extent Name
em.detail.extentNameHelp
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Upper Mississippi, Ohio and Missouri River sub-basins | HUCS in Michigan |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 100,000-1,000,000 km^2 |
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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1 km | reach in HUC |
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | No |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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Yes | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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Unclear | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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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-93 |
EM-660 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-93 |
EM-660 |
| None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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Centroid Latitude
em.detail.ddLatHelp
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36.98 | 45.12 |
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Centroid Longitude
em.detail.ddLongHelp
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-89.13 | 85.18 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated |
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Rivers and Streams |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Not applicable | stream reaches |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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
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EM ID
em.detail.idHelp
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EM-93 |
EM-660 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-93 |
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-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-660 |
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