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-584 ![]() |
EM-660 ![]() |
EM Short Name
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Nutrient Tracking Tool (NTT), north central Texas, USA | RUM: Valuing fishing quality, Michigan, USA |
EM Full Name
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Nutrient Tracking Tool (NTT), Upper North Bosque River watershed, Texas, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA |
EM Source or Collection
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None | None |
EM Source Document ID
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354 |
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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Saleh, A., O. Gallego, E. Osei, H. Lal, C. Gross, S. McKinney, and H. Cover | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson |
Document Year
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2011 | 2014 |
Document Title
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Nutrient Tracking Tool - a user-friendly tool for calculating nutrient reductions for water quality trading | Valuing recreational fishing quality at rivers and streams |
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 journal manuscript |
EM ID
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EM-584 ![]() |
EM-660 ![]() |
http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | |
Contact Name
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Ali Saleh | Richard Melstrom |
Contact Address
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Texas Institute for Applied Environmental Research-Tarleton State University, Stephenville, TX 76401,USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA |
Contact Email
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saleh@tiaer.tarleton.edu | melstrom@okstate.edu |
EM ID
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EM-584 ![]() |
EM-660 ![]() |
Summary Description
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ABSTRACT: "The Nutrient Tracking Tool (NTrT) is an enhanced version of the Nitrogen Trading Tool, a user-friendly Web-based computer program originally developed by the USDA. The NTrT estimates nutrient (nitrogen and phosphorus) and sediment losses from fields managed under a variety of cropping patterns and management practices through its user-friendly, Web-based linkage to the Agricultural Policy Environmental eXtender (APEX) model. It also accesses the USDA Natural Resources Conservation Service’s Web Soil Survey to utilize their geographic information system interface for field and operation identification and load soil information. The NTrT provides farmers, government officials, and other users with a fast and efficient method of estimating nitrogen and phosphorus credits for water quality trading, as well as other water quality, water quantity, and farm production impacts associated with conservation practices. The information obtained from the tool can help farmers determine the most cost-effective conservation practice alternatives for their individual operations and provide them with more advantageous options in a water quality credit trading program. An application of the NTrT to evaluate conservation practices on fields receiving dairy manure in a north central Texas watershed indicates that phosphorus-based application rates, filter strips, forest buffers, and complete manure export off the farm all result in reduced phosphorus losses from the fields on which those practices were implemented. When compared to a base¬line condition that entailed manure application at the nitrogen agronomic rate of receiving crops, the reductions in total phosphorus losses associated with these practices ranged from 15% (2P Rate scenario) to 76% (forest buffer scenario)." AUTHOR'S DESCRIPTION: "This paper provides a brief overview of the NTrT and presents results of verification and application of the tool on a selected field on a test field in the Upper North Bosque River (UNBR) watershed in Texas…simulations for the baseline and all five alternative scenarios were replicated for each of 90 specific soil types in Erath County, Texas…results reported and discussed in this report represent the averages of the output for all soil types." | 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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None identified | None identified |
Biophysical Context
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The UNBR watershed is comprised primarily of two main physiographic areas, the West Cross Timbers and the Grand Prairie Land Resource Areas. In the West Cross Timbers, soils are primarily fine sandy loams with sandy clay subsoils. Soils in the Grand Prairie area, on the other hand, are typically calcareous clays and clay loams (Ward et al. 1992). | stream and river reaches of Michigan |
EM Scenario Drivers
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Conservation management strategies to reduce phosphorus losses | targeted sport fish biomass |
EM ID
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EM-584 ![]() |
EM-660 ![]() |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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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
EM ID
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EM-584 ![]() |
EM-660 ![]() |
Document ID for related EM
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Doc-352 | None |
EM ID for related EM
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EM-549 | None |
EM Modeling Approach
EM ID
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EM-584 ![]() |
EM-660 ![]() |
EM Temporal Extent
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1960-2001 | 2008-2010 |
EM Time Dependence
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time-dependent | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable |
EM Time Continuity
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discrete | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable |
EM Temporal Grain Size Unit
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Day | Not applicable |
EM ID
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EM-584 ![]() |
EM-660 ![]() |
Bounding Type
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Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
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Upper North Bosque River watershed | HUCS in Michigan |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100,000-1,000,000 km^2 |
EM ID
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EM-584 ![]() |
EM-660 ![]() |
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 | reach in HUC |
EM ID
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EM-584 ![]() |
EM-660 ![]() |
EM Computational Approach
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Numeric | Numeric |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-584 ![]() |
EM-660 ![]() |
Model Calibration Reported?
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Yes | No |
Model Goodness of Fit Reported?
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No | Yes |
Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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No | No |
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-584 ![]() |
EM-660 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-584 ![]() |
EM-660 ![]() |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-584 ![]() |
EM-660 ![]() |
Centroid Latitude
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32.09 | 45.12 |
Centroid Longitude
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-98.12 | 85.18 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated |
EM ID
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EM-584 ![]() |
EM-660 ![]() |
EM Environmental Sub-Class
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Agroecosystems | Rivers and Streams |
Specific Environment Type
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Rangeland and forage fields for dairy | stream reaches |
EM Ecological Scale
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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
EM ID
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EM-584 ![]() |
EM-660 ![]() |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-584 ![]() |
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-584 ![]() |
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-584 ![]() |
EM-660 ![]() |
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