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-862 |
EM-863 ![]() |
EM Short Name
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Nutrient Tracking Tool (NTT), north central Texas, USA | Recreational fishery index, USA | SLAMM, Tampa Bay, FL, USA |
EM Full Name
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Nutrient Tracking Tool (NTT), Upper North Bosque River watershed, Texas, USA | Recreational fishery index for streams and rivers, USA | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA |
EM Source or Collection
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None | US EPA | None |
EM Source Document ID
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354 | 414 |
415 ?Comment:Secondary sources: Documents 412 and 413. |
Document Author
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Saleh, A., O. Gallego, E. Osei, H. Lal, C. Gross, S. McKinney, and H. Cover | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | Sherwood, E. T. and H. S. Greening |
Document Year
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2011 | 2021 | 2014 |
Document Title
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Nutrient Tracking Tool - a user-friendly tool for calculating nutrient reductions for water quality trading | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats |
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 journal manuscript | Published journal manuscript |
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | |
Contact Name
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Ali Saleh | Gregg Lomnicky | Edward T. Sherwood |
Contact Address
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Texas Institute for Applied Environmental Research-Tarleton State University, Stephenville, TX 76401,USA | 200 SW 35th St., Corvallis, OR, 97333 | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA |
Contact Email
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saleh@tiaer.tarleton.edu | lomnicky.gregg@epa.gov | esherwood@tbep.org |
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
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: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" |
Specific Policy or Decision Context Cited
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None identified | 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). | None | No additional description provided |
EM Scenario Drivers
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Conservation management strategies to reduce phosphorus losses | N/A | Varying sea level rise (baseline - 2m), and two habitat adaption strategies |
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | 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 | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
Document ID for related EM
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Doc-352 | None | Doc-412 | Doc-413 |
EM ID for related EM
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EM-549 | None | EM-857 |
EM Modeling Approach
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
EM Temporal Extent
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1960-2001 | 2013-2014 | 2002-2100 |
EM Time Dependence
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time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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future time | past time | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable |
EM Temporal Grain Size Value
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1 | 1 | Not applicable |
EM Temporal Grain Size Unit
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Day | Year | Not applicable |
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
Bounding Type
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Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC |
Spatial Extent Name
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Upper North Bosque River watershed | United States | Tampa Bay estuary watershed |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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Not applicable | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature |
Spatial Grain Size
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Not applicable | stream reach (site) | 10 x 10 m |
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
EM Computational Approach
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Numeric | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
Model Calibration Reported?
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Yes | No | No |
Model Goodness of Fit Reported?
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No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | 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-584 ![]() |
EM-862 |
EM-863 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-584 ![]() |
EM-862 |
EM-863 ![]() |
None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-584 ![]() |
EM-862 |
EM-863 ![]() |
Centroid Latitude
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32.09 | 36.21 | 27.76 |
Centroid Longitude
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-98.12 | -113.76 | -82.54 |
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-584 ![]() |
EM-862 |
EM-863 ![]() |
EM Environmental Sub-Class
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Agroecosystems | Rivers and Streams | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Rangeland and forage fields for dairy | reach | Esturary and associated urban and terrestrial environment |
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 | 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-862 |
EM-863 ![]() |
EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-584 ![]() |
EM-862 |
EM-863 ![]() |
None Available | None Available | None Available |
EnviroAtlas URL
EM-584 ![]() |
EM-862 |
EM-863 ![]() |
GAP Ecological Systems, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD) | None Available | National Hydrography Dataset Plus (NHD PlusV2) |
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-862 |
EM-863 ![]() |
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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-862 |
EM-863 ![]() |
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None |