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-660 ![]() |
EM-863 ![]() |
EM Short Name
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RUM: Valuing fishing quality, Michigan, USA | SLAMM, Tampa Bay, FL, USA |
EM Full Name
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Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA |
EM Source or Collection
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None | None |
EM Source Document ID
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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. |
415 ?Comment:Secondary sources: Documents 412 and 413. |
Document Author
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Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Sherwood, E. T. and H. S. Greening |
Document Year
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2014 | 2014 |
Document Title
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Valuing recreational fishing quality at rivers and streams | 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 |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-660 ![]() |
EM-863 ![]() |
Not applicable | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | |
Contact Name
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Richard Melstrom | Edward T. Sherwood |
Contact Address
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Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA |
Contact Email
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melstrom@okstate.edu | esherwood@tbep.org |
EM ID
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EM-660 ![]() |
EM-863 ![]() |
Summary Description
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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. " | 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 |
Biophysical Context
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stream and river reaches of Michigan | No additional description provided |
EM Scenario Drivers
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targeted sport fish biomass | Varying sea level rise (baseline - 2m), and two habitat adaption strategies |
EM ID
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EM-660 ![]() |
EM-863 ![]() |
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 | 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-660 ![]() |
EM-863 ![]() |
Document ID for related EM
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None | Doc-412 | Doc-413 |
EM ID for related EM
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None | EM-857 |
EM Modeling Approach
EM ID
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EM-660 ![]() |
EM-863 ![]() |
EM Temporal Extent
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2008-2010 | 2002-2100 |
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-660 ![]() |
EM-863 ![]() |
Bounding Type
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Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
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HUCS in Michigan | Tampa Bay estuary watershed |
Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-660 ![]() |
EM-863 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
Spatial Grain Size
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reach in HUC | 10 x 10 m |
EM ID
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EM-660 ![]() |
EM-863 ![]() |
EM Computational Approach
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Numeric | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-660 ![]() |
EM-863 ![]() |
Model Calibration Reported?
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No | No |
Model Goodness of Fit Reported?
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Yes | No |
Goodness of Fit (metric| value | unit)
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None |
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-660 ![]() |
EM-863 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-660 ![]() |
EM-863 ![]() |
None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-660 ![]() |
EM-863 ![]() |
Centroid Latitude
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45.12 | 27.76 |
Centroid Longitude
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85.18 | -82.54 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated |
EM ID
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EM-660 ![]() |
EM-863 ![]() |
EM Environmental Sub-Class
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Rivers and Streams | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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stream reaches | Esturary and associated urban and terrestrial environment |
EM Ecological Scale
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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-660 ![]() |
EM-863 ![]() |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-660 ![]() |
EM-863 ![]() |
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None Available |
EnviroAtlas URL
EM-660 ![]() |
EM-863 ![]() |
The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate | 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-660 ![]() |
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-660 ![]() |
EM-863 ![]() |
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None |