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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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EM Short Name
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Land capability classification | Chinook salmon value, Yaquina Bay, OR | Total duck recruits, CREP wetlands, Iowa, USA | Atlantis ecosystem physics submodel |
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EM Full Name
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Land capability classification | Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | Total duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Atlantis user's guide part I: general overview, physics & ecology |
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EM Source or Collection
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None | US EPA | None | None |
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EM Source Document ID
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340 | 324 |
372 ?Comment:Document 373 is a secondary source for this EM. |
461 |
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Document Author
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United States Department of Agriculture - Natural Resources Conservation Service | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | 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 | Audzijonyte, A., Gorton, R., Kaplan, I., & Fulton, E. A. |
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Document Year
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2013 | 2012 | 2010 | 2017 |
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Document Title
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National Soil Survey Handbook - Part 622 - Interpretative Groups | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Atlantis user’s guide part I: general overview, physics & ecology |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
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Comments on Status
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Published report | Published journal manuscript | Published report | Published report |
Software and Access
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
| Not applicable | Not applicable | Not applicable | https://research.csiro.au/atlantis/home/links/ | |
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Contact Name
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United States Department of Agriculture | Stephen Jordan | David Otis | Asta Audzijonyte |
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Contact Address
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Not reported | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | University of Tasmania (Australia); Nature Research Centre (Lithuania) |
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Contact Email
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http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | jordan.steve@epa.gov | dotis@iastate.edu | Asta.Audzijonyte@utas.edu.au |
EM Description
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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Summary Description
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AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: "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…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | Before delving into Atlantis we would like to provide a little bit of background on the modelling framework and this manual. Atlantis is just one of many marine ecosystem models, originally known as BM2 (BoxModel 2) it was christened Atlantis by Villy Christensen in South Africa in 2001. Marine ecosystem models have existed for more than 50 years, though they have only grown in popular use since the advent of (fast) modern computing power. They have grown from a biophysical focus to include more and more of the human dimensions. This is reflected in the structure of this manual, which sequentially works through the physical then biological before getting into the human dimensions. Atlantis was originally developed with an eye to temperate marine ecosystems and fisheries, though it has grown through time. |
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Specific Policy or Decision Context Cited
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None provided | None reported | None identified | None identified |
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Biophysical Context
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No additional description provided | Yaquina Bay estuary | Prairie Pothole Region of Iowa | Marine and coastal ecosystems |
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EM Scenario Drivers
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No scenarios presented | N/A | No scenarios presented | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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Method Only, Application of Method or Model Run
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Method Only | Method + Application | Method + Application | Method Only |
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New or Pre-existing EM?
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New or revised model | New or revised model | 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
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EM-434 | EM-603 | EM-705 | EM-983 |
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Document ID for related EM
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None | None | Doc-372 | Doc-373 | Doc-456 | Doc-459 |
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EM ID for related EM
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None | EM-604 | EM-397 | EM-632 | EM-700 | EM-701 | EM-702 | EM-703 | EM-704 | EM-981 | EM-978 | EM-985 | EM-990 | EM-991 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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EM Temporal Extent
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Not applicable | 2003-2008 | 1987-2007 | Not applicable |
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EM Time Dependence
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Not applicable | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | continuous |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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Bounding Type
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Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Not applicable |
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Spatial Extent Name
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Not applicable | Pacific Northwest | CREP (Conservation Reserve Enhancement Program | Not applicable |
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Spatial Extent Area (Magnitude)
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Not applicable | >1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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EM Spatial Distribution
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Not applicable | spatially lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable |
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Spatial Grain Type
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Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
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Not applicable | Not applicable | multiple, individual, irregular sites | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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EM Computational Approach
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Not applicable | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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Model Calibration Reported?
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Not applicable | No | Unclear | Not applicable |
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Model Goodness of Fit Reported?
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Not applicable | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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No |
Yes ?Comment:Compared to a second methodological approach |
No | Not applicable |
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Model Uncertainty Analysis Reported?
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Not applicable | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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Not applicable | No | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-434 | EM-603 | EM-705 | EM-983 |
| None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-434 | EM-603 | EM-705 | EM-983 |
| None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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Centroid Latitude
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Not applicable | 44.62 | 42.62 | Not applicable |
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Centroid Longitude
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Not applicable | -124.02 | -93.84 | Not applicable |
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Centroid Datum
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Not applicable | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Not applicable | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
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Specific Environment Type
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None identified | Yaquina Bay | Wetlands buffered by grassland within agroecosystems | Multiple |
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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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-434 | EM-603 | EM-705 | EM-983 |
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EM Organismal Scale
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Not applicable | Individual or population, within a species | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-434 | EM-603 | EM-705 | EM-983 |
| None Available |
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None Available |
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)
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| EM-434 | EM-603 | EM-705 | EM-983 |
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None |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-434 | EM-603 | EM-705 | EM-983 |
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None |
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