EcoService Models Library (ESML)
Home
Search EMs
My
EMs 
Learn about
ESML
View ESML Data Maploading
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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788 
|
EM-983 |
|
EM Short Name
em.detail.shortNameHelp
?
|
Chinook salmon value, Yaquina Bay, OR | N removal by wetland restoration, Midwest, USA | Wild bees over 26 yrs of restored prairie, IL, USA | Atlantis ecosystem physics submodel |
|
EM Full Name
em.detail.fullNameHelp
?
|
Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Atlantis user's guide part I: general overview, physics & ecology |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | None | None | None |
|
EM Source Document ID
|
324 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
401 | 461 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Audzijonyte, A., Gorton, R., Kaplan, I., & Fulton, E. A. |
|
Document Year
em.detail.documentYearHelp
?
|
2012 | 2006 | 2017 | 2017 |
|
Document Title
em.detail.sourceIdHelp
?
|
Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Atlantis user’s guide part I: general overview, physics & ecology |
|
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published report | Published journal manuscript | Published report |
Software and Access
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
| Not applicable | Not applicable | Not applicable | https://research.csiro.au/atlantis/home/links/ | |
|
Contact Name
em.detail.contactNameHelp
?
|
Stephen Jordan | William G. Crumpton | Sean R. Griffin | Asta Audzijonyte |
|
Contact Address
|
U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | University of Tasmania (Australia); Nature Research Centre (Lithuania) |
|
Contact Email
|
jordan.steve@epa.gov | crumpton@iastate.edu | srgriffin108@gmail.com | Asta.Audzijonyte@utas.edu.au |
EM Description
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
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: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | 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. |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None reported | None identified | None identified | None identified |
|
Biophysical Context
|
Yaquina Bay estuary | No additional description provided | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | Marine and coastal ecosystems |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
N/A | More conservative, average and less conservative nitrate loss rate | No scenarios presented | No scenarios presented |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
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
em.detail.relatedEmHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
None | None | None | Doc-456 | Doc-459 |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-604 | EM-397 | None | None | EM-981 | EM-978 | EM-985 | EM-990 | EM-991 |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2003-2008 | 1973-1999 | 1988-2014 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-dependent | time-stationary | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | future time | Not applicable | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | discrete | Not applicable | continuous |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | 1 | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Day | Not applicable | Not applicable |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Pacific Northwest | Upper Mississippi River and Ohio River basins | Nachusa Grasslands | Not applicable |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
>1,000,000 km^2 | >1,000,000 km^2 | 10-100 km^2 | Not applicable |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
Not applicable | 1 km2 | Area varies by site | Not applicable |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Numeric | Analytic | Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
Model Checking Procedures Used
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | No | Not applicable |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | No | Not applicable |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None | None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Yes ?Comment:Compared to a second methodological approach |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | Not applicable |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | Not applicable |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | Not applicable |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
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])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-603 | EM-627 |
EM-788
|
EM-983 |
|
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-603 | EM-627 |
EM-788
|
EM-983 |
|
None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
44.62 | 40.6 | 41.89 | Not applicable |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
-124.02 | -88.4 | -89.34 | Not applicable |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | Not applicable |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Provided | Not applicable |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Agroecosystems | 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 |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Yaquina Bay | Agroecosystems and associated drainage and wetlands | Restored prairie, prairie remnants, and cropland | Multiple |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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 | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-603 | EM-627 |
EM-788
|
EM-983 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Individual or population, within a species | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-603 | EM-627 |
EM-788
|
EM-983 |
|
None Available |
|
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)
em.detail.cicesHelp
?
| EM-603 | EM-627 |
EM-788
|
EM-983 |
| None |
|
|
|
<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>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-603 | EM-627 |
EM-788
|
EM-983 |
| None |
|
None |
|