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-760 ![]() |
EM-887 | EM-985 |
EM Short Name
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WESP: Marsh & wet meadow, ID, USA | VELMA v. 2.0 disturbance | Atlantis ecosystem assessment submodel |
EM Full Name
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WESP: Seasonally flooded marsh & wet meadow, Idaho, USA | VELMA (Visualizing Ecosystems for Land Management Assessment) version 2.0 disturbance | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
EM Source or Collection
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None | US EPA | None |
EM Source Document ID
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393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
366 | 463 |
Document Author
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Murphy, C. and T. Weekley | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
Document Year
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2012 | 2014 | 2011 |
Document Title
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Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | VELMA Version 2.0 User Manual and Technical Documentation | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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 report | Published report | Published journal manuscript |
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
Contact Name
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Chris Murphy | Robert B. McKane | Elizabeth Fulton |
Contact Address
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Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | U.S. EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia |
Contact Email
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chris.murphy@idfg.idaho.gov | mckane.bob@epa.gov | beth.fulton@csiro.au |
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
Summary Description
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A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | VELMA – Visualizing Ecosystems for Land Management Assessments - is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas. AUTHOR'S DESCRIPTION: "Understanding how disturbances such as harvest, fire and fertilization affect ecosystem services has been a major motivation in the development of VELMA. For example, how do disturbances such as forest harvest or the application of agronomic fertilizers affect hydrological and biogeochemical processes controlling water quality and quantity, carbon sequestration, production of greenhouse gases, etc.? Abdelnour et al. (2011, 2013) have already demonstrated the use of VELMA v1.0 to simulate the effects of forest clearcutting on ecohydrological processes that regulate a variety of ecosystem services. With the addition of a tissue-specific plant biomass (LSR) simulator and an enhanced GUI, VELMA v2.0 significantly expands the detail, flexibility, and ease of use for simulating disturbance effects. Currently available disturbance models include: - BurnDisturbanceModel, effects of fire. - GrazeDisturbanceModel, effects of grazing. - FertilizeLsrDisturbanceModel, effects of fertilizer applications. - HarvestLsrDisturbanceModel, effects of biomass harvest. Each of these disturbance models specifies where and when a disturbance event will occur. The Burn, Graze and Harvest models have options for specifying how much of each plant tissue and detritus pool (leaves, stems, roots) will be removed and where it goes (offsite and/or to a specified onsite C and N pools). The Fertilize model has options for applying nitrogen as ammonium, nitrate, urea and/or manure." | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
Biophysical Context
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restored, enhanced and created wetlands | No additional description provided | N/A |
EM Scenario Drivers
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Sites, function or habitat focus | No scenarios presented | No scenarios presented |
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only |
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-760 ![]() |
EM-887 | EM-985 |
Document ID for related EM
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Doc-390 | Doc-13 | Doc-317 | Doc-366 | Doc-359 | Doc-456 | Doc-459 | Doc-461 |
EM ID for related EM
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EM-718 | EM-734 | EM-743 | EM-883 | EM-884 | EM-375 | EM-379 | EM-380 | EM-605 | EM-892 | EM-978 | EM-981 | EM-983 | EM-990 | EM-991 |
EM Modeling Approach
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
EM Temporal Extent
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2010-2012 | Not applicable | Not applicable |
EM Time Dependence
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time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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past time | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable |
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
Bounding Type
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Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Not applicable |
Spatial Extent Name
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Wetlands in idaho | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | Not applicable | Not applicable |
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable |
Spatial Grain Type
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Not applicable | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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Not applicable | user defined | Not applicable |
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
EM Computational Approach
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Numeric | Numeric | 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-760 ![]() |
EM-887 | EM-985 |
Model Calibration Reported?
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No | Not applicable | Not applicable |
Model Goodness of Fit Reported?
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No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
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No | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
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No | Not applicable | Not applicable |
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-760 ![]() |
EM-887 | EM-985 |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-760 ![]() |
EM-887 | EM-985 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
Centroid Latitude
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44.06 | Not applicable | Not applicable |
Centroid Longitude
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-114.69 | Not applicable | Not applicable |
Centroid Datum
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WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
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Estimated | Not applicable | Not applicable |
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
EM Environmental Sub-Class
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Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | 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
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created, restored and enhanced wetlands | Terrestrial environment sub-classes | Multiple |
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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-760 ![]() |
EM-887 | EM-985 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-760 ![]() |
EM-887 | EM-985 |
None Available | 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-760 ![]() |
EM-887 | EM-985 |
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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-760 ![]() |
EM-887 | EM-985 |
None | None | None |