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
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EM ID
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EM-185 | EM-438 | EM-985 |
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EM Short Name
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Blue crabs and SAV, Chesapeake Bay, USA | InVESTv3.0 Nutrient retention, Guánica Bay | Atlantis ecosystem assessment submodel |
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EM Full Name
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Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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EM Source or Collection
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None | US EPA | InVEST | None |
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EM Source Document ID
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292 ?Comment:Conference paper |
338 | 463 |
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Document Author
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Mykoniatis, N. and Ready, R. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | 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. |
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Document Year
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2013 | 2017 | 2011 |
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Document Title
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Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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Document Status
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Not formally documented | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Conference proceedings | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-185 | EM-438 | EM-985 |
| Not applicable | http://www.naturalcapitalproject.org/invest/ | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
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Contact Name
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Nikolaos Mykoniatis | Susan H. Yee | Elizabeth Fulton |
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Contact Address
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Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia |
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Contact Email
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Not reported | yee.susan@epa.gov | beth.fulton@csiro.au |
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EM ID
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EM-185 | EM-438 | EM-985 |
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Summary Description
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ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | 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. |
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Specific Policy or Decision Context Cited
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Not applicable | Improving water quality | None identified |
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Biophysical Context
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Submerged Aquatic Vegetation (SAV), eelgrass | No additional description provided | N/A |
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EM Scenario Drivers
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Essential or Facultative habitat | No scenarios presented | No scenarios presented |
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EM ID
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EM-185 | EM-438 | EM-985 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only |
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New or Pre-existing EM?
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Application of existing model | Application of existing model | Application of existing 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-185 | EM-438 | EM-985 |
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Document ID for related EM
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Doc-227 | Doc-309 | Doc-205 | Doc-456 | Doc-459 | Doc-461 |
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EM ID for related EM
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EM-106 | EM-363 | EM-112 | EM-978 | EM-981 | EM-983 | EM-990 | EM-991 |
EM Modeling Approach
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EM ID
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EM-185 | EM-438 | EM-985 |
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EM Temporal Extent
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1993-2011 | 1980 - 2013 | Not applicable |
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EM Time Dependence
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time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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past time | other or unclear (comment) | Not applicable |
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EM Time Continuity
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discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Year | Year | Not applicable |
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EM ID
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EM-185 | EM-438 | EM-985 |
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Bounding Type
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Physiographic or ecological | Watershed/Catchment/HUC | Not applicable |
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Spatial Extent Name
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Chesapeake Bay | Guanica Bay Study Area | Not applicable |
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Spatial Extent Area (Magnitude)
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10,000-100,000 km^2 | 1000-10,000 km^2. | Not applicable |
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EM ID
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EM-185 | EM-438 | EM-985 |
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EM Spatial Distribution
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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 | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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Not applicable | 30 m x 30 m | Not applicable |
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EM ID
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EM-185 | EM-438 | EM-985 |
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EM Computational Approach
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Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-185 | EM-438 | EM-985 |
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Model Calibration Reported?
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Yes | No | Not applicable |
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Model Goodness of Fit Reported?
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Yes | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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Yes | No | Not applicable |
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Model Uncertainty Analysis Reported?
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Yes | No | Not applicable |
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Model Sensitivity Analysis Reported?
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Yes | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Yes | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-185 | EM-438 | EM-985 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-185 | EM-438 | EM-985 |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-185 | EM-438 | EM-985 |
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Centroid Latitude
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36.99 | 17.97 | Not applicable |
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Centroid Longitude
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-75.95 | -66.93 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Not applicable |
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EM ID
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EM-185 | EM-438 | EM-985 |
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EM Environmental Sub-Class
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None | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | 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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Yes | 13 LULC were used | Multiple |
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EM Ecological Scale
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Yes | 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
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EM ID
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EM-185 | EM-438 | EM-985 |
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EM Organismal Scale
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Yes | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-185 | EM-438 | EM-985 |
| None Available | None Available | None Available |
EnviroAtlas URL
| EM-185 | EM-438 | EM-985 |
| None Available | Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD) | Big game hunting recreation demand, Percent GAP Status 1 & 2, Total Employment |
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-185 | EM-438 | EM-985 |
| 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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
| EM-185 | EM-438 | EM-985 |
| None | None | None |
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