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-414 |
EM-542 ![]() |
EM-998 |
EM Short Name
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SAV occurrence, St. Louis River, MN/WI, USA | Coastal protection in Belize | CAESAR landscape evolution model |
EM Full Name
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Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
EM Source or Collection
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US EPA | InVEST | None |
EM Source Document ID
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330 | 350 | 468 |
Document Author
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Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J. |
Document Year
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2013 | 2016 | 2007 |
Document Title
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Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
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 journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
Not applicable | Not identified in paper | http://www.coulthard.org.uk/ | |
Contact Name
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Ted R. Angradi | Greg Guannel | Marco J. Van De Wiel |
Contact Address
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U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | The Nature Conservancy, Coral Gables, FL. USA | Department of Geography, University of Western Ontario, London, Ontario, Canada |
Contact Email
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angradi.theodore@epa.gov | greg.guannel@gmail.com | mvandew3@uwo.ca |
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
Summary Description
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ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK. |
Specific Policy or Decision Context Cited
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None identified | Future rock lobster fisheries management | None identified |
Biophysical Context
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submerged aquatic vegetation | barrier reef and fringing reef in nearshore coastal marine system | River Teifi, Lampeter, Wales |
EM Scenario Drivers
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No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | Varying flow velocities and durations |
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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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 ID
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EM-414 |
EM-542 ![]() |
EM-998 |
Document ID for related EM
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None | None | Doc-467 |
EM ID for related EM
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None | None | EM-997 |
EM Modeling Approach
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
EM Temporal Extent
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2010 - 2012 | 2005-2013 | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | discrete | continuous |
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 | Second | Not applicable |
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
Bounding Type
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Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC |
Spatial Extent Name
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St. Louis River Estuary | Coast of Belize | River Teifi |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | Not applicable |
Spatial Grain Size
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0.07 m^2 to 0.70 m^2 | 1 meter | Not applicable |
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
EM Computational Approach
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Analytic | Analytic | 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-414 |
EM-542 ![]() |
EM-998 |
Model Calibration Reported?
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Yes | No | Not applicable |
Model Goodness of Fit Reported?
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Yes | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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Yes |
No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | 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-414 |
EM-542 ![]() |
EM-998 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-414 |
EM-542 ![]() |
EM-998 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
Centroid Latitude
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46.72 | 18.63 | 52.04 |
Centroid Longitude
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-96.13 | -88.22 | -4.39 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated |
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
EM Environmental Sub-Class
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Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Rivers and Streams |
Specific Environment Type
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Freshwater estuarine system | coral reefs | River |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-414 |
EM-542 ![]() |
EM-998 |
EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-414 |
EM-542 ![]() |
EM-998 |
None Available | None Available | None Available |
EnviroAtlas URL
EM-414 |
EM-542 ![]() |
EM-998 |
Average Annual Precipitation | GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation | Average Annual Precipitation |
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-414 |
EM-542 ![]() |
EM-998 |
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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-414 |
EM-542 ![]() |
EM-998 |
None |
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