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-260 |
EM-542 ![]() |
EM-998 |
EM Short Name
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Coral taxa and land development, St.Croix, VI, USA | Coastal protection in Belize | CAESAR landscape evolution model |
EM Full Name
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Coral taxa richness and land development, St.Croix, Virgin Islands, 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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96 | 350 | 468 |
Document Author
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Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | 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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2011 | 2016 | 2007 |
Document Title
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Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | 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-260 |
EM-542 ![]() |
EM-998 |
Not applicable | Not identified in paper | http://www.coulthard.org.uk/ | |
Contact Name
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Leah Oliver | Greg Guannel | Marco J. Van De Wiel |
Contact Address
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National Health and Environmental Research Effects Laboratory | The Nature Conservancy, Coral Gables, FL. USA | Department of Geography, University of Western Ontario, London, Ontario, Canada |
Contact Email
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leah.oliver@epa.gov | greg.guannel@gmail.com | mvandew3@uwo.ca |
EM ID
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EM-260 |
EM-542 ![]() |
EM-998 |
Summary Description
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AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | 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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Not applicable | Future rock lobster fisheries management | None identified |
Biophysical Context
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nearshore; <1.5 km offshore; <12 m depth | barrier reef and fringing reef in nearshore coastal marine system | River Teifi, Lampeter, Wales |
EM Scenario Drivers
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Not applicable | 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-260 |
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-260 |
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-260 |
EM-542 ![]() |
EM-998 |
EM Temporal Extent
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2006-2007 | 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-260 |
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.Croix, U.S. Virgin Islands | 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-260 |
EM-542 ![]() |
EM-998 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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Not applicable | length, for linear feature (e.g., stream mile) | Not applicable |
Spatial Grain Size
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Not applicable | 1 meter | Not applicable |
EM ID
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EM-260 |
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-260 |
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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No |
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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Yes | 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-260 |
EM-542 ![]() |
EM-998 |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-260 |
EM-542 ![]() |
EM-998 |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-260 |
EM-542 ![]() |
EM-998 |
Centroid Latitude
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17.75 | 18.63 | 52.04 |
Centroid Longitude
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-64.75 | -88.22 | -4.39 |
Centroid Datum
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NAD83 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated |
EM ID
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EM-260 |
EM-542 ![]() |
EM-998 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams |
Specific Environment Type
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stony coral reef | coral reefs | River |
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-260 |
EM-542 ![]() |
EM-998 |
EM Organismal Scale
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Guild or Assemblage | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-260 |
EM-542 ![]() |
EM-998 |
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None Available | None Available |
EnviroAtlas URL
EM-260 |
EM-542 ![]() |
EM-998 |
None Available | 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-260 |
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-260 |
EM-542 ![]() |
EM-998 |
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