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-376 | EM-449 | EM-698 | EM-993 |
EM Short Name
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MIMES: For Massachusetts Ocean (v1.0) | Decrease in erosion (shoreline), St. Croix, USVI | Fish species richness, St. Croix, USVI | Velma- 6PPD-Q concentrations, Seattle, WA |
EM Full Name
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Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Fish Species Richness, Buck Island, St. Croix , USVI | VELMA: 6PPD-Quinone stormwater concentrations , Seattle, Washington |
EM Source or Collection
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US EPA | US EPA | None | US EPA |
EM Source Document ID
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316 | 335 | 355 | 465 |
Document Author
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Altman, I., R.Boumans, J. Roman, L. Kaufman | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Halama JJ, McKane RB, Barnhart BL, Pettus PP, Brookes AF, Adams AK, Gockel CK, Djang KS, Phan V, Chokshi SM, Graham JJ, Tian Z, Peter KT and Kolodziej,EP |
Document Year
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2012 | 2014 | 2007 | 2024 |
Document Title
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Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Watershed analysis of urban stormwater contaminant 6PPD-Quinone hotspots and stream concentrations using a process-based ecohydrological model |
Document Status
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Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | Not applicable | Not reported | |
Contact Name
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Irit Altman | Susan H. Yee | Simon Pittman | Jonathan Halama |
Contact Address
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Boston University, Portland, Maine | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | 1305 East-West Highway, Silver Spring, MD 20910, USA | U.S. Environmental Protection Agency, Corvallis, OR |
Contact Email
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iritaltman@bu.edu | yee.susan@epa.gov | simon.pittman@noaa.gov | Halama.Jonathan@epa.gov |
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
Summary Description
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AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT: "Coho salmon (Oncorhynchus kisutch) are highly sensitive to 6PPD-Quinone (6PPD-Q). Details of the hydrological and biogeochemical processes controlling spatial and temporal dynamics of 6PPD-Q fate and transport from points of deposition to receiving waters (e.g., streams, estuaries) are poorly understood. To understand the fate and transport of 6PPD and mechanisms leading to salmon mortality Visualizing Ecosystem Land Management Assessments (VELMA), an ecohydrological model developed by US Environmental Protection Agency (EPA), was enhanced to better understand and inform stormwater management planning by municipal, state, and federal partners seeking to reduce stormwater contaminant loads in urban streams draining to the Puget Sound National Estuary. This work focuses on the 5.5 km2 Longfellow Creek upper watershed (Seattle, Washington, United States), which has long exhibited high rates of acute urban runoff mortality syndrome in coho salmon. We present VELMA model results to elucidate these processes for the Longfellow Creek watershed across multiple scales–from 5-m grid cells to the entire watershed. Our results highlight hydrological and biogeochemical controls on 6PPD-Q flow paths, and hotspots within the watershed and its stormwater infrastructure, that ultimately impact contaminant transport to Longfellow Creek and Puget Sound. Simulated daily average 6PPD-Q and available observed 6PPD-Q peak in-stream grab sample concentrations (ng/L) corresponds within plus or minus 10 ng/L. Most importantly, VELMA’s high-resolution spatial and temporal analysis of 6PPD-Q hotspots provides a tool for prioritizing the locations, amounts, and types of green infrastructure that can most effectively reduce 6PPD-Q stream concentrations to levels protective of coho salmon and other aquatic species. " |
Specific Policy or Decision Context Cited
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None identified | None identified | None provided | Not reported |
Biophysical Context
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No additional description provided | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | 6PPD deposition from vehicle tire wear particles. |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | N/A |
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | 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
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
Document ID for related EM
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None | Doc-335 | Doc-355 | Doc-366 | Doc-423 | Doc-430 |
EM ID for related EM
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None | EM-447 | EM-448 | EM-590 | EM-699 | None |
EM Modeling Approach
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
EM Temporal Extent
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Not applicable | 2006-2007, 2010 | 2000-2005 | 9/2020-6/2021 |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | past time |
EM Time Continuity
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discrete | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Day |
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
Bounding Type
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Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Watershed/Catchment/HUC |
Spatial Extent Name
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Massachusetts Ocean | Coastal zone surrounding St. Croix | SW Puerto Rico, | Longfellow creek |
Spatial Extent Area (Magnitude)
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1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 1-10 km^2 |
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | 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 | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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1 km x1 km | 10 m x 10 m | not reported | Not applicable |
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
EM Computational Approach
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Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
Model Calibration Reported?
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No | Yes | No | Yes |
Model Goodness of Fit Reported?
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No | No | Yes | No |
Goodness of Fit (metric| value | unit)
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None | None |
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None |
Model Operational Validation Reported?
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No | Yes | Yes | Yes |
Model Uncertainty Analysis Reported?
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No | No | No | Unclear |
Model Sensitivity Analysis Reported?
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No | No | Yes | Unclear |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-376 | EM-449 | EM-698 | EM-993 |
None | None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
Centroid Latitude
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41.72 | 17.73 | 17.79 | 47.55 |
Centroid Longitude
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-69.87 | -64.77 | -64.62 | 122.37 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Provided |
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams |
Specific Environment Type
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None identified | Coral reefs | shallow coral reefs | small stream |
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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-376 | EM-449 | EM-698 | EM-993 |
EM Organismal Scale
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Species | Not applicable | Guild or Assemblage | Species |
Taxonomic level and name of organisms or groups identified
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None Available |
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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-376 | EM-449 | EM-698 | EM-993 |
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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)
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Comment:Model identifies toxicant concentrations relative to the known LC50 for coho juveniles which is 95ng/L (Spromber and Scholz, 2011; |