EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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EM Short Name
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Sed. denitrification, St. Louis River, MN/WI, USA | SIRHI, St. Croix, USVI | Decrease in erosion (shoreline), St. Croix, USVI | EcoAIM v.1.0 APG, MD |
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EM Full Name
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Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA | SIRHI (SImplified Reef Health Index), St. Croix, USVI | Decrease in erosion (shoreline) by reef, St. Croix, USVI | EcoAIM v.1.0, Aberdeen Proving Ground, MD |
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EM Source or Collection
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US EPA | US EPA | US EPA | None |
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EM Source Document ID
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333 | 335 | 335 | 374 |
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Document Author
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Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd |
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Document Year
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2014 | 2014 | 2014 | 2014 |
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Document Title
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Sediment nitrification and denitrification in a Lake Superior estuary | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report |
Software and Access
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
| Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Brent J. Bellinger | Susan H. Yee | Susan H. Yee | Pieter Booth |
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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 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Exponent Inc., Bellevue WA |
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Contact Email
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bellinger.brent@epa.ogv | yee.susan@epa.gov | yee.susan@epa.gov | pbooth@ramboll.com |
EM Description
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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Summary Description
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ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ?Comment:BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model. |
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...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati | 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: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None reported |
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Biophysical Context
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Estuarine system | No additional description provided | No additional description provided | Chesapeake bay coastal plain, elev. 60ft. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing model | New or revised 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-416 | EM-418 | EM-449 | EM-647 |
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Document ID for related EM
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None | None | Doc-335 | None |
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EM ID for related EM
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None | None | EM-447 | EM-448 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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EM Temporal Extent
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2011 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | 2014 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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Bounding Type
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Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
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St. Louis River estuary | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Aberdeen Proving Ground |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams. |
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Spatial Grain Type
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Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
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Not applicable | 10 m x 10 m | 10 m x 10 m | 100m x 100m |
EM Structure and Computation Approach
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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Model Calibration Reported?
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No | Yes | Yes |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
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Model Goodness of Fit Reported?
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No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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No | Yes | Yes | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-416 | EM-418 | EM-449 | EM-647 |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-416 | EM-418 | EM-449 | EM-647 |
| None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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Centroid Latitude
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46.75 | 17.73 | 17.73 | 39.46 |
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Centroid Longitude
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-92.08 | -64.77 | -64.77 | 76.12 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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EM Environmental Sub-Class
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Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland |
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Specific Environment Type
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Freshwater estuary | Coral reefs | Coral reefs | Coastal Plain |
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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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-416 | EM-418 | EM-449 | EM-647 |
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EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-416 | EM-418 | EM-449 | EM-647 |
| None Available |
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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)
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| EM-416 | EM-418 | EM-449 | EM-647 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-416 | EM-418 | EM-449 | EM-647 |
| None | None |
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