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-148 
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EM-195 | EM-260 | EM-414 |
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EM Short Name
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InVEST - Water provision, Francoli River, Spain | C Sequestration and De-N, Tampa Bay, FL, USA | Coral taxa and land development, St.Croix, VI, USA | SAV occurrence, St. Louis River, MN/WI, USA |
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EM Full Name
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InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA |
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EM Source or Collection
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InVEST | US EPA | US EPA | US EPA |
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EM Source Document ID
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280 | 186 | 96 | 330 |
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Document Author
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Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Russell, M. and Greening, H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke |
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Document Year
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2013 | 2013 | 2011 | 2013 |
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Document Title
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The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | Estimating benefits in a recovering estuary: Tampa Bay, Florida | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary |
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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 journal manuscript |
Software and Access
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EM ID
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EM-148
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EM-195 | EM-260 | EM-414 |
| https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Montse Marquès | M. Russell | Leah Oliver | Ted R. Angradi |
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Contact Address
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Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | National Health and Environmental Research Effects Laboratory | 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 |
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Contact Email
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montserrat.marques@fundacio.urv.cat | Russell.Marc@epamail.epa.gov | leah.oliver@epa.gov | angradi.theodore@epa.gov |
EM Description
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EM ID
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EM-148
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EM-195 | EM-260 | EM-414 |
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Summary Description
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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: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | 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) | 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." |
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Specific Policy or Decision Context Cited
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None identified | Restoration of seagrass | Not applicable | None identified |
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Biophysical Context
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Mediteranean coastal mountains | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | nearshore; <1.5 km offshore; <12 m depth | submerged aquatic vegetation |
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EM Scenario Drivers
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IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | Habitat loss or restoration in Tampa Bay Estuary | Not applicable | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-148
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EM-195 | EM-260 | EM-414 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application |
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New or Pre-existing EM?
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Application of existing model | 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
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EM ID
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EM-148
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EM-195 | EM-260 | EM-414 |
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Document ID for related EM
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Doc-307 | Doc-311 | Doc-338 | Doc-205 | None | None | None |
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EM ID for related EM
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EM-344 | EM-368 | EM-437 | EM-111 | None | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-148
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EM-195 | EM-260 | EM-414 |
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EM Temporal Extent
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1971-2100 | 1982-2010 | 2006-2007 | 2010 - 2012 |
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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-148
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EM-195 | EM-260 | EM-414 |
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Bounding Type
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Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological |
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Spatial Extent Name
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Francoli River | Tampa Bay Estuary | St.Croix, U.S. Virgin Islands | St. Louis River Estuary |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 10-100 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-148
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EM-195 | EM-260 | EM-414 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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. |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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30m x 30m | 1 ha | Not applicable | 0.07 m^2 to 0.70 m^2 |
EM Structure and Computation Approach
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EM ID
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EM-148
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EM-195 | EM-260 | EM-414 |
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EM Computational Approach
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Numeric | Analytic | Analytic | Analytic |
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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-148
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EM-195 | EM-260 | EM-414 |
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Model Calibration Reported?
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No | Yes | Yes | Yes |
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Model Goodness of Fit Reported?
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No | No | Yes | Yes |
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Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
No | No | Yes |
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Model Uncertainty Analysis Reported?
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No | No | Yes | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No |
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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-148
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EM-195 | EM-260 | EM-414 |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-148
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EM-195 | EM-260 | EM-414 |
| None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-148
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EM-195 | EM-260 | EM-414 |
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Centroid Latitude
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41.26 | 27.95 | 17.75 | 46.72 |
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Centroid Longitude
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1.18 | -82.47 | -64.75 | -96.13 |
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Centroid Datum
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WGS84 | WGS84 | NAD83 | 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-148
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EM-195 | EM-260 | EM-414 |
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EM Environmental Sub-Class
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Rivers and Streams | 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 |
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Specific Environment Type
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Coastal mountains | Subtropical Estuary | stony coral reef | Freshwater estuarine system |
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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 corresponds to 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-148
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EM-195 | EM-260 | EM-414 |
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EM Organismal Scale
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Not applicable | Not applicable | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-148
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EM-195 | EM-260 | EM-414 |
| None Available | None Available |
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None Available |
EnviroAtlas URL
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EM-148
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EM-195 | EM-260 | EM-414 |
| Average Annual Precipitation, The Watershed Boundary Dataset (WBD) | Carbon Storage by Tree Biomass | None Available | 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)
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EM-148
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EM-195 | EM-260 | EM-414 |
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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-148
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EM-195 | EM-260 | EM-414 |
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None |