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-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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EM Short Name
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ARIES flood regulation, Puget Sound Region, USA | Reef dive site favorability, St. Croix, USVI | Chinook salmon value (household), Yaquina Bay, OR | Valuing environmental ed., New York, New York | ESTIMAP - Pollination potential, Iran |
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EM Full Name
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ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | Dive site favorability (reef), St. Croix, USVI | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Valuing environmental education, Hudson River Park, New York, New York | ESTIMAP - Pollination potential, Iran |
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EM Source or Collection
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ARIES | US EPA | US EPA | None | None |
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EM Source Document ID
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302 | 335 | 324 | 416 | 434 |
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Document Author
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Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Hutcheson, W. Hoagland, P., and D. Jin | Rahimi, E., Barghjelveh, S., and P. Dong |
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Document Year
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2014 | 2014 | 2012 | 2018 | 2020 |
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Document Title
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From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Valuing environmental education as a cultural ecosystem service at Hudson River Park | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) |
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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 | 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 | Published journal manuscript |
Software and Access
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
| http://aries.integratedmodelling.org/ | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Ken Bagstad | Susan H. Yee | Stephen Jordan | Walter Hutcheson | Ehsan Rahini |
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Contact Address
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Geosciences and Environmental Change Science Center, US Geological Survey | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | New York University, United States | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
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Contact Email
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kjbagstad@usgs.gov | yee.susan@epa.gov | jordan.steve@epa.gov | wwh235@nyu.edu | ehsanrahimi666@gmail.com |
EM Description
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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Summary Description
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ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures." | 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 recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. " | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None reported |
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Biophysical Context
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No additional description provided | No additional description provided | Yaquina Bay estuary | N/A | None additional |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | N?A | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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Method Only, Application of Method or Model Run
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Method + Application | 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 | New or revised model | New or revised model | Application of existing 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-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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Document ID for related EM
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Doc-303 | Doc-305 | None | Doc-324 | None | Doc-432 |
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EM ID for related EM
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None | None | EM-603 | EM-397 | None | EM-939 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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EM Temporal Extent
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1971-2006 | 2006-2007, 2010 | 2003-2008 | 2015 | 2020 |
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EM Time Dependence
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time-stationary | 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 | Not applicable |
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EM Time Continuity
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Not applicable | 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 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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Bounding Type
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Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical |
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Spatial Extent Name
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Puget Sound Region | Coastal zone surrounding St. Croix | Pacific Northwest | Hudson River Park | Iran |
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Spatial Extent Area (Magnitude)
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10,000-100,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 10-100 ha | >1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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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 lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
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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 | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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200m x 200m | 10 m x 10 m | Not applicable | Not applicable | ha^2 |
EM Structure and Computation Approach
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Numeric |
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EM Determinism
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deterministic | 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-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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Model Calibration Reported?
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No | Yes | No | No | No |
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Model Goodness of Fit Reported?
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No | No | No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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No | Yes | Yes | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | 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 | 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-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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None |
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Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
| None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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Centroid Latitude
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48 | 17.73 | 44.62 | 40.73 | 32.29 |
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Centroid Longitude
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-123 | -64.77 | -124.02 | -74.01 | 53.68 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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EM Environmental Sub-Class
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Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Terrestrial environment surrounding a large estuary | Coral reefs | Yaquina Bay estuary and ocean | Park | terrestrial land types |
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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 | 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-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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EM Organismal Scale
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Not applicable | Guild or Assemblage | Other (multiple scales) | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
| None Available | None Available |
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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)
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| EM-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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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-326 | EM-456 | EM-604 | EM-875 | EM-941 |
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