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-367 | EM-699 | EM-712 |
EM-784 
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EM-968 |
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EM Short Name
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InVEST Coastal Blue Carbon | Fish species richness, St. John, USVI, USA | ESII Tool method | Wildflower mix supporting bees, Florida, USA | EPA Stormwater Manamgement Model |
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EM Full Name
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InVEST v3.0 Coastal Blue Carbon | Fish species richness, St. John, USVI, USA | ESII (Ecosystem Services Identification & Inventory) Tool method | Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA | Storm Water Management Model User's Manual Version 5.2 |
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EM Source or Collection
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InVEST | None | None | None | US EPA |
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EM Source Document ID
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310 | 355 |
391 ?Comment:Website for online user support |
400 | 452 |
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Document Author
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Natural Capital Project | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | EcoMetrix Solutions Group (ESG) | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Rossman, L. A., M., Simon |
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Document Year
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2014 | 2007 | 2016 | 2015 | 2022 |
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Document Title
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Blue Carbon model - InVEST (v3.0) | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | ESII Tool | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Storm Water Management Model User's Manual Version 5.2 |
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Document Status
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Documented, not peer reviewed | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
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Comments on Status
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other | Published journal manuscript | Website | Published journal manuscript | Published EPA report |
Software and Access
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
| http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Not applicable | https://www.esiitool.com/ | Not applicable | https://www.epa.gov/water-research/storm-water-management-model-swmm | |
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Contact Name
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Gregg Verutes | Simon Pittman | Not reported | Neal Williams | David Burden |
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Contact Address
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Stanford University | 1305 East-West Highway, Silver Spring, MD 20910, USA | Not reported | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 |
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Contact Email
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gverutes@stanford.edu | simon.pittman@noaa.gov | Not reported | nmwilliams@ucdavis.edu | burden.david@epa.gov |
EM Description
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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Summary Description
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Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | 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." | AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance." | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
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Specific Policy or Decision Context Cited
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None identified | None provided | None identified | None identrified | NA |
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Biophysical Context
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Land use land class; habitat type | Hard and soft benthic habitat types approximately to the 33m isobath | Not applicable | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | NA |
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EM Scenario Drivers
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Land use land cover changes; habitat disturbance | No scenarios presented | No scenarios presented | Varied wildflower planting mixes of annuals and perennials | NA |
EM Relationship to Other EMs or Applications
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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Method Only, Application of Method or Model Run
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Method Only | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method Only |
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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 | 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-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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Document ID for related EM
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None | Doc-355 | None | None | None |
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EM ID for related EM
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None | EM-590 | EM-698 | EM-713 | EM-796 | EM-797 | EM-804 | EM-805 | EM-806 | EM-812 | EM-814 | EM-971 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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EM Temporal Extent
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Not applicable | 2000-2005 | Not applicable | 2011-2012 | Not applicable |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | past time | both |
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EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | continuous |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Year | Not applicable |
EM Spatial Extent
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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Bounding Type
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Not applicable | Physiographic or ecological | Not applicable |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
No location (no locational reference given) |
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Spatial Extent Name
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Not applicable | SW Puerto Rico, | Not applicable | Agricultural plots | Not applicable |
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Spatial Extent Area (Magnitude)
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Not applicable | 100-1000 km^2 | Not applicable | 10-100 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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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 distributed (in at least some cases) ?Comment:map units delineated by user based on project. |
spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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volume, for 3-D feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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user-specified | not reported | map units | Not applicable | mm |
EM Structure and Computation Approach
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Analytic |
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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-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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Model Calibration Reported?
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Not applicable | No | Not applicable | No | Not applicable |
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Model Goodness of Fit Reported?
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Not applicable | Yes | Not applicable | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None | None | None |
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Model Operational Validation Reported?
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Not applicable | Yes | Not applicable | No | Not applicable |
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Model Uncertainty Analysis Reported?
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Not applicable | No | Not applicable | No | Not applicable |
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Model Sensitivity Analysis Reported?
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Not applicable | Yes | Not applicable | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | No | 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-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
| None | None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
| None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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Centroid Latitude
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-9999 | 17.79 | Not applicable | 29.4 | Not applicable |
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Centroid Longitude
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-9999 | -64.62 | Not applicable | -82.18 | Not applicable |
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Centroid Datum
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Not applicable | WGS84 | Not applicable | WGS84 | Not applicable |
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Centroid Coordinates Status
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Not applicable | Estimated | Not applicable | Provided | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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EM Environmental Sub-Class
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Inland Wetlands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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user specified | shallow coral reefs | Not applicable | Agricultural landscape | User-defined catchments |
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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 | Not applicable | Ecological scale corresponds to the Environmental Sub-class | Other or unclear (comment) |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
| None Available |
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None Available |
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None Available |
EnviroAtlas URL
| EM-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
| Carbon Storage by Tree Biomass | None Available | GAP Ecological Systems, The National Hydrography Dataset (NHD), Average Annual Precipitation, Agricultural water use (million gallons/day) | None Available | None Available |
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-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
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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-367 | EM-699 | EM-712 |
EM-784
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EM-968 |
| None |
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