EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
EM Short Name
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FORCLIM v2.9, West Cascades, OR, USA | EnviroAtlas - Restorable wetlands | WESP Method | Predicting ecosystem service values, Bangladesh | Atlantis ecosystem harvest submodel |
EM Full Name
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FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
EM Source or Collection
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US EPA | US EPA | EnviroAtlas | None | None | None |
EM Source Document ID
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23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
262 | 390 | 457 | 463 |
Document Author
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Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | US EPA Office of Research and Development - National Exposure Research Laboratory | Adamus, P. R. | Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y. | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
Document Year
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2007 | 2013 | 2016 | 2022 | 2011 |
Document Title
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Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | EnviroAtlas - National | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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 |
Comments on Status
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Published journal manuscript | Published on US EPA EnviroAtlas website | Published report | Published journal manuscript | Published journal manuscript |
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
Not applicable | https://www.epa.gov/enviroatlas |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | https://research.csiro.au/atlantis/home/links/ | |
Contact Name
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Richard T. Busing | EnviroAtlas Team | Paul R. Adamus | Syed Riad Morshed | Elizabeth Fulton |
Contact Address
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U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Not reported | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh | Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. |
Contact Email
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rtbusing@aol.com | enviroatlas@epa.gov | adamus7@comcast.net | riad.kuet.urp16@gmail.com | beth.fulton@csiro.au |
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
Summary Description
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ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability. | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
Specific Policy or Decision Context Cited
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None Identified | None Identified | None identified | N/A | None identified |
Biophysical Context
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West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | No additional description provided | None | Jashore city, Bangladesh | NA |
EM Scenario Drivers
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Two scenarios modelled, forests with and without fire | No scenarios presented | N/A | No scenarios presented | No scenarios presented |
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method Only | Method + Application | Method Only |
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 | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
Document ID for related EM
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Doc-22 | Doc-23 | None | None | None | Doc-456 | Doc-459 | Doc-461 | Doc-463 |
EM ID for related EM
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EM-146 | EM-208 | EM-186 | None | EM-718 | None | EM-978 | EM-981 | EM-983 | EM-985 | EM-990 |
EM Modeling Approach
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
EM Temporal Extent
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>650 yrs | 2006-2013 | Not applicable | 2000-2050 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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past time | Not applicable | Not applicable | both | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | continuous |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | 10 | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Year | Not applicable |
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
Bounding Type
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Physiographic or ecological | Geopolitical | Not applicable | Geopolitical | Not applicable |
Spatial Extent Name
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West Cascades, Oregon | conterminous United States | Not applicable | Jashore city, Bangladesh | Not applicable |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | >1,000,000 km^2 | Not applicable | 1000-10,000 km^2. | Not applicable |
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable |
Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | map scale, for cartographic feature | Not applicable |
Spatial Grain Size
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0.08 ha | irregular | not reported | 30m | Not applicable |
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
Model Calibration Reported?
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No | No | Not applicable | Yes | Not applicable |
Model Goodness of Fit Reported?
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No | No | Not applicable | Yes | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
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None |
Model Operational Validation Reported?
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Yes | No | No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Not applicable | Unclear | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | Not applicable | Unclear | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-492 | EM-706 | EM-979 | EM-991 |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-492 | EM-706 | EM-979 | EM-991 |
None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
Centroid Latitude
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44.24 | 39.5 | Not applicable | 23.95 | Not applicable |
Centroid Longitude
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-122.24 | -98.35 | Not applicable | 89.12 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | Not applicable | other | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Not applicable | Provided | Not applicable |
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
EM Environmental Sub-Class
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Forests | Agroecosystems | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
Specific Environment Type
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Primarily conifer forest | Terrestrial | Wetlands | Urban city | Multiple |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
EM Organismal Scale
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Species | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-492 | EM-706 | EM-979 | EM-991 |
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None Available | None Available | 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-492 | EM-706 | EM-979 | EM-991 |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-224 ![]() |
EM-492 | EM-706 | EM-979 | EM-991 |
None | None |
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None |