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
em.detail.idHelp
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EM-99 |
EM-186 
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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EM Short Name
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Landscape importance for crops, Europe | FORCLIM v2.9, Western OR, USA | EPA H2O, Tampa Bay Region, FL,USA | Wetland shellfish production, Gulf of Mexico, USA | Yasso07 - SOC, Loess Plateau, China | Fish species richness, St. John, USVI, USA | WESP: Urban Stormwater Treatment, ID, USA | SLAMM, Tampa Bay, FL, USA |
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EM Full Name
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Landscape importance for crop-based production, Europe | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | EPA H2O, Tampa Bay Region, FL, USA | Wetland shellfish production, Gulf of Mexico, USA | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | Fish species richness, St. John, USVI, USA | WESP: Urban Stormwater Treament, ID, USA | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
None | None | None | None |
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EM Source Document ID
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228 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
321 | 324 | 344 | 355 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
415 ?Comment:Secondary sources: Documents 412 and 413. |
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Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Murphy, C. and T. Weekley | Sherwood, E. T. and H. S. Greening |
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Document Year
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2012 | 2007 | 2015 | 2012 | 2015 | 2007 | 2012 | 2014 |
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Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | EPA H20 User Manual | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats |
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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 | 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 EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript |
Software and Access
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EM ID
em.detail.idHelp
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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| Not applicable | Not applicable | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | |
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Contact Name
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Marion Potschin | Richard T. Busing | Marc J. Russell, Ph.D. | Stephen J. Jordan | Xing Wu | Simon Pittman | Chris Murphy | Edward T. Sherwood |
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Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | Chinese Academy of Sciences, Beijing 100085, China | 1305 East-West Highway, Silver Spring, MD 20910, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA |
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Contact Email
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marion.potschin@nottingham.ac.uk | rtbusing@aol.com | russell.marc@epa.gov | jordan.steve@epa.gov | xingwu@rceesac.cn | simon.pittman@noaa.gov | chris.murphy@idfg.idaho.gov | esherwood@tbep.org |
EM Description
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EM ID
em.detail.idHelp
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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Summary Description
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ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | 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." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e). | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | ABSTRACT: "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 … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | 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." | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" |
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Specific Policy or Decision Context Cited
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None identified | None Identified | None reported | None identified | None | None provided | None identified | None identified |
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Biophysical Context
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No additional description provided | Coastal to montane, Pacific Northwest US (Oregon) forests. | Not applicable | Estuarine environments and marsh-land interfaces | Agricultural plain, hills, gulleys, forest, grassland, Central China | Hard and soft benthic habitat types approximately to the 33m isobath | restored, enhanced and created wetlands | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | Two scenarios modelled, forests with and without fire | Land Use, EGS algorithm values, | Shellfish type; Changes to submerged aquatic vegetation (SAV) | Land use change | No scenarios presented | Sites, function or habitat focus | Varying sea level rise (baseline - 2m), and two habitat adaption strategies |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application |
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 + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | WESP - Urban Stormwater Treatment | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
em.detail.idHelp
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-231 | Doc-228 |
Doc-22 | Doc-23 ?Comment:Related document ID 22 provides tree species specific parameters in appendix. |
None | None | Doc-343 | Doc-342 | Doc-355 | Doc-390 | Doc-412 | Doc-413 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-146 | EM-208 | EM-224 | None | EM-604 | EM-603 | EM-466 | EM-467 | EM-480 | EM-485 | EM-590 | EM-698 | EM-718 | EM-734 | EM-857 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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EM Temporal Extent
em.detail.tempExtentHelp
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2000 | >650 yrs | Not applicable | 1950 - 2050 | 1969-2011 | 2000-2005 | 2010-2011 | 2002-2100 |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | past time | Not applicable | future time | past time | Not applicable | past time | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Varies by Run | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Year | Year | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or ecological |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC |
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Spatial Extent Name
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The EU-25 plus Switzerland and Norway | Western Oregon, north of 43.00 N to Washington border | Tampa Bay region | Gulf of Mexico (estuarine and coastal) | Yangjuangou catchment | SW Puerto Rico, | Wetlands in idaho | Tampa Bay estuary watershed |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 1-10 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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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) |
spatially distributed (in at least some cases) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
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) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | 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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1 km x 1 km | 0.08 ha | 30m x 30m | 55.2 km^2 | 30m x 30m | not reported | Not applicable | 10 x 10 m |
EM Structure and Computation Approach
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EM ID
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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EM Computational Approach
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Logic- or rule-based | Numeric | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | 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-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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Model Calibration Reported?
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No | No | No | Yes | Yes | No | No | No |
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Model Goodness of Fit Reported?
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No | No | No | No |
Yes ?Comment:For the year 2006 and 2011 |
Yes | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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None | None |
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Model Operational Validation Reported?
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Yes | Yes | No | No | No | Yes | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | No | Yes | 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 | No | 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-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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| None | None | None |
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None |
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None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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Centroid Latitude
em.detail.ddLatHelp
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50.53 | 44.66 | 28.05 | 30.44 | 36.7 | 17.79 | 44.06 | 27.76 |
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Centroid Longitude
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7.6 | -122.56 | -82.52 | -87.99 | 109.52 | -64.62 | -114.69 | -82.54 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Forests | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Inland Wetlands | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Not applicable | Primarily conifer forest | All terestrial landcover and waterbodies | Submerged aquatic vegetation in estuaries and coastal lagoons | Loess plain | shallow coral reefs | created, restored and enhanced wetlands | Esturary and associated urban and terrestrial environment |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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 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 | 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
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Species | Not applicable | Species | Not applicable | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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| None Available |
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None Available |
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None Available |
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None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
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| EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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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>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-99 |
EM-186
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EM-392 |
EM-397
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EM-469 | EM-699 |
EM-729
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EM-863
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None |
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None |
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None | None |