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-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
EM Short Name
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InVEST carbon storage and sequestration (v3.2.0) | InVEST fisheries, lobster, South Africa | Grasshopper Sparrow density, CREP, Iowa, USA | WESP: Marsh & wet meadow, ID, USA | NU-WRF, USA |
EM Full Name
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InVEST v3.2.0 Carbon storage and sequestration | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Grasshopper Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | WESP: Seasonally flooded marsh & wet meadow, Idaho, USA | NASA Unified Weather Research and Forecasting model |
EM Source or Collection
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InVEST | InVEST | None | None | None |
EM Source Document ID
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315 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
372 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
484 |
Document Author
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The Natural Capital Project | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Murphy, C. and T. Weekley | Peters-Lidard, Christa et al. Sujay V. Kumar, , Jossy P. Jacob b , Thomas Clune f , Wei-Kuo Tao g , Mian Chin h , Arthur Hou I , Jonathan L. Case j , Dongchul Kim k , Kyu-Myong Kim l , William Lau m, Yuqiong Liu n , Jainn Shi o , David Starr g , Qian Tan h , Zhining Tao k , Benjamin F. Zaitchik p , Bradley Zavodsky q , Sara Q. Zhang r , Milija Zupanski |
Document Year
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2015 | 2018 | 2010 | 2012 | 2015 |
Document Title
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Carbon storage and sequestration - InVEST (v3.2.0) | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales |
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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Website | Published journal manuscript | Published report | Published report | Published journal manuscript |
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | https://git.smce.nasa.gov/astg/nuwrf/nu-wrf-dev | |
Contact Name
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The Natural Capital Project | Michelle Ward | David Otis | Chris Murphy | Christa Peters-Lidard |
Contact Address
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371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Hydrospheric and Biospheric Sciences Division, Code 610HB, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA |
Contact Email
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invest@naturalcapitalproject.org | m.ward@uq.edu.au | dotis@iastate.edu | chris.murphy@idfg.idaho.gov | christa.peters@nasa.gov |
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
Summary Description
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Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Grasshopper Sparrow (Ammodramus savannarum)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: GRSP density = e (-2.554612 + 0.0246975 * grass400 – 0.1032461 * trees400) | 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: "With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satelliteresolved scales. “Satellite-resolved” scales (roughly 1e25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the Goddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles " |
Specific Policy or Decision Context Cited
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None identified | Future rock lobster fisheries management | None identified | None identified | Not Applicable |
Biophysical Context
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Not applicable | No additional description provided | Prairie pothole region of north-central Iowa | restored, enhanced and created wetlands | Atmospheric variables |
EM Scenario Drivers
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Optional future scenarios for changed LULC and wood harvest | Fisheries exploitation; fishing vulnerability (of age classes) | No scenarios presented | Sites, function or habitat focus | Not applicable |
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
Method Only, Application of Method or Model Run
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Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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New or revised model | Application of existing model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
Document ID for related EM
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Doc-309 | None | Doc-372 | Doc-390 | None |
EM ID for related EM
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EM-349 | None | EM-652 | EM-651 | EM-650 | EM-648 | EM-718 | EM-734 | EM-743 | EM-1029 |
EM Modeling Approach
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
EM Temporal Extent
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Not applicable | 1986-2115 | 2002-2007 | 2010-2012 | Not applicable |
EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-dependent | Not applicable |
EM Time Reference (Future/Past)
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future time | future time | Not applicable | past time | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | 1 | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Year | Not applicable | Not applicable | Not applicable |
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
Bounding Type
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Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable |
Spatial Extent Name
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Not applicable | Table Mountain National Park Marine Protected Area | CREP (Conservation Reserve Enhancement Program) wetland sites | Wetlands in idaho | Not applicable |
Spatial Extent Area (Magnitude)
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Not applicable | 100-1000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | Not applicable |
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | other or unclear (comment) |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
Spatial Grain Size
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application specific | Not applicable | multiple, individual, irregular shaped sites | Not applicable | Not applicable |
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
EM Computational Approach
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Analytic | Numeric | Analytic | Numeric | 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-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
Model Calibration Reported?
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Not applicable | No | Unclear | No | Not applicable |
Model Goodness of Fit Reported?
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Not applicable | No | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
Model Operational Validation Reported?
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Not applicable |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Unclear | No | Not applicable |
Model Uncertainty Analysis Reported?
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Not applicable | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
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Not applicable | No | No | No | 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])
EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
Centroid Latitude
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-9999 | -34.18 | 42.62 | 44.06 | Not applicable |
Centroid Longitude
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-9999 | 18.35 | -93.84 | -114.69 | Not applicable |
Centroid Datum
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Not applicable | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Not applicable | Provided | Estimated | Estimated | Not applicable |
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
EM Environmental Sub-Class
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Not applicable | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Atmosphere |
Specific Environment Type
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Terrestrial environments, but not specified for methods | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Grassland buffering inland wetlands set in agricultural land | created, restored and enhanced wetlands | Regional wealther |
EM Ecological Scale
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Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
EM Organismal Scale
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Not applicable | Individual or population, within a species | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
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-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
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None |
<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-374 |
EM-541 ![]() |
EM-649 |
EM-760 ![]() |
EM-1022 |
None |
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None | None |