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-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
EM Short Name
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Yasso07 - SOC, Loess Plateau, China | InVEST fisheries, lobster, South Africa | WESP: Urban Stormwater Treatment, ID, USA | WESP: Marsh & wet meadow, ID, USA | VELMA v. 2.0 disturbance | Atlantis ecosystem assessment submodel |
EM Full Name
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Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | WESP: Urban Stormwater Treament, ID, USA | WESP: Seasonally flooded marsh & wet meadow, Idaho, USA | VELMA (Visualizing Ecosystems for Land Management Assessment) version 2.0 disturbance | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
EM Source or Collection
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None | InVEST | None | None | US EPA | None |
EM Source Document ID
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344 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
366 | 463 |
Document Author
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Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Murphy, C. and T. Weekley | Murphy, C. and T. Weekley | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | 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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2015 | 2018 | 2012 | 2012 | 2014 | 2011 |
Document Title
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Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | VELMA Version 2.0 User Manual and Technical Documentation | 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 | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published report | Published report | Published report | Published journal manuscript |
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
Contact Name
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Xing Wu | Michelle Ward | Chris Murphy | Chris Murphy | Robert B. McKane | Elizabeth Fulton |
Contact Address
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Chinese Academy of Sciences, Beijing 100085, China | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | U.S. EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia |
Contact Email
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xingwu@rceesac.cn | m.ward@uq.edu.au | chris.murphy@idfg.idaho.gov | chris.murphy@idfg.idaho.gov | mckane.bob@epa.gov | beth.fulton@csiro.au |
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
Summary Description
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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." | 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." | 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. | 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. | VELMA – Visualizing Ecosystems for Land Management Assessments - is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas. AUTHOR'S DESCRIPTION: "Understanding how disturbances such as harvest, fire and fertilization affect ecosystem services has been a major motivation in the development of VELMA. For example, how do disturbances such as forest harvest or the application of agronomic fertilizers affect hydrological and biogeochemical processes controlling water quality and quantity, carbon sequestration, production of greenhouse gases, etc.? Abdelnour et al. (2011, 2013) have already demonstrated the use of VELMA v1.0 to simulate the effects of forest clearcutting on ecohydrological processes that regulate a variety of ecosystem services. With the addition of a tissue-specific plant biomass (LSR) simulator and an enhanced GUI, VELMA v2.0 significantly expands the detail, flexibility, and ease of use for simulating disturbance effects. Currently available disturbance models include: - BurnDisturbanceModel, effects of fire. - GrazeDisturbanceModel, effects of grazing. - FertilizeLsrDisturbanceModel, effects of fertilizer applications. - HarvestLsrDisturbanceModel, effects of biomass harvest. Each of these disturbance models specifies where and when a disturbance event will occur. The Burn, Graze and Harvest models have options for specifying how much of each plant tissue and detritus pool (leaves, stems, roots) will be removed and where it goes (offsite and/or to a specified onsite C and N pools). The Fertilize model has options for applying nitrogen as ammonium, nitrate, urea and/or manure." | 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 | Future rock lobster fisheries management | None identified | None identified | None identified | None identified |
Biophysical Context
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Agricultural plain, hills, gulleys, forest, grassland, Central China | No additional description provided | restored, enhanced and created wetlands | restored, enhanced and created wetlands | No additional description provided | N/A |
EM Scenario Drivers
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Land use change | Fisheries exploitation; fishing vulnerability (of age classes) | Sites, function or habitat focus | Sites, function or habitat focus | No scenarios presented | No scenarios presented |
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only |
New or Pre-existing EM?
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Application of existing model | Application of existing model | WESP - Urban Stormwater Treatment | 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-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
Document ID for related EM
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Doc-343 | Doc-342 | None | Doc-390 | Doc-390 | Doc-13 | Doc-317 | Doc-366 | Doc-359 | Doc-456 | Doc-459 | Doc-461 |
EM ID for related EM
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EM-466 | EM-467 | EM-480 | EM-485 | None | EM-718 | EM-734 | EM-718 | EM-734 | EM-743 | EM-883 | EM-884 | EM-375 | EM-379 | EM-380 | EM-605 | EM-892 | EM-978 | EM-981 | EM-983 | EM-990 | EM-991 |
EM Modeling Approach
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
EM Temporal Extent
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1969-2011 | 1986-2115 | 2010-2011 | 2010-2012 | Not applicable | Not applicable |
EM Time Dependence
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time-dependent | time-dependent | time-dependent | time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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past time | future time | past time | past time | Not applicable | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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1 | 1 | Not applicable | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Year | Year | Not applicable | Not applicable | Day | Not applicable |
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
Bounding Type
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Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Not applicable |
Spatial Extent Name
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Yangjuangou catchment | Table Mountain National Park Marine Protected Area | Wetlands in idaho | Wetlands in idaho | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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1-10 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | Not applicable |
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable | Not applicable | Not applicable | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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30m x 30m | Not applicable | Not applicable | Not applicable | user defined | Not applicable |
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
EM Computational Approach
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Numeric | Numeric | Numeric | Numeric | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
Model Calibration Reported?
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Yes | No | No | No | Not applicable | Not applicable |
Model Goodness of Fit Reported?
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Yes ?Comment:For the year 2006 and 2011 |
No | No | No | Not applicable | 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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No |
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. |
No | No | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | No | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | No | No | Not applicable | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | 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-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
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None |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
Centroid Latitude
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36.7 | -34.18 | 44.06 | 44.06 | Not applicable | Not applicable |
Centroid Longitude
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109.52 | 18.35 | -114.69 | -114.69 | Not applicable | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
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Provided | Provided | Estimated | Estimated | Not applicable | Not applicable |
EM ID
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EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Inland Wetlands | 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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Loess plain | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | created, restored and enhanced wetlands | created, restored and enhanced wetlands | Terrestrial environment sub-classes | Multiple |
EM Ecological Scale
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Ecological scale is finer than that of 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 | Ecological scale is finer than that of 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-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
EM Organismal Scale
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Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
None Available |
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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)
EM-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
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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-469 |
EM-541 ![]() |
EM-729 ![]() |
EM-760 ![]() |
EM-887 | EM-985 |
None |
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None | None | None | None |