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
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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EM Short Name
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Annual profit - carbon plantings, South Australia | Envision, Puget Sound, WA, USA | Atlantis ecosystem assessment submodel |
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EM Full Name
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Annual profit from carbon plantings, South Australia | Envision, Puget Sound, WA, USA | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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EM Source or Collection
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None | Envision | None |
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EM Source Document ID
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243 |
313 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
463 |
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Document Author
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Crossman, N. D., Bryan, B. A., and Summers, D. M. | Bolte, J. and Vache, K. | 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. |
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Document Year
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2011 | 2010 | 2011 |
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Document Title
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Carbon payments and low-cost conservation | Envisioning Puget Sound Alternative Futures: PSNERP Final Report | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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Document Status
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Peer reviewed and published | Documentation is peer-reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published report | Published journal manuscript |
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EM ID
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EM-127 |
EM-369 |
EM-985 |
| Not applicable | http://envision.bioe.orst.edu | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
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Contact Name
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Neville D. Crossman |
John Bolte ?Comment:Phone# 541-737-2041 |
Elizabeth Fulton |
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Contact Address
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CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333 | CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia |
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Contact Email
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neville.crossman@csiro.au | boltej@engr.orst.edu | beth.fulton@csiro.au |
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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Summary Description
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ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially." | SUMMARY: "...the Puget Sound Nearshore Ecosystem Restoration Project, completed an analysis of alternative future regional trajectories of landscape change for the Puget Sound region. This effort developed three scenarios of change: 1) Status Quo, reflecting a continuation of current trends in the region, 2) Managed Growth, reflecting the adoption of an aggressive set of land use management policies focusing on protecting and restoring ecosystem function and concentrating growth within Urban Growth Areas (UGA) and near regional growth centers, and 3) Unmanaged Growth, reflecting a relaxation of land use restrictions with limited protection of ecosystem functions. Analyses assumed a fixed population growth rate across all three scenarios, defined by the Washington Office of Financial Management county level growth estimates. Scenarios were generated using a spatially- and temporally-explicit alternative futures analysis model, Envision, previously developed by Oregon State University researchers. The model accepts as input a vector-based representation of the landscape and associated datasets describing relevant landscape characteristics, descriptors of various processes influencing landscape change, and a set of policies, or decision alternatives, which reflect scenario-specific land management alternatives. The model generates 1) a set of spatial coverages (maps) reflecting scenario outcomes of a variety of landscape variables, most notably land use/land cover, shoreline modifications, and population projections, and 2) a set of summary statistics describing landscape change variables summarized across spatial reporting units. Analyses were run on each of such sub-basins in the Puget Sound, and aggregated to providing Sound-wide results. This information is being used by PSNERP to project future impairment of ecosystem functions, goods, and services. The Puget Sound Nearshore Ecosystem project data also provide inputs to calculate aspects of future nearshore process degradation. Impairment and degradation are primary factors being used to define future conditions for the PSNERP General Investigation Study." AUTHOR'S DESCRIPTION: "In this report, we document the application of an alternative futures analysis framework that incorporates these capabilities to the analysis of alternative future trajectories in the Puget Sound region. This framework, Envision (Bolte et al, 2007; Hulse et al. 2008) is a spatially and temporally explicit, standards-based, open source toolset specifically designed to facilitate alternative futures analyses. It employs a multiagent-based modeling approach that contains a robust capability for defining alternative management strategies and scenarios, incorporating a variety of landscape change processes, and creating maps of alternative landscape trajectories, expressed though a variety of metrics defined in an application-specific way." ABOUT ENVISION (ENVISION WEBSITE): "Central to Envision, and conceived at the s | 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. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
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Biophysical Context
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Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | No additional description provided | N/A |
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EM Scenario Drivers
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Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e | Alternative future land management strategies (status quo, managed growth, unmanaged growth) | No scenarios presented |
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
Method + Application (multiple runs exist) View EM Runs | Method Only |
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New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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Document ID for related EM
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Doc-245 | Doc-246 | Doc-247 | Doc-243 |
Doc-314 | Doc-47 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
Doc-456 | Doc-459 | Doc-461 |
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EM ID for related EM
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EM-128 | EM-141 | EM-12 | EM-333 | EM-978 | EM-981 | EM-983 | EM-990 | EM-991 |
EM Modeling Approach
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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EM Temporal Extent
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2009-2050 | 2000-2060 | Not applicable |
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EM Time Dependence
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time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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future time | future time | Not applicable |
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EM Time Continuity
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discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Year | Year | Not applicable |
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Not applicable |
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Spatial Extent Name
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Agricultural districts of the state of South Australia | Puget Sound watershed | Not applicable |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable |
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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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) | Not applicable |
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Spatial Grain Type
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area, for pixel or radial feature | Irregular | Not applicable |
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Spatial Grain Size
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1 ha x 1 ha | Varies | Not applicable |
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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EM Computational Approach
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Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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Model Calibration Reported?
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No | Unclear | Not applicable |
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Model Goodness of Fit Reported?
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No | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | Not applicable | Not applicable |
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Model Uncertainty Analysis Reported?
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No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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No | Not applicable | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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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-127 |
EM-369 |
EM-985 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-127 |
EM-369 |
EM-985 |
| None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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Centroid Latitude
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-34.9 | 47.58 | Not applicable |
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Centroid Longitude
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138.7 | -122.32 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Not applicable |
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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EM Environmental Sub-Class
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Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | 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 |
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Specific Environment Type
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Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Pacific NW US region, coastal to montane, urban to rural | Multiple |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-127 |
EM-369 |
EM-985 |
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EM Organismal Scale
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Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-127 |
EM-369 |
EM-985 |
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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)
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EM-127 |
EM-369 |
EM-985 |
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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)
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EM-127 |
EM-369 |
EM-985 |
| None | None | None |
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