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-154 | EM-985 |
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EM Short Name
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Annual profit - carbon plantings, South Australia | Mangrove development, Tampa Bay, FL, USA | Atlantis ecosystem assessment submodel |
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EM Full Name
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Annual profit from carbon plantings, South Australia | Mangrove wetland development, Tampa Bay, FL, 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 | US EPA | None |
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EM Source Document ID
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243 | 97 | 463 |
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Document Author
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Crossman, N. D., Bryan, B. A., and Summers, D. M. | Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L. | 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 | 2012 | 2011 |
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Document Title
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Carbon payments and low-cost conservation | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | 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 | 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 journal manuscript |
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EM ID
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EM-127 |
EM-154 | EM-985 |
| Not applicable | Not applicable | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
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Contact Name
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Neville D. Crossman | Michael Osland | Elizabeth Fulton |
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Contact Address
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CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | 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 | mosland@usgs.gov | beth.fulton@csiro.au |
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EM ID
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EM-127 |
EM-154 | 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." | ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses." | 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 | Not applicable | 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. | mangrove forest,Salt marsh, estuary, sea level, | 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 | Not applicable | No scenarios presented |
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EM ID
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EM-127 |
EM-154 | 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 | Method Only |
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New or Pre-existing EM?
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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
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EM ID
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EM-127 |
EM-154 | EM-985 |
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Document ID for related EM
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Doc-245 | Doc-246 | Doc-247 | Doc-243 | None | Doc-456 | Doc-459 | Doc-461 |
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EM ID for related EM
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EM-128 | EM-141 | None | 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-154 | EM-985 |
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EM Temporal Extent
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2009-2050 | 1990-2010 | 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 | continuous | Not applicable |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable |
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EM ID
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EM-127 |
EM-154 | EM-985 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Not applicable |
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Spatial Extent Name
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Agricultural districts of the state of South Australia | Tampa Bay | Not applicable |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 100-1000 km^2 | Not applicable |
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EM ID
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EM-127 |
EM-154 | 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 | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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1 ha x 1 ha | m^2 | Not applicable |
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EM ID
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EM-127 |
EM-154 | EM-985 |
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EM Computational Approach
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Analytic | Analytic | 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-154 | EM-985 |
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Model Calibration Reported?
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No | No | Not applicable |
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Model Goodness of Fit Reported?
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No | No | 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 | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | Yes | Not applicable |
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Model Sensitivity Analysis Reported?
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No | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | No | 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-154 | EM-985 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-127 |
EM-154 | EM-985 |
| None |
Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian |
None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-127 |
EM-154 | EM-985 |
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Centroid Latitude
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-34.9 | 27.8 | Not applicable |
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Centroid Longitude
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138.7 | -82.4 | 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-154 | EM-985 |
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EM Environmental Sub-Class
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Agroecosystems | Near Coastal Marine and Estuarine | 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 | Created Mangrove wetlands | 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-154 | 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-154 | EM-985 |
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None Available |
EnviroAtlas URL
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EM-127 |
EM-154 | EM-985 |
| Carbon Storage by Tree Biomass | None Available | Big game hunting recreation demand, Percent GAP Status 1 & 2, Total Employment |
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-154 | 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)
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EM-127 |
EM-154 | EM-985 |
| None |
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None |
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