EcoService Models Library (ESML)
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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-86 | EM-126 |
EM-333 
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EM-434 |
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EM Short Name
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Area and hotspots of soil retention, South Africa | Annual profit from agriculture, South Australia | Evoland v3.5 (unbounded growth), Eugene, OR, USA | Land capability classification |
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EM Full Name
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Area and hotspots of soil retention, South Africa | Annual profit from agriculture, South Australia | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Land capability classification |
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EM Source or Collection
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None | None | Envision | None |
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EM Source Document ID
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271 | 243 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
340 |
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Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | United States Department of Agriculture - Natural Resources Conservation Service |
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Document Year
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2008 | 2011 | 2008 | 2013 |
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Document Title
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Mapping ecosystem services for planning and management | Carbon payments and low-cost conservation | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | National Soil Survey Handbook - Part 622 - Interpretative Groups |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report |
Software and Access
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
| Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | Not applicable | |
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Contact Name
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Benis Egoh | Neville D. Crossman | Michael R. Guzy | United States Department of Agriculture |
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Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Oregon State University, Dept. of Biological and Ecological Engineering | Not reported |
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Contact Email
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Not reported | neville.crossman@csiro.au | Not reported | http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ |
EM Description
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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Summary Description
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AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | 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 agriculture and from carbon plantings." 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). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] |
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Specific Policy or Decision Context Cited
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None identified | None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None provided |
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Biophysical Context
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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | 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 | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | 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 | New or revised model | New or revised model | New or revised 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-86 | EM-126 |
EM-333
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EM-434 |
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Document ID for related EM
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Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
Doc-244 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None |
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EM ID for related EM
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EM-85 | EM-87 | EM-88 | None | EM-12 | EM-369 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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EM Temporal Extent
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Not reported | 2002-2008 | 1990-2050 | Not applicable |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | Not applicable |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 2 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Not applicable |
EM Spatial Extent
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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Bounding Type
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Geopolitical | Physiographic or Ecological | Geopolitical | Not applicable |
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Spatial Extent Name
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South Africa | Agricultural districts of the state of South Australia | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Not applicable |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | 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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Distributed across catchments with average size of 65,000 ha | 1 ha | varies | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Not applicable |
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EM Determinism
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deterministic | deterministic | stochastic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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Model Calibration Reported?
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No | No | Unclear | Not applicable |
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Model Goodness of Fit Reported?
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No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-86 | EM-126 |
EM-333
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EM-434 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-86 | EM-126 |
EM-333
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EM-434 |
| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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Centroid Latitude
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-30 | -34.9 | 44.11 | Not applicable |
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Centroid Longitude
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25 | 138.7 | -123.09 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Not reported | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Agricultural-urban interface at river junction | None identified |
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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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-86 | EM-126 |
EM-333
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EM-434 |
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EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-86 | EM-126 |
EM-333
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EM-434 |
| None Available |
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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-86 | EM-126 |
EM-333
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EM-434 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-86 | EM-126 |
EM-333
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EM-434 |
| None | None |
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