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-185 |
EM-333 
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EM-376 | EM-414 |
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EM Short Name
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Area and hotspots of soil retention, South Africa | Blue crabs and SAV, Chesapeake Bay, USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | MIMES: For Massachusetts Ocean (v1.0) | SAV occurrence, St. Louis River, MN/WI, USA |
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EM Full Name
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Area and hotspots of soil retention, South Africa | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA |
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EM Source or Collection
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None | None | Envision | US EPA | US EPA |
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EM Source Document ID
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271 |
292 ?Comment:Conference paper |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
316 | 330 |
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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. | Mykoniatis, N. and Ready, R. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Altman, I., R.Boumans, J. Roman, L. Kaufman | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke |
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Document Year
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2008 | 2013 | 2008 | 2012 | 2013 |
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Document Title
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Mapping ecosystem services for planning and management | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary |
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Document Status
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Peer reviewed and published | Not formally documented | Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Conference proceedings | Published journal manuscript | Published report | Published journal manuscript |
Software and Access
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
| Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | |
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Contact Name
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Benis Egoh | Nikolaos Mykoniatis | Michael R. Guzy | Irit Altman | Ted R. Angradi |
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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 | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | Oregon State University, Dept. of Biological and Ecological Engineering | Boston University, Portland, Maine | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA |
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Contact Email
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Not reported | Not reported | Not reported | iritaltman@bu.edu | angradi.theodore@epa.gov |
EM Description
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
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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: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | **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." | AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." |
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Specific Policy or Decision Context Cited
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None identified | Not applicable | 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 identified | None identified |
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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. | Submerged Aquatic Vegetation (SAV), eelgrass | No additional description provided | No additional description provided | submerged aquatic vegetation |
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EM Scenario Drivers
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No scenarios presented | Essential or Facultative habitat | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
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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 + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | Application of existing 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-185 |
EM-333
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EM-376 | EM-414 |
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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-227 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None | None |
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EM ID for related EM
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EM-85 | EM-87 | EM-88 | EM-106 | EM-12 | EM-369 | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
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EM Temporal Extent
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Not reported | 1993-2011 | 1990-2050 | Not applicable | 2010 - 2012 |
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EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | past time | future time | future time | Not applicable |
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EM Time Continuity
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Not applicable | discrete | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | 2 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Year | Year | Not applicable |
EM Spatial Extent
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
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Bounding Type
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Geopolitical | Physiographic or ecological | Geopolitical | Physiographic or ecological | Physiographic or ecological |
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Spatial Extent Name
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South Africa | Chesapeake Bay | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Massachusetts Ocean | St. Louis River Estuary |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 10-100 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
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Distributed across catchments with average size of 65,000 ha | Not applicable | varies | 1 km x1 km | 0.07 m^2 to 0.70 m^2 |
EM Structure and Computation Approach
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | stochastic | deterministic | 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-185 |
EM-333
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EM-376 | EM-414 |
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Model Calibration Reported?
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No | Yes | Unclear | No | Yes |
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Model Goodness of Fit Reported?
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No | Yes | No | No | Yes |
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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 | Yes | No | No | Yes |
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Model Uncertainty Analysis Reported?
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No | Yes | No | No | No |
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Model Sensitivity Analysis Reported?
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No | Yes | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Yes | 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-185 |
EM-333
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EM-376 | EM-414 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
| None |
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None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
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Centroid Latitude
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-30 | 36.99 | 44.11 | 41.72 | 46.72 |
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Centroid Longitude
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25 | -75.95 | -123.09 | -69.87 | -96.13 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | None | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds |
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Specific Environment Type
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Not reported | Yes | Agricultural-urban interface at river junction | None identified | Freshwater estuarine system |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Yes | 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-185 |
EM-333
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EM-376 | EM-414 |
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EM Organismal Scale
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Not applicable | Yes | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
| None Available | 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-185 |
EM-333
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EM-376 | EM-414 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-86 | EM-185 |
EM-333
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EM-376 | EM-414 |
| None | None |
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None |