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-12 
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EM-306 | EM-651 | EM-682 | EM-846 |
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EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | Urban Temperature, Baltimore, MD, USA | Dickcissel density, CREP, Iowa, USA | WTP for a beach day, Massachusetts, USA | Indigo bunting abund, Piedmont region, USA |
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EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | Urban Air Temperature Change, Baltimore, MD, USA | Dickcissel population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA | Indigo bunting abundance, Piedmont ecoregion, USA |
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EM Source or Collection
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Envision | i-Tree | USDA Forest Service | None | US EPA | None |
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EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
217 | 372 | 386 | 405 |
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Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Riffel, S., Scognamillo, D., and L. W. Burger |
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Document Year
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2008 | 2016 | 2010 | 2018 | 2008 |
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Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds |
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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 | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Michael R. Guzy | Gordon M. Heisler | David Otis | Kate K, Mulvaney | Sam Riffell |
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Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA |
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Contact Email
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Not reported | gheisler@fs.fed.us | dotis@iastate.edu | Mulvaney.Kate@EPA.gov | sriffell@cfr.msstate.edu |
EM Description
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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Summary Description
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**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." | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Dickcissel (Spiza americana)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: DICK density = 1-1/1+e^(-6.811334 + 1.889878 * bbspath) * e^(-1.831015 + 0.0312571 * hay400) | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "We used existing studies in a meta-analysis to estimate appropriate benefit transfer values of consumer surplus per beach visit for Barnstable. The studies we include in the model are for beaches across the United States, allowing the metaregression model to be more broadly applicable to other beaches and for values to be adjusted based on appropriate site attributes...To identify relevant studies, we selected 25 studies of beach use and swimming from the Recreation Use Values Database (RUVD), where consumer surplus values are presented as value per day in 2016 dollars...We added beach length and history of closures to contextualize the model for our application by proxying water quality and site quality." Equation 1, page 11, provides the meta-regression. | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." |
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Specific Policy or Decision Context Cited
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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 | Economic value of protecting coastal beach water quality from contamination caused closures. | None reported |
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Biophysical Context
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No additional description provided | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | Prairie pothole region of north-central Iowa | Four separate beaches within the community of Barnstable | Conservation Reserve Program lands left to go fallow |
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EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application |
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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 ?Comment:Models developed by Quamen (2007). |
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-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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Document ID for related EM
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Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-220 | Doc-219 | Doc-218 | Doc-372 | Doc-386 | Doc-387 | Doc-405 |
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EM ID for related EM
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EM-333 | EM-369 | None | EM-652 | EM-650 | EM-649 | EM-648 | EM-684 | EM-685 | EM-683 | EM-686 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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EM Temporal Extent
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1990-2050 | May 5-Sept 30 2006 | 1992-2007 | July 1, 2011 to June 31, 2016 | 2008 |
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EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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future time | future time | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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2 | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Hour | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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Bounding Type
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Geopolitical | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Physiographic or ecological |
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Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Baltimore, MD | CREP (Conservation Reserve Enhancement Program) wetland sites | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Piedmont Ecoregion |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 1-10 km^2 | 10-100 ha | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable |
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Spatial Grain Size
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varies | 10m x 10m | multiple, individual, irregular shaped sites | by beach site | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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stochastic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Comment:Agent based modeling results in response indices. |
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Model Checking Procedures Used
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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Model Calibration Reported?
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Unclear | Yes | Unclear | Yes | Yes |
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Model Goodness of Fit Reported?
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No | Yes | No | Yes | No |
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Goodness of Fit (metric| value | unit)
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None |
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None |
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None |
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Model Operational Validation Reported?
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No | No | Unclear | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | No |
Yes ?Comment:p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables. |
Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | 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-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
| None | None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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Centroid Latitude
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44.11 | 39.28 | 42.62 | 41.64 | 36.23 |
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Centroid Longitude
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-123.09 | -76.62 | -93.84 | -70.29 | -81.9 |
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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-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Inland Wetlands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Grasslands |
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Specific Environment Type
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Agricultural-urban interface at river junction | Urban landscape and surrounding area | Grassland buffering inland wetlands set in agricultural land | Saltwater beach | grasslands |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to 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-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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EM Organismal Scale
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Not applicable | Not applicable | Species | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
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EM-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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None Available |
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None Available |
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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-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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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-12
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EM-306 | EM-651 | EM-682 | EM-846 |
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