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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-102 
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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EM Short Name
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Area and hotspots of soil retention, South Africa | Fish species habitat value, Tampa Bay, FL, USA | Urban Temperature, Baltimore, MD, USA | ARIES flood regulation, Puget Sound Region, USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | SAV occurrence, St. Louis River, MN/WI, USA | APEX v1501 | Chinook salmon value (household), Yaquina Bay, OR | Grasshopper Sparrow density, CREP, Iowa, USA | Dickcissel density, CREP, Iowa, USA | Estuary visitation, Cape Cod, MA | WESP: Riparian & stream habitat, ID, USA | Home ownership, Great Lakes, USA | ESTIMAP - Pollination potential, Iran |
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EM Full Name
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Area and hotspots of soil retention, South Africa | Fish species habitat value, Tampa Bay, FL, USA | Urban Air Temperature Change, Baltimore, MD, USA | ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | APEX (Agricultural Policy/Environmental eXtender Model) v1501 | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Grasshopper Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Dickcissel population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Value of recreational use of an estuary, Cape Cod, Massachusetts | WESP: Riparian and stream habitat focus projects, ID, USA | Human well being indicator - home ownership, Great Lakes waterfront, USA | ESTIMAP - Pollination potential, Iran |
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EM Source or Collection
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None | US EPA | i-Tree | USDA Forest Service | ARIES | Envision | US EPA | None | US EPA | None | None | US EPA | None | US EPA | None |
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EM Source Document ID
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271 | 187 | 217 | 302 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
330 | 357 | 324 | 372 | 372 | 387 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
434 |
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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. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Steglich, E. M., J. Jeong and J. R. Williams | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | 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 | 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 | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Murphy, C. and T. Weekley | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Rahimi, E., Barghjelveh, S., and P. Dong |
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Document Year
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2008 | 2016 | 2016 | 2014 | 2008 | 2013 | 2016 | 2012 | 2010 | 2010 | 2019 | 2012 | None | 2020 |
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Document Title
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Mapping ecosystem services for planning and management | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Agricultural Policy/Environmental eXtender Model User's Manual Version 1501 | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) |
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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 | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | 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 journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published report | Published report | Draft manuscript-work progressing | Published report | Journal manuscript submitted or in review | Published journal manuscript |
Software and Access
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EM ID
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
| Not applicable | Not applicable | Not applicable | http://aries.integratedmodelling.org/ | http://evoland.bioe.orst.edu/ | Not applicable | https://epicapex.tamu.edu/manuals-and-publications/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Benis Egoh | Richard Fulford | Gordon M. Heisler | Ken Bagstad | Michael R. Guzy | Ted R. Angradi | E. M. Steglich | Stephen Jordan | David Otis | David Otis | Mulvaney, Kate | Chris Murphy | Ted Angradi | Ehsan Rahini |
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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 | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Geosciences and Environmental Change Science Center, US Geological Survey | Oregon State University, Dept. of Biological and Ecological Engineering | 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 | Blackland Research and Extension Center, 720 East Blackland Road, Temple, TX 76502 | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
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Contact Email
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Not reported | Fulford.Richard@epa.gov | gheisler@fs.fed.us | kjbagstad@usgs.gov | Not reported | angradi.theodore@epa.gov | epicapex@brc.tamus.edu | jordan.steve@epa.gov | dotis@iastate.edu | dotis@iastate.edu | None reported | chris.murphy@idfg.idaho.gov | tedangradi@gmail.com | ehsanrahimi666@gmail.com |
EM Description
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EM ID
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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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: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | 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: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures." | **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." | 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." | ABSTRACT: "APEX is a tool for managing whole farms or small watersheds to obtain sustainable production efficiency and maintain environmental quality. APEX operates on a daily time step and is capable of performing long term simulations (1-4000 years) at the whole farm or small watershed level. The watershed may be divided into many homogeneous (soils, land use, topography, etc.) subareas (<4000). The routing component simulates flow from one subarea to another through channels and flood plains to the watershed outlet and transports sediment, nutrients, and pesticides. This allows evaluation of interactions between fields in respect to surface run-on, sediment deposition and degradation, nutrient and pesticide transport and subsurface flow. Effects of terrace systems, grass waterways, strip cropping, buffer strips/vegetated filter strips, crop rotations, plant competition, plant burning, grazing patterns of multiple herds, fertilizer, irrigation, liming, furrow diking, drainage systems, and manure management (feed yards and dairies with or without lagoons) can be simulated and assessed. Most recent developments in APEX1501 include: • Flexible grazing schedule of multiple owners and herds across landscape and paddocks. • Wind dust distribution from feedlots. • Manure erosion from feedlots and grazing fields. • Optional pipe and crack flow in soil due to tree root growth. • Enhanced filter strip consideration. • Extended lagoon pumping and manure scraping options. • Enhanced burning operation. • Carbon pools and transformation equations similar to those in the Century model with the addition of the Phoenix C/N microbial biomass model. • Enhanced water table monitoring. • Enhanced denitrification methods. • Variable saturation hydraulic conductivity method. • Irrigation using reservoir and well reserves. • Paddy module for use with rice or wetland areas." | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | 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 Grasshopper Sparrow (Ammodramus savannarum)... 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: GRSP density = e (-2.554612 + 0.0246975 * grass400 – 0.1032461 * trees400) | 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: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." |
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Specific Policy or Decision Context Cited
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None identified | None identifed | 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 identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported |
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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. | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | 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. | No additional description provided | No additional description provided | submerged aquatic vegetation | No additional description provided | Yaquina Bay estuary | Prairie pothole region of north-central Iowa | Prairie pothole region of north-central Iowa | None identified | restored, enhanced and created wetlands | Waterfront districts on south Lake Michigan and south lake Erie | None additional |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | No scenarios presented | 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 | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | Sites, function or habitat focus | N/A | N/A |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application | Method + Application | 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?
em.detail.newOrExistHelp
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | Application of existing model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM Modeling Approach
EM Relationship to Time
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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EM Temporal Extent
em.detail.tempExtentHelp
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Not reported | 2006-2011 | May 5-Sept 30 2006 | 1971-2006 | 1990-2050 | 2010 - 2012 | Not applicable | 2003-2008 | 2002-2007 | 1992-2007 | Summer 2017 | 2010-2011 | 2022 | 2020 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | 1 | Not applicable | 2 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Hour | Not applicable | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Geopolitical | Physiographic or ecological | Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Geopolitical |
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Spatial Extent Name
em.detail.extentNameHelp
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South Africa | Tampa Bay | Baltimore, MD | Puget Sound Region | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | St. Louis River Estuary | Not applicable | Pacific Northwest | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | Three Bays, Cape Cod | Wetlands in idaho | Great Lakes waterfront | Iran |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 10-100 km^2 | Not applicable | >1,000,000 km^2 | 1-10 km^2 | 1-10 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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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) | 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. |
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) | spatially lumped (in all cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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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 | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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Distributed across catchments with average size of 65,000 ha | 1 km^2 | 10m x 10m | 200m x 200m | varies | 0.07 m^2 to 0.70 m^2 | homogenous subareas | Not applicable | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | beach length | Not applicable | Not applicable | ha^2 |
EM Structure and Computation Approach
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Yes | No | Unclear | Yes | Not applicable | No | Unclear | Unclear | Yes | No | No | No |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | Yes | No | No | Yes | Not applicable | No | No | No | No | No | No | No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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None | None |
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None | None | None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | No | Yes | Not applicable | Yes | Unclear | Unclear | No | No | No | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | Not applicable | No | No | No | No | No | Yes | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 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])
em.detail.relationToSpaceTerrestrialHelp
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| EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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None |
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None |
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Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
|
EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
| None |
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None | None | None | None | None |
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None | None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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Centroid Latitude
em.detail.ddLatHelp
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-30 | 27.74 | 39.28 | 48 | 44.11 | 46.72 | Not applicable | 44.62 | 42.62 | 42.62 | 41.62 | 44.06 | 42.26 | 32.29 |
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Centroid Longitude
em.detail.ddLongHelp
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25 | -82.57 | -76.62 | -123 | -123.09 | -96.13 | Not applicable | -124.02 | -93.84 | -93.84 | -70.42 | -114.69 | -87.84 | 53.68 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Agroecosystems | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Not reported | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Urban landscape and surrounding area | Terrestrial environment surrounding a large estuary | Agricultural-urban interface at river junction | Freshwater estuarine system | Terrestrial environment associated with agroecosystems | Yaquina Bay estuary and ocean | Grassland buffering inland wetlands set in agricultural land | Grassland buffering inland wetlands set in agricultural land | Beaches | created, restored and enhanced wetlands | Lake Michigan & Lake Erie waterfront | terrestrial land types |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Zone within an ecosystem | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to 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 | Ecological scale corresponds to 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 | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
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EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Species | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Other (multiple scales) | Species | Species | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-86 |
EM-102
|
EM-306 | EM-326 |
EM-333
|
EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
|
EM-891 | EM-941 |
| None Available |
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None Available | None Available |
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None Available | None Available |
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None Available | None Available | 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)
em.detail.cicesHelp
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| EM-86 |
EM-102
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EM-306 | EM-326 |
EM-333
|
EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-86 |
EM-102
|
EM-306 | EM-326 |
EM-333
|
EM-414 | EM-592 | EM-604 | EM-649 | EM-651 | EM-683 |
EM-718
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EM-891 | EM-941 |
| None |
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None |
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None | None |
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