EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
EM Short Name
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Area and hotspots of soil retention, South Africa | Fish species habitat value, Tampa Bay, FL, USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | InVEST (v1.004) Carbon, Indonesia | InVEST (v1.004) sediment retention, Indonesia | InVEST (v1.004) water purification, Indonesia | SAV occurrence, St. Louis River, MN/WI, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico | Visitation to reef dive sites, St. Croix, USVI | Yasso 15 - soil carbon model | Yasso07 - SOC, Loess Plateau, China | EnviroAtlas-Carbon sequestered by trees | Chinook salmon value (household), Yaquina Bay, OR | VELMA v2.0, Ohio, USA | N removal by wetland restoration, Midwest, USA | Natural amenities and population migration, USA | RUM: Valuing fishing quality, Michigan, USA | WESP: Riparian & stream habitat, ID, USA | Plant-pollinator networks at reclaimed mine, USA | Bird species diversity on restored landfills, UK | ARIES: Crop pollination in Rwanda and Burundi | Valuing environmental ed., New York, New York |
EM Full Name
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Area and hotspots of soil retention, South Africa | Fish species habitat value, Tampa Bay, FL, USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004) carbon storage and sequestration, Sumatra, Indonesia | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) water purification (nutrient retention), Sumatra, Indonesia | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | Visitation to dive sites (reef), St. Croix, USVI | Yasso 15 - soil carbon | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Natural amenities and rural population migration, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | WESP: Riparian and stream habitat focus projects, ID, USA | Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA | Bird species diversity on restored landfills compared to paired reference sites, East Midlands, UK | ARIES; Crop pollination in Rwanda and Burundi | Valuing environmental education, Hudson River Park, New York, New York |
EM Source or Collection
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None | US EPA | Envision | InVEST | InVEST | InVEST | US EPA | US EPA | US EPA | None | None | US EPA | EnviroAtlas | i-Tree | US EPA | US EPA | None | USDA Forest Service | None | None | None | None | ARIES | None |
EM Source Document ID
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271 | 187 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
309 | 309 | 309 | 330 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
335 |
342 ?Comment:Webpage pdf users manual for model. |
344 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
324 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
375 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
397 | 406 | 411 | 416 |
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. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | US EPA Office of Research and Development - National Exposure Research Laboratory | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Cordell H. K., V. Heboyan, F. Santos, J. C. Bergstrom | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Murphy, C. and T. Weekley | Cusser, S. and K. Goodell | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Hutcheson, W. Hoagland, P., and D. Jin |
Document Year
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2008 | 2016 | 2008 | 2014 | 2014 | 2014 | 2013 | 2017 | 2014 | 2016 | 2015 | 2013 | 2012 | 2018 | 2006 | 2011 | 2014 | 2012 | 2013 | 2011 | 2018 | 2018 |
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 | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Yasso 15 graphical user-interface manual | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | EnviroAtlas - Featured Community | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Natural amenities and rural population migration | Valuing recreational fishing quality at rivers and streams | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | Towards globally customizable ecosystem service models | Valuing environmental education as a cultural ecosystem service at Hudson River Park |
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 | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | 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 |
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 journal manuscript | Published journal manuscript | Published journal manuscript | Not applicable | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://github.com/integratedmodelling/im.aries.global | Not applicable | |
Contact Name
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Benis Egoh | Richard Fulford | Michael R. Guzy | Nirmal K. Bhagabati | Nirmal K. Bhagabati | Nirmal K. Bhagabati | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Jari Liski | Xing Wu | EnviroAtlas Team | Stephen Jordan | Heather Golden | William G. Crumpton | Ken Cordell | Richard Melstrom | Chris Murphy |
Sarah Cusser ?Comment:Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A. |
Lutfor Rahman | Javier Martinez | Walter Hutcheson |
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 | Oregon State University, Dept. of Biological and Ecological Engineering | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | 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 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | Chinese Academy of Sciences, Beijing 100085, China | Not reported | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | U.S. Department of Agriculture, Forest Service, Southern Research Station, Athens, GA 30602 | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A. | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | New York University, United States |
Contact Email
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Not reported | Fulford.Richard@epa.gov | Not reported | nirmal.bhagabati@wwfus.org | nirmal.bhagabati@wwfus.org | nirmal.bhagabati@wwfus.org | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | jari.liski@ymparisto.fi | xingwu@rceesac.cn | enviroatlas@epa.gov | jordan.steve@epa.gov | Golden.Heather@epa.gov | crumpton@iastate.edu | Not reported | melstrom@okstate.edu | chris.murphy@idfg.idaho.gov | sarah.cusser@gmail.com | lutfor.rahman@northampton.ac.uk | javier.martinez@bc3research.org | wwh235@nyu.edu |
EM ID
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
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." | **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." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... We mapped biomass carbon by assigning carbon values (in ton ha_1) for aboveground, belowground, and dead organic matter to each LULC class based on values from literature, as described in Tallis et al. (2010). We mapped soil carbon separately, as large quantities of carbon are stored in peat soil (Page et al., 2011). We estimated total losses in peat carbon over 50 years into the future scenarios, using reported annual emission rates for specific LULC transitions on peat (Uryu et al., 2008)...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... Our nutrient retention model estimates nitrogen and phosphorus loading (kg y^-1), leading causes of water pollution from fertilizer application and other activities, using the export coefficient approach of Reckhow et al. (1980). The model routes nutrient runoff from each land parcel downslope along the flow path, with some of the nutrient that originated upstream being retained by the parcel according to its retention efficiency. For assessing variation within the same LULC map (2008 and each scenario), we compared sediment and nutrient retention across the landscape. However, for assessing change to scenarios, we compared sediment and nutrient export between the relevant LULC maps, as the change in export (rather than in retention) better reflects the change in service experienced downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | 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." | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | 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: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | ABSTRACT: "Research suggests that significant relationships exist between rural population change and natural amenities. Thus, understanding and predicting domestic migration trends as a function of changes in natural amenities is important for effective regional growth and development policies and strategies. In this study, we first estimated an econometric model which showed the effects of natural amenities, such as climate and landscape variables, on rural population migration patterns in the United States between 1990 and 2007. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections. Results suggest that people prefer rural areas with mild winters and cooler summers; thus we can expect a direct impact of climate change on population migration when areas associated with these conditions change. Results also suggest preference for varied landscapes that feature a mix of forest land and open space (e g , pasture and range land). During the projection period from 2010 to 2060 in the United States, changes in natural amenities were predicted to have positive effects on rural population migration trends in most parts of the Intermountain and Pacific Northwest regions, and some parts of the Southeastern, South Central, and Northeastern U S regions (e g , Southern Appalachian Mountains, Ozark Mountains, northern New England). Changes in natural amenities were predicted to have negative effects on rural population migration trends during the projection period in Midwestern regions (e g , Great Plains and North Central regions)." AUTHOR'S DESCRIPTION: "This model was estimated for 2,014 rural counties in the continental United States using various national data bases and sources. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections." | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | 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: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context." | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | [Abstract:Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.] | ABSTRACT: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. " |
Specific Policy or Decision Context Cited
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None identified | None identifed | 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…" | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | None provided | None identified | None identified | None | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified |
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 | No additional description provided | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | submerged aquatic vegetation | No additional description provided | No additional description provided | Not applicable | Agricultural plain, hills, gulleys, forest, grassland, Central China | No additional description provided | Yaquina Bay estuary | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | No additional description provided | No additional description provided | stream and river reaches of Michigan | restored, enhanced and created wetlands | The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils. | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | Entire countries of Rwanda and Burundi considered | N/A |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Land use change | No scenarios presented | No scenarios presented | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | More conservative, average and less conservative nitrate loss rate | Climate projections based on the CGCM 3 1 general circulation model of moderate warming (IPCC). The A1B scenario assumes a growing world population that peaks in the mid-century and balanced technological growth. | targeted sport fish biomass | Sites, function or habitat focus | No scenarios presented | No scenarios presented | N/A | N?A |
EM ID
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EM-86 |
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EM-837 | EM-855 | EM-875 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Application of existing 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 | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-86 |
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EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
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EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
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EM-627 | EM-653 |
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EM Temporal Extent
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Not reported | 2006-2011 | 1990-2050 | 2008-2020 | 2008-2020 | 2008-2020 | 2010 - 2012 | 1978 - 2009 | 2006-2007, 2010 | Not applicable | 1969-2011 | 2010-2013 | 2003-2008 | Jan 1, 2009 to Dec 31, 2011 | 1973-1999 | 1982-2060 | 2008-2010 | 2010-2011 | 2009-2010 | Not applicable | 2010 | 2015 |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | past time | future time | future time | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | 2 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Day | Day | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Not applicable | Geopolitical | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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South Africa | Tampa Bay | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | central Sumatra | central Sumatra | central Sumatra | St. Louis River Estuary | Guanica Bay watershed | Coastal zone surrounding St. Croix | Not applicable | Yangjuangou catchment | Durham NC and vicinity | Pacific Northwest | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | Upper Mississippi River and Ohio River basins | continental United States | HUCS in Michigan | Wetlands in idaho | The Wilds | Not applicable | Rwanda and Burndi | Hudson River Park |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | Not applicable | 1-10 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 10-100 ha | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 1-10 km^2 | Not applicable | 10,000-100,000 km^2 | 10-100 ha |
EM ID
em.detail.idHelp
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
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) |
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 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) ?Comment:Census block groups |
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 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) |
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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | map scale, for cartographic feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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Distributed across catchments with average size of 65,000 ha | 1 km^2 | varies | 30 m x 30 m | 30 m x 30 m | 30 m x 30 m | 0.07 m^2 to 0.70 m^2 | HUC | 10 m x 10 m | Not applicable | 30m x 30m | irregular | Not applicable | 10m x 10m | 1 km2 | varies | reach in HUC | Not applicable | 10 m radius | multiple unrelated sites | 1km | Not applicable |
EM ID
em.detail.idHelp
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Numeric | Numeric | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Unclear | No | No | No | Yes | No | Yes | Not applicable | Yes | No | No | Yes | No | Yes | No | No | Not applicable | Not applicable | Unclear | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No | Yes | No | No | Not applicable |
Yes ?Comment:For the year 2006 and 2011 |
No | No |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
No | No | Yes | No | Not applicable | Not applicable | No | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None |
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None | None | None |
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None | None | None | None | None |
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None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | No | No | Yes | No | Yes | Not applicable | No | No | Yes | Yes |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | No | No | Yes | Not applicable | No | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | Yes | Not applicable | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | Not applicable | No | No |
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 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
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None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
None |
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None | None | None | None | None | None |
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None | None | None |
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None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
Centroid Latitude
em.detail.ddLatHelp
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-30 | 27.74 | 44.11 | 0 | 0 | 0 | 46.72 | 17.96 | 17.73 | Not applicable | 36.7 | 35.99 | 44.62 | 39.19 | 40.6 | 39.8 | 45.12 | 44.06 | 39.82 | Not applicable | -2.59 | 40.73 |
Centroid Longitude
em.detail.ddLongHelp
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25 | -82.57 | -123.09 | 102 | 102 | 102 | -96.13 | -67.02 | -64.77 | Not applicable | 109.52 | -78.96 | -124.02 | -84.29 | -88.4 | -98.55 | 85.18 | -114.69 | -81.75 | Not applicable | 29.97 | -74.01 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Provided | Provided | Provided | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Forests | Grasslands | Scrubland/Shrubland | Tundra | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Inland Wetlands | Agroecosystems | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Rivers and Streams | Inland Wetlands | Grasslands | Created Greenspace | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Not reported | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Agricultural-urban interface at river junction | 104 land use land cover classes | 104 land use land cover classes | 104 land use land cover classes | Freshwater estuarine system | Tropical terrestrial | Coral reefs | Not applicable | Loess plain | Urban and vicinity | Yaquina Bay estuary and ocean | Mixed land cover suburban watershed | Agroecosystems and associated drainage and wetlands | Terrestrial environments including water bodies and coastlines | stream reaches | created, restored and enhanced wetlands | Grassland | restored landfills and conserved grasslands | varied | Park |
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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Species | Not applicable | Community | Community | Community | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Other (multiple scales) | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Species | Individual or population, within a species | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-86 |
EM-102 ![]() |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-493 | EM-604 |
EM-605 ![]() |
EM-627 | EM-653 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-837 | EM-855 | EM-875 |
None |
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None | None | None | None |
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None | None | None |
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None |
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None | None | None | None |
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