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-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
EM Short Name
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EnviroAtlas-Air pollutant removal | Area and hotspots of soil retention, South Africa | RHyME2, Upper Mississippi River basin, USA | Fish species habitat value, Tampa Bay, FL, USA | EnviroAtlas - Water recharge | ARIES carbon, Puget Sound Region, USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | InVEST habitat quality, Puli Township, Taiwan | 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 | Yasso07 - SOC, Loess Plateau, China | Chinook salmon value (household), Yaquina Bay, OR | N removal by wetland restoration, Midwest, USA | RUM: Valuing fishing quality, Michigan, USA | Alewife derived nutrients, Connecticut, USA | WESP: Riparian & stream habitat, ID, USA | Plant-pollinator networks at reclaimed mine, USA | HWB indicator-poor mental health, Great Lakes, USA | EPA national stormwater calculator tool |
EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Area and hotspots of soil retention, South Africa | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Fish species habitat value, Tampa Bay, FL, USA | US EPA EnviroAtlas - Annual water recharge by tree cover; Example is shown for Durham NC and vicinity, USA | ARIES (Artificial Intelligence for Ecosystem Services) Carbon Storage and Sequestration, Puget Sound Region, Washington, USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan | 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 | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Alewife derived nutrients in stream food web, Connecticut, USA | WESP: Riparian and stream habitat focus projects, ID, USA | Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA | Human well being indicator-poor mental health,Great Lakes waterfront, USA | Environmental Protection Agency National stormwater calculator tool |
EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
None | US EPA | US EPA |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
ARIES | Envision | InVEST | InVEST | InVEST | InVEST | US EPA | US EPA | US EPA | None | US EPA | None | None | None | None | None | None | US EPA |
EM Source Document ID
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223 | 271 | 123 | 187 |
223 ?Comment:Parameter default values used in the i-Tree Hydro model were obtained from the i-Tree website (Document ID 198, EM 137). |
302 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
308 | 309 | 309 | 309 | 330 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
335 | 344 | 324 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
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. |
384 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
397 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
428 ?Comment:This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool. |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | US EPA Office of Research and Development - National Exposure Research Laboratory | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T. | 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 | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Walters, A. W., R. T. Barnes, and D. M. Post | Murphy, C. and T. Weekley | Cusser, S. and K. Goodell | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon |
Document Year
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2013 | 2008 | 2013 | 2016 | 2013 | 2014 | 2008 | 2014 | 2014 | 2014 | 2014 | 2013 | 2017 | 2014 | 2015 | 2012 | 2006 | 2014 | 2009 | 2012 | 2013 | None | 2022 |
Document Title
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EnviroAtlas - Featured Community | Mapping ecosystem services for planning and management | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | EnviroAtlas - Featured Community | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan | 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 | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Valuing recreational fishing quality at rivers and streams | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | 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 | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 but unpublished (explain in Comment) | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | 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 | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Journal manuscript submitted or in review | Published EPA report |
EM ID
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | http://aries.integratedmodelling.org/ | http://evoland.bioe.orst.edu/ | https://www.naturalcapitalproject.org/invest/ | 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 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/national-stormwatercalculator | |
Contact Name
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EnviroAtlas Team | Benis Egoh | Liem Tran | Richard Fulford | EnviroAtlas Team | Ken Bagstad | Michael R. Guzy |
Yu-Pin Lin ?Comment:Tel.: +886 2 3366 3467; fax: +866 2 2368 6980 |
Nirmal K. Bhagabati | Nirmal K. Bhagabati | Nirmal K. Bhagabati | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Xing Wu | Stephen Jordan | William G. Crumpton | Richard Melstrom | Annika W. Walters | 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. |
Ted Angradi | Lewis Rossman |
Contact Address
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Not reported | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Not reported | Geosciences and Environmental Change Science Center, US Geological Survey | Oregon State University, Dept. of Biological and Ecological Engineering | Not reported | 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 | Chinese Academy of Sciences, Beijing 100085, China | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, 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. | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Center for environmental solutions and emergency response, Cincinnati, Ohio |
Contact Email
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enviroatlas@epa.gov | Not reported | ltran1@utk.edu | Fulford.Richard@epa.gov | enviroatlas@epa.gov | kjbagstad@usgs.gov | Not reported | yplin@ntu.edu.tw | nirmal.bhagabati@wwfus.org | nirmal.bhagabati@wwfus.org | nirmal.bhagabati@wwfus.org | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | xingwu@rceesac.cn | jordan.steve@epa.gov | crumpton@iastate.edu | melstrom@okstate.edu | annika.walters@yale.edu | chris.murphy@idfg.idaho.gov | sarah.cusser@gmail.com | tedangradi@gmail.com | n.a. |
EM ID
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | 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: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | 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." | The Water Recharge model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results. Calibration coefficients (0-1 with 1.0 = perfect fit) were calculated for peak flow, base flow, and balance flow (peak and base). To estimate the effect of trees at the block group level for Durham, the Hydro model was run for: Gauging Station Name: SANDY CREEK AT CORNWALLIS RD NEAR DURHAM, NC, Gauging Station Location: 35°58'59.6",-78°57'24.5", Gauging Station Number: 0209722970. After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed. To estimate block group effects, the block group was assumed to act similarly to the watershed in terms of hydrologic effects. To estimate the block group effect, the outputs of the watershed were determined for each possible combination of tree cover (0-100%) and impervious cover (0-100%). Thus, there were a total of 10,201 possible responses (101 x 101). For each block group, the percent tree cover and percent impervious cover combination (e.g., 30% tree / 20% impervious) was matched to the appropriate watershed hydrologic response output for that combination. The hydrologic response outputs were calculated as either percent change or absolute change in units of cubic meters of water per square meter of land area for water flow or kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level. | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We quantified carbon sequestration and storage in vegetation and soils using Bayesian models (Bagstad et al. 2011) calibrated with Moderate-resolution Imaging Spectroradiometer Net Primary Productivity (MODIS GPP/NPP Project, http://secure.ntsg.umt. edu/projects/index.php/ID/ca2901a0/fuseaction/prohttp://www.whrc.org/ational Bwww.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627)vey Geographic Dahttp://www.geomac.gov/index.shtml)wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627) soils data, respectively. By overlaying fire boundary polygons from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov/index.shtml) we estimated carbon storage losses caused by wildfire, using fuel consumption coefficients from Spracklen et al. (2009) and carbon pool data from Smith et al. (2006). By incorporating the impacts of land-cover change from urbanization (Bolte and Vache 2010) within carbon models, we quantified resultant changes in carbon storage." | **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: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the landscape. | 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." | 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." | 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: "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: " 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. " | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." | 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: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | "Abstract: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data." |
Specific Policy or Decision Context Cited
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None identified | None identified | Not reported | None identifed | None identified | None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | Environmental effects of Highway 6 construction on Puli Township, Taiwan | 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 | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None given |
Biophysical Context
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No additional description provided | 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. | No additional description provided | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | Range of tree and impervious covers in urban setting | No additional description provided | No additional description provided | 26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere. | 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 | Agricultural plain, hills, gulleys, forest, grassland, Central China | Yaquina Bay estuary | No additional description provided | stream and river reaches of Michigan | Alewife spawning runs typically occur Mid March - May. | 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. | Waterfront districts on south Lake Michigan and south lake Erie | Sites up to 12 acres |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas. | 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 | Land use change | No scenarios presented | More conservative, average and less conservative nitrate loss rate | targeted sport fish biomass | No scenarios presented | Sites, function or habitat focus | No scenarios presented | N/A | Climate change scenarios |
EM ID
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | 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 + Application | Method + Application | Method + Application (multiple runs exist) | 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 Only |
New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model | New or revised model |
Application of existing model ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
New or revised model | New or revised model | Application of existing 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 | Application of existing 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
EM Temporal Extent
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2008-2010 | Not reported | 1987-1997 | 2006-2011 | 2008-2010 | 1950-2007 | 1990-2050 | 2010-2025 | 2008-2020 | 2008-2020 | 2008-2020 | 2010 - 2012 | 1978 - 2009 | 2006-2007, 2010 | 1969-2011 | 2003-2008 | 1973-1999 | 2008-2010 | 1979-2009 | 2010-2011 | 2009-2010 | 2022 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | future time | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 2 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 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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Hour | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Geopolitical | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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Durham NC and vicinity | South Africa | Upper Mississippi River basin; St. Croix River Watershed | Tampa Bay | Durham, NC and vicinity | Puget Sound Region | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Puli Township, Nantou County | central Sumatra | central Sumatra | central Sumatra | St. Louis River Estuary | Guanica Bay watershed | Coastal zone surrounding St. Croix | Yangjuangou catchment | Pacific Northwest | Upper Mississippi River and Ohio River basins | HUCS in Michigan | Bride Brook | Wetlands in idaho | The Wilds | Great Lakes waterfront | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 100-1000 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 | 1-10 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 1-10 ha | 100,000-1,000,000 km^2 | 1-10 km^2 | 1000-10,000 km^2. | Not applicable |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
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) | 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 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 lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all 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) | other (specify), for irregular (e.g., stream reach, lake basin) | NHDplus v1 | area, for pixel or radial feature | 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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | 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 | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | Distributed across catchments with average size of 65,000 ha | NHDplus v1 | 1 km^2 | irregular | 200m x 200m | varies | 40 m x 40 m | 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 | 30m x 30m | Not applicable | 1 km2 | reach in HUC | Not applicable | Not applicable | 10 m radius | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | deterministic | deterministic | deterministic | deterministic | 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-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | No | Yes | No | Yes | Yes | Unclear | Unclear | No | No | No | Yes | No | Yes | Yes | No | No | No |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
No | Not applicable | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | Yes | No | Yes | No | No | Not applicable | No | No | No | Yes | No | No |
Yes ?Comment:For the year 2006 and 2011 |
No | No | Yes | No | No | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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None | None | None | None | None | None |
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None | None |
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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 | No | Not applicable | No | No | No | Yes | No | Yes | No | 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 | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Yes | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
No | Unclear | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Yes | Not applicable |
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 | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
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Comment:Taiwan |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
None | None | None |
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None | None | None | None | None | None | None | None | None |
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None |
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None | None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
Centroid Latitude
em.detail.ddLatHelp
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35.99 | -30 | 42.5 | 27.74 | 35.99 | 48 | 44.11 | 23.98 | 0 | 0 | 0 | 46.72 | 17.96 | 17.73 | 36.7 | 44.62 | 40.6 | 45.12 | 41.32 | 44.06 | 39.82 | 42.26 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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-78.96 | 25 | -90.63 | -82.57 | -78.96 | -123 | -123.09 | 120.96 | 102 | 102 | 102 | -96.13 | -67.02 | -64.77 | 109.52 | -124.02 | -88.4 | 85.18 | -72.24 | -114.69 | -81.75 | -87.84 | Not applicable |
Centroid Datum
em.detail.datumHelp
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None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Provided | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Created Greenspace | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Atmosphere | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | 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 | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Agroecosystems | Rivers and Streams | Rivers and Streams | Inland Wetlands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | Not reported | None | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Urban areas including streams | Terrestrial environment surrounding a large estuary | Agricultural-urban interface at river junction | Predominantly an agricultural area with associated forest land | 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 | Loess plain | Yaquina Bay estuary and ocean | Agroecosystems and associated drainage and wetlands | stream reaches | Coastal stream | created, restored and enhanced wetlands | Grassland | Lake Michigan waterfront | Terrrestrial landcover |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecosystem | 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 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 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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Species | Community | Not applicable | Not applicable | Community | Community | Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Other (multiple scales) | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available |
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None Available | 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-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
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None |
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None | None |
<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-59 ![]() |
EM-86 | EM-91 |
EM-102 ![]() |
EM-142 | EM-317 |
EM-333 ![]() |
EM-345 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-469 | EM-604 | EM-627 |
EM-660 ![]() |
EM-667 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-886 | EM-937 |
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None | None |
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None |
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None | None | None | None |
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None |
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None | None | None |
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