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-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | i-Tree Eco: Carbon storage & sequestration, USA | Area and hotspots of soil retention, South Africa | Fish species habitat value, Tampa Bay, FL, USA | Redfish and cold water coral (EFH), Norway | Evoland v3.5 (unbounded growth), Eugene, OR, USA | InVEST (v1.004) Carbon, Indonesia | InVEST (v1.004) sediment retention, Indonesia | InVEST (v1.004) water purification, Indonesia | SAV occurrence, St. Louis River, MN/WI, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico | Visitation to reef dive sites, St. Croix, USVI | Yasso 15 - soil carbon model | Yasso07 - SOC, Loess Plateau, China | Chinook salmon value (household), Yaquina Bay, OR | N removal by wetland restoration, Midwest, USA | Forest recreation, Wisconsin, USA | RUM: Valuing fishing quality, Michigan, USA | WESP: Riparian & stream habitat, ID, USA | Plant-pollinator networks at reclaimed mine, USA | ARIES: Crop pollination in Rwanda and Burundi | SLAMM, Tampa Bay, FL, USA | Valuing environmental ed., New York, New York | CEASAR and TRACER models, EU | CMAQ chemical transport model, UK |
EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | i-Tree Eco carbon storage and sequestration (trees), USA | Area and hotspots of soil retention, South Africa | Fish species habitat value, Tampa Bay, FL, USA | Linkage between redfish and cold water coral, Norway (essential fish habitat model) | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004) carbon storage and sequestration, Sumatra, Indonesia | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) water purification (nutrient retention), Sumatra, Indonesia | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | Visitation to dive sites (reef), St. Croix, USVI | Yasso 15 - soil carbon | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Forest recreation, Wisconsin, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | WESP: Riparian and stream habitat focus projects, ID, USA | Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA | ARIES; Crop pollination in Rwanda and Burundi | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA | Valuing environmental education, Hudson River Park, New York, New York | Modelling remediation scenarios in historical mining catchments | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
EM Source or Collection
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Envision | i-Tree | USDA Forest Service | None | US EPA | None | Envision | InVEST | InVEST | InVEST | US EPA | US EPA | US EPA | None | None | US EPA | None | None | None | None | None | ARIES | None | None | None | None |
EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
195 | 271 | 187 | 259 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
309 | 309 | 309 | 330 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
335 |
342 ?Comment:Webpage pdf users manual for model. |
344 | 324 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
376 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
397 | 411 |
415 ?Comment:Secondary sources: Documents 412 and 413. |
416 | 467 | 483 |
Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Qiu, J. and M. G. Turner | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Murphy, C. and T. Weekley | Cusser, S. and K. Goodell | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Sherwood, E. T. and H. S. Greening | Hutcheson, W. Hoagland, P., and D. Jin | Gamarra, J. G., Brewer, P. A., Macklin, M. G., & Martin, K. | Chemel, C., Fisher, B.E.A., Kong, X., Francis, X.V., Sokhi, R.S., Good, N., Collins, W.J. and Folberth, G.A. |
Document Year
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2008 | 2013 | 2008 | 2016 | 2010 | 2008 | 2014 | 2014 | 2014 | 2013 | 2017 | 2014 | 2016 | 2015 | 2012 | 2006 | 2013 | 2014 | 2012 | 2013 | 2018 | 2014 | 2018 | 2014 | 2014 |
Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Carbon storage and sequestration by trees in urban and community areas of the United States | Mapping ecosystem services for planning and management | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Estimating linkages between redfish and cold water coral on the Norwegian coast | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Yasso 15 graphical user-interface manual | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | 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 | Spatial interactions among ecosystem services in an urbanizing agricultural watershed | Valuing recreational fishing quality at rivers and streams | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine | Towards globally customizable ecosystem service models | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats | Valuing environmental education as a cultural ecosystem service at Hudson River Park | Modelling remediation scenarios in historical mining catchments | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
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 | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Not applicable | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
Not applicable | Not applicable | Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://github.com/integratedmodelling/im.aries.global | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | Not applicable | Not applicable | https://www.epa.gov/cmaq/download-cmaq | |
Contact Name
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Michael R. Guzy | David J. Nowak | Benis Egoh | Richard Fulford | Naomi S. Foley | Michael R. Guzy | Nirmal K. Bhagabati | Nirmal K. Bhagabati | Nirmal K. Bhagabati | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Jari Liski | Xing Wu | Stephen Jordan | William G. Crumpton | Monica G. Turner | Richard Melstrom | Chris Murphy |
Sarah Cusser ?Comment:Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A. |
Javier Martinez | Edward T. Sherwood | Walter Hutcheson | Javier G. P. Gamarra | B.E.A. Fisher |
Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Dept. of Economics and Management, Univeristy of Tromso, Norway | Oregon State University, Dept. of Biological and Ecological Engineering | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | Chinese Academy of Sciences, Beijing 100085, China | 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 | Not reported | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A. | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA | New York University, United States | Institute of Biological, Environmental and Rural Sciences, Aberystwyth, SY23 3DB, UK | Little Beeches, Headley Road, Leatherhead KT22 8PT, UK. |
Contact Email
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Not reported | dnowak@fs.fed.us | Not reported | Fulford.Richard@epa.gov | naomifoley@gmail.com | Not reported | nirmal.bhagabati@wwfus.org | nirmal.bhagabati@wwfus.org | nirmal.bhagabati@wwfus.org | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | jari.liski@ymparisto.fi | xingwu@rceesac.cn | jordan.steve@epa.gov | crumpton@iastate.edu | turnermg@wisc.edu | melstrom@okstate.edu | chris.murphy@idfg.idaho.gov | sarah.cusser@gmail.com | javier.martinez@bc3research.org | esherwood@tbep.org | wwh235@nyu.edu | jgg@aber.ac.uk | None provided |
EM ID
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | ABSTRACT: "…This paper applies the production function approach to estimate the link between cold water corals and redfish in Norway. Both the carrying capacity and growth rate of redfish are found to be functions of cold water coral habitat and thus cold water corals can be considered an essential fish habitat…The essential habitat model shows the best fit to the data…" AUTHOR'S DESCRIPTION: "…the EFH model presented by Barbier and Strand (1998), in which the habitat is considered essential to the stock; i.e., if the habitat declines to zero the fish stock will perish…based on the Gordon-Schaefer model, which is a single-species biomass model, where effort is the control variable and fish stock is the state variable. In the case of habitat-fisheries interactions, such as in our case, a second state variable is introduced, the habitat (CWC)…Scientists have stimated that 30-50% of CWC habitat has been damaged (Fossa, Mortensen, and Furevik 2002. Working within these bounds, we empirically estimate the relationship between CWC as a habitat and a fish stock..." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... We mapped biomass carbon by assigning carbon values (in ton ha_1) for aboveground, belowground, and dead organic matter to each LULC class based on values from literature, as described in Tallis et al. (2010). We mapped soil carbon separately, as large quantities of carbon are stored in peat soil (Page et al., 2011). We estimated total losses in peat carbon over 50 years into the future scenarios, using reported annual emission rates for specific LULC transitions on peat (Uryu et al., 2008)...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... Our nutrient retention model estimates nitrogen and phosphorus loading (kg y^-1), leading causes of water pollution from fertilizer application and other activities, using the export coefficient approach of Reckhow et al. (1980). The model routes nutrient runoff from each land parcel downslope along the flow path, with some of the nutrient that originated upstream being retained by the parcel according to its retention efficiency. For assessing variation within the same LULC map (2008 and each scenario), we compared sediment and nutrient retention across the landscape. However, for assessing change to scenarios, we compared sediment and nutrient export between the relevant LULC maps, as the change in export (rather than in retention) better reflects the change in service experienced downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | 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 | AUTHOR'S DESCRIPTION (from Supporting Information): "Forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area. Several assumptions were made for this assessment approach: larger areas and places with more recreational opportunities would provide more recreational service; areas near large population centers would be visited and used more than remote areas; and proximity to major roads would increase access and thus recreational use of an area… we quantified forest recreation service for each 30-m grid cells using the equation below: FRSi = Ai Σ(Oppti + Popi +Roadi), where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5,200) to a range of 0–100, with 0 representing no forest recreation service and 100 representing highest service. | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context." | [Abstract:Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.] | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" | ABSTRACT: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. " | Local remediation measures, particularly those undertaken in historical mining areas, can often be ineffective or even deleterious because erosion and sedimentation processes operate at spatial scales beyond those typically used in point-source remediation. Based on realistic simulations of a hybrid landscape evolution model combined with stochastic rainfall generation, we demonstrate that similar remediation strategies may result in differing effects across three contrasting European catchments depending on their topographic and hydrologic regimes. Based on these results, we propose a conceptual model of catchment-scale remediation effectiveness based on three basic catchment characteristics: the degree of contaminant source coupling, the ratio of contaminated to non-contaminated sediment delivery, and the frequency of sediment transport events. | This paper shows how the advanced chemical transport model CMAQ can be used to estimate future levels of PM2.5 in the UK, the key air pollutant in terms of human health effects, but one which is largely made up from the formation of secondary particulate in the atmosphere. By adding the primary particulate contribution from typical urban roads and including a margin for error, it is concluded that the current indicative limit value for PM2.5 will largely be met in 2020 assuming 2006 meteorological conditions. Contributions to annual average regional PM2.5 concentration from wild fires in Europe in 2006 and from possible climate change between 2006 and 2020 are shown to be small compared with the change in PM2.5 concentration arising from changes in emissions between 2006 and 2020. The contribution from emissions from major industrial sources regulated in the UK is estimated from additional CMAQ calculations. The potential source strength of these emissions is a useful indicator of the linearity of the response of the atmosphere to changes in emissions. Uncertainties in the modelling of regional and local sources are taken into account based on previous evaluations of the models. Future actual trends in emissions mean that exceedences of limit values may arise, and these and further research into PM2.5 health effects will need to be part of the future strategy to manage PM2.5 concentrations. |
Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | Not reported | None identified | None identifed | 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…" | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | None provided | None identified | None identified | None | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified |
Biophysical Context
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No additional description provided | Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | Continental slope | No additional description provided | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | submerged aquatic vegetation | No additional description provided | No additional description provided | Not applicable | Agricultural plain, hills, gulleys, forest, grassland, Central China | Yaquina Bay estuary | No additional description provided | No additional description provided | stream and river reaches of Michigan | restored, enhanced and created wetlands | The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils. | Entire countries of Rwanda and Burundi considered | No additional description provided | N/A | Rver system catchments associated with mining sites distributed across Europe | United kingdom atmosphere |
EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | No scenarios presented | Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area. | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Land use change | No scenarios presented | More conservative, average and less conservative nitrate loss rate | No scenarios presented | targeted sport fish biomass | Sites, function or habitat focus | No scenarios presented | N/A | Varying sea level rise (baseline - 2m), and two habitat adaption strategies | N?A | No scenarios presented | 2020 European emissions scenario |
EM ID
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
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 (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing model | 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 | Application of existing model | New or revised model | Application of existing model | Application of existing 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-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
EM Temporal Extent
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1990-2050 | 1989-2010 | Not reported | 2006-2011 | 1986-2002 | 1990-2050 | 2008-2020 | 2008-2020 | 2008-2020 | 2010 - 2012 | 1978 - 2009 | 2006-2007, 2010 | Not applicable | 1969-2011 | 2003-2008 | 1973-1999 | 2000-2006 | 2008-2010 | 2010-2011 | 2009-2010 | 2010 | 2002-2100 | 2015 | 1800-2100 | 2006-2020 |
EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | future time | 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 | Not applicable | both | both |
EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | continuous | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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2 | 1 | Not applicable | Not applicable | Not applicable | 2 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 14 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Year | Year | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | United States | South Africa | Tampa Bay | Norwegian Sea (ICES areas I and II) | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | central Sumatra | central Sumatra | central Sumatra | St. Louis River Estuary | Guanica Bay watershed | Coastal zone surrounding St. Croix | Not applicable | Yangjuangou catchment | Pacific Northwest | Upper Mississippi River and Ohio River basins | Yahara Watershed, Wisconsin | HUCS in Michigan | Wetlands in idaho | The Wilds | Rwanda and Burndi | Tampa Bay estuary watershed | Hudson River Park | Ystwyth, Ampoi, and Naracauli | United Kingdom |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 10-100 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | Not applicable | 1-10 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 1-10 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 10-100 ha | 100-1000 km^2 | 100,000-1,000,000 km^2 |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially 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 lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | map scale, for cartographic feature | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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varies | 1 m^2 | Distributed across catchments with average size of 65,000 ha | 1 km^2 | Not applicable | varies | 30 m x 30 m | 30 m x 30 m | 30 m x 30 m | 0.07 m^2 to 0.70 m^2 | HUC | 10 m x 10 m | Not applicable | 30m x 30m | Not applicable | 1 km2 | 30m x 30m | reach in HUC | Not applicable | 10 m radius | 1km | 10 x 10 m | Not applicable | Not reported | Not applicable |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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stochastic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Comment:Agent based modeling results in response indices. |
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EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | No | No | No | Yes | Unclear | No | No | No | Yes | No | Yes | Not applicable | Yes | No | No | No | No | No | Not applicable | Unclear | No | No | Yes | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | Yes | No | No | No | No | Yes | No | No | Not applicable |
Yes ?Comment:For the year 2006 and 2011 |
No | No | No | Yes | No | Not applicable | No | No | No | No |
Yes ?Comment:Two versions of CMAQ (v4.6 and v4.7) were used to assess performance. Both values are provided here respectively. |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None |
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None | None | None | None |
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None | None | None |
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None | None | None |
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None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | No | No | No | No | No | Yes | No | Yes | Not applicable | 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 | No | No | Yes | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No |
Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | Yes | No | No | No | Unclear | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | No | Yes | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No | Unclear | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
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Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
None | None | None |
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None | None | None | None | None | None |
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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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Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
Centroid Latitude
em.detail.ddLatHelp
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44.11 | 40.16 | -30 | 27.74 | 70 | 44.11 | 0 | 0 | 0 | 46.72 | 17.96 | 17.73 | Not applicable | 36.7 | 44.62 | 40.6 | 43.1 | 45.12 | 44.06 | 39.82 | -2.59 | 27.76 | 40.73 |
52.5 ?Comment:There are 3 locations provided in this study with latitudes of 52.5, 46, and 40 as well as longitudes of -4, 10, and 25, respectively. |
54 |
Centroid Longitude
em.detail.ddLongHelp
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-123.09 | -99.79 | 25 | -82.57 | 10 | -123.09 | 102 | 102 | 102 | -96.13 | -67.02 | -64.77 | Not applicable | 109.52 | -124.02 | -88.4 | -89.4 | 85.18 | -114.69 | -81.75 | 29.97 | -82.54 | -74.01 | -4 | 4 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Provided | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Forests | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Open Ocean and Seas | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Forests | Grasslands | Scrubland/Shrubland | Tundra | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Agroecosystems | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Rivers and Streams | Inland Wetlands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Atmosphere |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Agricultural-urban interface at river junction | Urban forests | Not reported | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | cold water coral reefs | Agricultural-urban interface at river junction | 104 land use land cover classes | 104 land use land cover classes | 104 land use land cover classes | Freshwater estuarine system | Tropical terrestrial | Coral reefs | Not applicable | Loess plain | Yaquina Bay estuary and ocean | Agroecosystems and associated drainage and wetlands | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape | stream reaches | created, restored and enhanced wetlands | Grassland | varied | Esturary and associated urban and terrestrial environment | Park | Watershed catchment | United Kingdom atmosphere |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable |
Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Not applicable | Species | Guild or Assemblage | Not applicable | Community | Community | Community | Not applicable | Not applicable | Not applicable | Species | Not applicable | Other (multiple scales) | Not applicable | Not applicable | Not applicable | Not applicable | Species | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
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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 | None Available |
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None Available |
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None Available | None Available | 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-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-12 ![]() |
EM-24 | EM-86 |
EM-102 ![]() |
EM-319 |
EM-333 ![]() |
EM-349 ![]() |
EM-359 ![]() |
EM-363 ![]() |
EM-414 | EM-432 | EM-457 | EM-466 | EM-469 | EM-604 | EM-627 | EM-654 |
EM-660 ![]() |
EM-718 ![]() |
EM-774 ![]() |
EM-855 |
EM-863 ![]() |
EM-875 | EM-997 | EM-1021 |
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None |
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None | None | None | None |
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None | None |
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None |
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None | None | None | None |
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