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-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
EM Short Name
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Green biomass production, Central French Alps | Plant species diversity, Central French Alps | Community flowering date, Central French Alps | Divergence in flowering date, Central French Alps | Reduction in pesticide runoff risk, Europe | Birds in estuary habitats, Yaquina Estuary, WA, USA | InVEST water yield, Hood Canal, WA, USA | Landscape importance for wildlife products, Europe | Flood regulation supply-demand, Etropole, Bulgaria | Cultural ecosystem services, Bilbao, Spain | Coral taxa and land development, St.Croix, VI, USA | Urban Temperature, Baltimore, MD, USA | ARIES open Space, Puget Sound Region, USA | EPA H2O, Tampa Bay Region, FL,USA | SAV occurrence, St. Louis River, MN/WI, USA | Wave energy attenuation, St. Croix, USVI | Decrease in wave runup, St. Croix, USVI | Reef snorkeling opportunity, St. Croix, USVI | Presence of Euchema sp., St. Croix, USVI | EnviroAtlas - Restorable wetlands | Wild bees over 26 yrs of restored prairie, IL, USA | Bird abundance on restored landfills, UK | Human well-being index, Pensacola Bay, Florida | Human well being index for U.S. | ESTIMAP - Pollination potential, Iran | GI toolkit users guide |
EM Full Name
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Green biomass production, Central French Alps | Plant species diversity, Central French Alps | Community weighted mean flowering date, Central French Alps | Functional divergence in flowering date, Central French Alps | Reduction in pesticide runoff risk, Europe | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | Landscape importance for wildlife products, Europe | Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria | Cultural ecosystem services, Bilbao, Spain | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Urban Air Temperature Change, Baltimore, MD, USA | ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA | EPA H2O, Tampa Bay Region, FL, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Wave energy attenuation (by reef), St. Croix, USVI | Decrease in wave runup (by reef), St. Croix, USVI | Relative snorkeling opportunity (in reef), St. Croix, USVI | Relative presence of Euchema sp. (on reef), St. Croix, USVI | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK | Human well-being index (HWBI), Pensacola Bay, Florida | Human well being index for multiple scales, United States | ESTIMAP - Pollination potential, Iran | Green Infrastructure valuation toolkit users guide |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | InVEST | EU Biodiversity Action 5 | EU Biodiversity Action 5 |
None ?Comment:EU Mapping Studies |
US EPA | i-Tree | USDA Forest Service | ARIES | US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | EnviroAtlas | None | None | US EPA | US EPA | None | None |
EM Source Document ID
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260 | 260 | 260 | 260 | 255 | 275 | 205 | 228 | 248 | 191 | 96 | 217 | 302 | 321 | 330 | 335 | 335 | 335 | 335 | 262 | 401 | 406 | 418 | 421 | 434 | 474 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Haines-Young, R., Potschin, M. and Kienast, F. | Nedkov, S., Burkhard, B. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Yee, S.H., Paulukonis, E., Simmons, C., Russell, M., Fullford, R., Harwell, L., and L.M. Smith | Smith, L.M., Harwell, L.C., Summers, J.K., Smith, H.M., Wade, C.M., Straub, K.R. and J.L. Case | Rahimi, E., Barghjelveh, S., and P. Dong | Genecon LLP. |
Document Year
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2011 | 2011 | 2011 | 2011 | 2012 | 2014 | 2013 | 2012 | 2012 | 2013 | 2011 | 2016 | 2014 | 2015 | 2013 | 2014 | 2014 | 2014 | 2014 | 2013 | 2017 | 2011 | 2021 | 2014 | 2020 | 2010 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | EPA H20 User Manual | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - National | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | Projecting effects of land use change on human well being through changes in ecosystem services | A U.S. Human Well-being index (HWBI) for multiple scales: linking service provisioning to human well-being endpoints (2000-2010) | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | Building natural value for sustainable economic development The green infrastructure valuation toolkit user guide |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 |
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 | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published report |
EM ID
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://aries.integratedmodelling.org/ | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.merseyforest.org.uk/services/gi-val/ | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Sven Lautenbach |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
J.E. Toft | Marion Potschin | Stoyan Nedkov | Izaskun Casado-Arzuaga | Leah Oliver | Gordon M. Heisler | Ken Bagstad | Marc J. Russell, Ph.D. | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | EnviroAtlas Team | Sean R. Griffin | Lutfor Rahman | Susan Yee | Lisa Smith | Ehsan Rahini | The Mercey Forest |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | National Health and Environmental Research Effects Laboratory | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Geosciences and Environmental Change Science Center, US Geological Survey | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | 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 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Gulf Ecosystem Measurement and Modeling Division, Center for Environmental Measurement and Modeling, US Environmental Prntection Agency, Gulf Breeze, FL 32561, USA | 1 Sabine Island Dr, Gulf Breeze, FL 32561 | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | Moss Ln, Woolston, Warrington WA3 6QX, United Kingdom |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sven.lautenbach@ufz.de | frazier@nceas.ucsb.edu | jetoft@stanford.edu | marion.potschin@nottingham.ac.uk | snedkov@abv.bg | izaskun.casado@ehu.es | leah.oliver@epa.gov | gheisler@fs.fed.us | kjbagstad@usgs.gov | russell.marc@epa.gov | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | enviroatlas@epa.gov | srgriffin108@gmail.com | lutfor.rahman@northampton.ac.uk | yee.susan@epa.gov | smith.lisa@epa.gov | ehsanrahimi666@gmail.com | mail@merseyforest.org.uk |
EM ID
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | InVEST Water Yield and Scarcity Model 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. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub- watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Wildlife Products” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape." | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | ABSTRACT: "...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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...The energy (attenuation) in a moving wave (E) can then be calculated by E = 1/8ρgH^2 where ρ is the density of seawater (1025 kg m^-3) and H is wave height (attenuation)." | 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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | 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)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: snorkeling opportunity" | 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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(4) density of Euchema sp. seaweed," | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | ABSTRACT: "Changing patterns of land use, temperature, and precipitation are expected to impact ecosystem se1vices, including water quality and quantity, buffering of extreme events, soil quality, and biodiversity. Scenario ana lyses that link such impacts on ecosystem se1vices to human well-being may be valuable in anticipating potential consequences of change that are meaningful to people living in a community. Ecosystem se1vices provide munerous benefits to community well-being, including living standards, health, cultural fulfillment, education, and connection to nature. Yet assessments of impacts of ecosystem se1vices on human well-being have largely focused on human health or moneta1y benefits (e.g. market values). This study applies a human well-being modeling framework to demonsffate the potential impacts of alternative land use scenarios on multi-faceted components of human well-being through changes in ecosystem se1vices (i.e., ecological benefits functions). The modeling framework quantitatively defines these relationships in a way that can be used to project the influence of ecosystem se1vice flows on indicators of human well-being, alongside social se1vice flows and economic se1vice flows. Land use changes are linked to changing indicators of ecosystem se1vices through the application of ecological production functions. The approach is demonstrated for two future land use scenarios in a Florida watershed, representing different degrees of population growth and environmental resource protection. Increasing rates of land development were almost universally associated with declines in ecosystem se1vices indicators and associated indicators of well-being, as natural ecosystems were replaced by impe1vious surfaces that depleted the ability of ecosystems to buffer air pollutants, provide habitat for biodiversity, and retain rainwater. Scenarios with increases in indicators of ecosystem se1vices, however, did not necessarily translate into increases in indicators of well-being, due to cova1ying changes in social and economic se1vices indicators. The approach is broadly ffansferable to other communities or decision scenarios and se1ves to illustrate the potential impacts of changing land use on ecosystem se1vices and human well-being. " | Executive summary: "The HWBI is a composite assessment covering 8 domains based on 25 indicators measured using 80 different metrics. Service flow and stock assessments include 7 economic services (23 indicators, 40 metrics), 5 ecosystem services (8 indicators, 24 metrics) and 10 social services (37 indicators, 76 metrics). Data from 64 data sources were included in the HWBI and services provisioning characterizations (Fig. ES-3). For each U.S. county, state, and GSS region, data were acquired or imputed for the 2000-2010 time period resulting in over 1.5 million data points included in the full assessment linking service flows to well-being endpoints. The approaches developed for calculation of the HWBI, use of relative importance values, service stock characterization and functional modeling are transferable to smaller scales and specific population groups. Additionally, tracked over time, the HWBI may be useful in evaluating the sustainability of decisions in terms of EPA’s Total Resources Impact Outcome (TRIO) approaches. " | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." | [The toolkit provides a very helpful introduction to the evidence demonstrating the benefits of green infrastructure interventions. It offers a structured argument that speaks the language of regeneration and economic developments. The 11 economic benefits structure provides a relatively simple high level means of presenting and communicating the benefits of green infrastructure projects in economic contexts, although it also brings some risks of double-counting (see Limitations below). The toolkit provides a structured approach to value green infrastructure benefits in monetary, quantitative and qualitative terms, with equal weight being applied to each of these three ways to present existing evidence. It can add value to and inform the decision-making process, particularly when used at an early stage to get broad brush figures and weigh pros and cons.The toolkit relies on current state-of-the-art evidence and valuation techniques for green infrastructure benefits. However, the toolkit also highlights the need for considerable improvement and expansion of the evidence base to enable future iterations to provide improved valuations. The toolkit helps make green infrastructure benefits ‘visible’ to potential funders. The inclusion of environmental benefits in cost benefit analysis is currently very difficult, often requiring professional assistance. Such assistance is frequently beyond the means of many groups seeking project funding. The toolkit is aimed at filling this gap, providing a means of scoping out the indicative benefits of green infrastructure using tools and approaches accessible to many projects and groups. However, whilst the toolkit provides a means of undertaking a broad Value for Money assessment, it must but emphasised that this is only indicative and cannot replace more rigorous formal project appraisal techniques.] |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | Land use change | None identified | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | None identified | None identified | None reported | None identified | None identified | None identified | None identified | None identified | None Identified | None identified | None identified | None identified | None reported | None reported | None |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Not applicable | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Not additional description provided | No additional description provided | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | No additional description provided | Not applicable | submerged aquatic vegetation | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | N/A | Not applicable | None additional | N/A |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Future land use and land cover; climate change | No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | No scenarios presented | Land Use, EGS algorithm values, | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | geographic region | N/A | N/A |
EM ID
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised 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 | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
EM Temporal Extent
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2007-2009 | 2007-2009 | 2007-2008 | 2007-2008 | 2000 | December 2007 - November 2008 | 2005-7; 2035-45 | 2000 | Not reported | 2000 - 2007 | 2006-2007 | May 5-Sept 30 2006 | 2000-2011 | Not applicable | 2010 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2013 | 1988-2014 | Not applicable | 2010 | 2000-2010 | 2020 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | 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 Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | 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 Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 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 Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Hour | 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 ID
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or ecological |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Geopolitical | Geopolitical | Not applicable |
Spatial Extent Name
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Central French Alps | Central French Alps | Central French Alps | Central French Alps | EU-27 | Yaquina Estuary (intertidal), Oregon, USA | Hood Canal | The EU-25 plus Switzerland and Norway | Municipality of Etropole | Bilbao Metropolitan Greenbelt | St.Croix, U.S. Virgin Islands | Baltimore, MD | Puget Sound Region | Tampa Bay region | St. Louis River Estuary | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | conterminous United States | Nachusa Grasslands | East Midland | Pensacola Bay Region | Continental U.S. | Iran | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10-100 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Distributed by land cover and soil type polygons |
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) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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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 (habitat type) | 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 | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 10 km x 10 km | 0.87-104.29 ha | 30 m x 30 m | 1 km x 1 km | Distributed by irregular land cover and soil type polygons | 2 m x 2 m | Not applicable | 10m x 10m | 200m x 200m | 30m x 30m | 0.07 m^2 to 0.70 m^2 | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | irregular | Area varies by site | multiple unrelated sites | county | county | ha^2 | Not reported |
EM ID
em.detail.idHelp
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
em.detail.idHelp
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | No | Unclear | Yes | No | No | No | Yes | Yes | No | No | Yes | Yes | Yes | Yes | Yes | No | No | Not applicable | Unclear | No | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | Yes | Yes | Yes | No | No | No | No | No | No | Yes | Yes | No | No | Yes | No | No | No | No | No | No | Not applicable | Not applicable | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None |
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None | None |
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None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | No | No | Yes | No | Yes | Yes | No | Yes | No | No | No | No | Yes | Yes | Yes | Yes | Yes | No | No | Not applicable | No | No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | Not applicable | Yes | Unclear | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | Yes | Yes | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | 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 | Unclear | Yes | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
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None |
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None | None | None | None |
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Comment:Model for Iran - no form preset id for country |
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
None | None | None | None | None |
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None | None | None |
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None |
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None | None |
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None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 45.05 | 45.05 | 50.53 | 44.62 | 47.8 | 50.53 | 42.8 | 43.25 | 17.75 | 39.28 | 48 | 28.05 | 46.72 | 17.73 | 17.73 | 17.73 | 17.73 | 39.5 | 41.89 | 52.22 | 30.05 | 39.83 | 32.29 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | 6.4 | 6.4 | 7.6 | -124.06 | -122.7 | 7.6 | 24 | -2.92 | -64.75 | -76.62 | -123 | -82.52 | -96.13 | -64.77 | -64.77 | -64.77 | -64.77 | -98.35 | -89.34 | -0.91 | -87.61 | -98.58 | 53.68 | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Provided | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Not applicable |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows | Streams and near upstream environments | Estuarine intertidal | glacier-carved saltwater fjord | Not applicable | Mountainous flood-prone region | none | stony coral reef | Urban landscape and surrounding area | Terrestrial environment surrounding a large estuary | All terestrial landcover and waterbodies | Freshwater estuarine system | Coral reefs | Coral reefs | Coral reefs | Coral reefs | Terrestrial | Restored prairie, prairie remnants, and cropland | restored landfills and conserved grasslands | Mixed | All land of the continental US | terrestrial land types | Multiple |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Community | Community | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Species | Not applicable | Species | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
None | None | None | None |
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None |
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None |
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-65 | EM-70 | EM-71 | EM-79 | EM-94 | EM-103 |
EM-111 ![]() |
EM-119 | EM-133 | EM-193 | EM-260 | EM-306 | EM-315 | EM-392 | EM-414 | EM-448 | EM-450 | EM-454 | EM-461 | EM-492 |
EM-788 ![]() |
EM-836 |
EM-880 ![]() |
EM-882 | EM-941 | EM-1004 |
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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 |
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None |
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None |