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-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
EM Short Name
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MIMES: For Massachusetts Ocean (v1.0) | InVESTv3.0 Water yield, Guánica Bay, Puerto Rico | State of the reef index, St. Croix, USVI | Value of a reef dive site, St. Croix, USVI | Reef density of S. gigas, St. Croix, USVI | Value of finfish, St. Croix, USVI | Yasso 15 - soil carbon model | InVEST fisheries, lobster, South Africa | Nutrient Tracking Tool (NTT), north central Texas, USA | VELMA v2.0, Ohio, USA | Beach visitation, Barnstable, MA, USA | Visitor value lost to a beach closure, MA, USA | Floral resources on landfill sites, United Kingdom | WESP: Irrigation water, ID, USA | Wildflower mix supporting bees, Florida, USA | Brown-headed cowbird abundance, Piedmont, USA | Horned lark abundance, Piedmont region, USA | Health, safety and greening urban space, PA, USA | Human well being index for U.S. | VELMA v. 2.0 Hydro | HWB indicator-Adult success, Great Lakes, USA | i-Tree species selector v. 4.0 | C-GEM, Lousiana continental shelf, USA | EcoSim II - method | Predicting ecosystem service values, Bangladesh |
EM Full Name
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Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) v3.0 Water yield, Guánica Bay, Puerto Rico, USA | State of the reef index, St. Croix, USVI | Value of a dive site (reef), St. Croix, USVI | Relative density of Strombus gigas (on reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | Yasso 15 - soil carbon | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Nutrient Tracking Tool (NTT), Upper North Bosque River watershed, Texas, USA | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | Beach visitation, Barnstable, Massachusetts, USA | Visitor value lost to a beach closure, Barnstable, Massachusetts, USA | Floral resources on landfill sites, East Midlands, United Kingdom | WESP: Irrigation return water treatment, Idaho, USA | Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA | Brown-headed cowbird abundance, Piedmont ecoregion, USA | Horned lark abundance, Piedmont ecoregion, USA | Health, safety and greening urban vacant space, Pennsylvania, USA | Human well being index for multiple scales, United States | Visualizing Ecosystems for Land Management Assessments (VELMA) Version 2.0 Hydro | Human well being indicator-Adult financial success, Great Lakes waterfront, USA | i-Tree species selector v. 4.0 | Carbon Generic Estuary Model (C-GEM), Lousiana Continental Shelf, USA | EcoSim II - method | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
EM Source or Collection
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US EPA | US EPA | InVEST | US EPA | US EPA | US EPA | US EPA | None | InVEST | None | US EPA | US EPA | US EPA | None | None | None | None | None | None | US EPA | US EPA | US EPA | i-Tree | US EPA | None | None |
EM Source Document ID
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316 | 338 | 335 | 335 | 335 | 335 |
342 ?Comment:Webpage pdf users manual for model. |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
354 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
386 | 386 | 389 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
400 | 405 | 405 | 419 | 421 | 366 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
426 ?Comment:Doc# 427 is an additional source for this EM. |
441 | 448 | 457 |
Document Author
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Altman, I., R.Boumans, J. Roman, L. Kaufman | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | 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 | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Saleh, A., O. Gallego, E. Osei, H. Lal, C. Gross, S. McKinney, and H. Cover | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Murphy, C. and T. Weekley | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | Branas, C. C., R. A. Cheney, J. M. MacDonald, V. W. Tam, T. D. Jackson, and T. R. Ten Havey | Smith, L.M., Harwell, L.C., Summers, J.K., Smith, H.M., Wade, C.M., Straub, K.R. and J.L. Case | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | i-Tree | Jarvis, B.M., Lehrter, J.C., Lowe, L.L., Hagy, J.D., Wan, Y., Murrell, M.C., Ko, D.S., Penta, B., and R.W. Gould | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y. |
Document Year
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2012 | 2017 | 2014 | 2014 | 2014 | 2014 | 2016 | 2018 | 2011 | 2018 | 2018 | 2018 | 2013 | 2012 | 2015 | 2008 | 2008 | 2011 | 2014 | 2014 | None | None | 2020 | 2000 | 2022 |
Document Title
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Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | 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 | 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 | Yasso 15 graphical user-interface manual | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Nutrient Tracking Tool - a user-friendly tool for calculating nutrient reductions for water quality trading | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | A difference-in-differences analysis of health, safety, and greening vacant urban space | A U.S. Human Well-being index (HWBI) for multiple scales: linking service provisioning to human well-being endpoints (2000-2010) | VELMA Version 2.0 User Manual and Technical Documentation | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | i-Tree Species Selector User's Manual v. 4.0 | Modeling spatiotemporal patterns of ecosystem metabolism and organic carbon dynamics affecting hypoxia on the Louisiana continental shelf | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
Document Status
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Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Not applicable | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published EPA report | Journal manuscript submitted or in review | Webpage | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
http://www.afordablefutures.com/orientation-to-what-we-do | http://www.naturalcapitalproject.org/invest/ | Not applicable | 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. |
https://www.naturalcapitalproject.org/invest/ | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | https://species.itreetools.org/ | Not applicable | https://ecopath.org/downloads/ | Not applicable | |
Contact Name
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Irit Altman | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Jari Liski | Michelle Ward | Ali Saleh | Heather Golden | Kate K, Mulvaney | Kate K, Mulvaney | Sam Tarrant | Chris Murphy | Neal Williams | Sam Riffell | Sam Riffell | Charles C. Branas | Lisa Smith | Robert B. McKane | Ted Angradi |
Not reported ?Comment:send comments through any of the means listed on the i-Tree support page: http://www.itreetools.org/support/. |
Brandon Jarvis | Carl Walters | Syed Riad Morshed |
Contact Address
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Boston University, Portland, Maine | 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 | 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 | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Texas Institute for Applied Environmental Research-Tarleton State University, Stephenville, TX 76401,USA | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | Not reported | Not reported | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 936, 423 Guardian Drive, Philadelphia, PA 19104-6021 | 1 Sabine Island Dr, Gulf Breeze, FL 32561 | USEPA Office of Research and Development National Health and Environmental Effects Research Laboratory Western Ecology Division Corvallis, Oregon 97333 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Not reported | 1US EPA, Office of Research and Development, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA Office of Research and Development, U.S. EPA, Gulf Breeze, FL, USA | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh |
Contact Email
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iritaltman@bu.edu | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | jari.liski@ymparisto.fi | m.ward@uq.edu.au | saleh@tiaer.tarleton.edu | Golden.Heather@epa.gov | Mulvaney.Kate@EPA.gov | Mulvaney.Kate@EPA.gov | sam.tarrant@rspb.org.uk | chris.murphy@idfg.idaho.gov | nmwilliams@ucdavis.edu | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | cbranas@upenn.edu | smith.lisa@epa.gov | mckane.bob@epa.gov | tedangradi@gmail.com | info@itreetools.org | jarvis.brandon@epa.gov | c.walters@oceans.ubc.ca | riad.kuet.urp16@gmail.com |
EM ID
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
Summary Description
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AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. | 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: "Stakeholders identified an objective of meeting water demands for agriculture and domestic purposes, including irrigation, drinking water, or hydropower production…Geomorphology, climate, and vegetation determine the amount of water runoff from the landscape that could be available for consumptive uses. Long-term average water yield was estimated for each HUC12 sub-watershed as the difference between total precipitation and the amount absorbed by the different land cover classes using a reservoir hydropower production model (InVEST 3.0.0; Tallis et al., 2013)." | 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 indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000)...for reef ecological integrity (van Beukering and Cesar, 2004) defines the state of the reef as State of the Reef =ΣiwiRi where the Ri are the relative quantity of coral cover, macro-algal cover, fish richness, coral richness, and fish abundance, standardized to reflect the range of conditions at the location being evaluated (in this case, St. Croix). The wi give the weighted contribution of each attribute to reef condition based on expert judgment, originally developed for Hawaii, which were wcoral_cover=0.30, walgae_cover= 0.15, wfish_richness=0.15, wcoral_richness=0.20, and wfish_abundance=0.20 (van Beukering and Cesar, 2004). Ideally, these values would be developed to reflect local knowledge and concerns for the Caribbean or St. Croix. For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models t | 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)…Another method to quantify recreational opportunities is to use survey data of tourists and recreational visitors to the reefs to generate statistical models to quantify the link between reef condition and production of recreation-related ecosystem services. Wielgus et al. (2003) used interviews with SCUBA divers in Israel to derive coefficients for a choice model in which willingness to pay for higher quality dive sites was determined in part by a weighted combination of factors identified with dive quality: Relative value of dive site = 0.1227(Scoral+Sfish+Acoral+Afish)+0.0565V where Scoral, Sfish are coral and fish richness, Acoral, Afish are abundances of fish and coral per square meter, and V is water visibility (meters)." | 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:...(2) density of the queen conch Strombus gigas" | 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:...(5) value of finfish," | 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." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | ABSTRACT: "The Nutrient Tracking Tool (NTrT) is an enhanced version of the Nitrogen Trading Tool, a user-friendly Web-based computer program originally developed by the USDA. The NTrT estimates nutrient (nitrogen and phosphorus) and sediment losses from fields managed under a variety of cropping patterns and management practices through its user-friendly, Web-based linkage to the Agricultural Policy Environmental eXtender (APEX) model. It also accesses the USDA Natural Resources Conservation Service’s Web Soil Survey to utilize their geographic information system interface for field and operation identification and load soil information. The NTrT provides farmers, government officials, and other users with a fast and efficient method of estimating nitrogen and phosphorus credits for water quality trading, as well as other water quality, water quantity, and farm production impacts associated with conservation practices. The information obtained from the tool can help farmers determine the most cost-effective conservation practice alternatives for their individual operations and provide them with more advantageous options in a water quality credit trading program. An application of the NTrT to evaluate conservation practices on fields receiving dairy manure in a north central Texas watershed indicates that phosphorus-based application rates, filter strips, forest buffers, and complete manure export off the farm all result in reduced phosphorus losses from the fields on which those practices were implemented. When compared to a base¬line condition that entailed manure application at the nitrogen agronomic rate of receiving crops, the reductions in total phosphorus losses associated with these practices ranged from 15% (2P Rate scenario) to 76% (forest buffer scenario)." AUTHOR'S DESCRIPTION: "This paper provides a brief overview of the NTrT and presents results of verification and application of the tool on a selected field on a test field in the Upper North Bosque River (UNBR) watershed in Texas…simulations for the baseline and all five alternative scenarios were replicated for each of 90 specific soil types in Erath County, Texas…results reported and discussed in this report represent the averages of the output for all soil types." | ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "While it might be assumed that the economic value of a beach day and the value of a lost beach day would be symmetric, they are not quite the same in our analysis. This is because the town has many fixed costs for beach management, including staff, facility maintenance, and other amenities. These fixed costs are offset by the daily parking fees charged to out-of-town visitors and the various beach stickers available for town residents. Assuming the town does not make a profit and just breaks even on beach parking fees in relation to the costs incurred to provide the services, the net economic value of a day without a closure (benefits less costs) would simply be the consumer surplus for the public. However, this amount is different than the net economic value lost due to a beach closure, which includes the lost consumer surplus as well as the lost revenue to the town. This revenue is money the town would have collected to cover costs and therefore is considered a loss (negative producer surplus). Therefore, a beach day affected by a closure is valued as a loss of consumer surplus plus lost parking revenue…" Equation 3, page 19, provides the resulting formula for the value lost from a beach closure. | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | 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: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT: "Greening of vacant urban land may affect health and safety. The authors conducted a decade-long difference-indifferences analysis of the impact of a vacant lot greening program in Philadelphia, Pennsylvania, on health and safety outcomes. ‘‘Before’’ and ‘‘after’’ outcome differences among treated vacant lots were compared with matched groups of control vacant lots that were eligible but did not receive treatment. Control lots from 2 eligibility pools were randomly selected and matched to treated lots at a 3:1 ratio by city section. Random-effects regression models were fitted, along with alternative models and robustness checks. Across 4 sections of Philadelphia, 4,436 vacant lots totaling over 7.8 million square feet (about 725,000 m^2) were greened from 1999 to 2008. Regression adjusted estimates showed that vacant lot greening was associated with consistent reductions in gun assaults across all 4 sections of the city (P < 0.001) and consistent reductions in vandalism in 1 section of the city (P < 0.001). Regression-adjusted estimates also showed that vacant lot greening was associated with residents’ reporting less stress and more exercise in select sections of the city (P < 0.01). Once greened, vacant lots may reduce certain crimes and promote some aspects of health. Limitations of the current study are discussed. Community-based trials are warranted to further test these findings." REVIEWER'S COMMENTS: Regression models were fitted separately for point-based, tract-based, and block group-based outcomes, and for the four sections of Philadelphia separately and combined. This entry presents just the point-based outcomes for the whole of Philadelphia. | 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: "VELMA – Visualizing Ecosystems for Land Management Assessments – is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | ABSTRACT: "The Species Selector is a free-standing i-Tree utility that ranks tree species based on their environmental benefits at maturity. As such, it complements existing tree selection programs that rank species based on esthetics or other features. Species are selected based on three types of information. First, hardiness is considered. The hardiness zone is determined based on state and city, and all species that are not sufficiently hardy are eliminated from consideration. Second, mature height is considered. Users are asked to specify minimum and maximum heights, and species outside of that range are eliminated. Finally, eight environmental factors are considered in the rankings created by the Species Selector: • Air pollution removal • Air temperature reduction • Ultraviolet radiation reduction • Carbon storage • Pollen allergenicity • Building energy conservation • Wind reduction • Stream flow reduction (stormwater management). Users are asked to rank the importance of each of these factors on a scale of 0 to 10. The combination of hardiness, mature height, and desired functionality produces a ranked list of appropriate species from an initial database of about 1,600 species. The large species database covers a broad range of native, naturalized and exotic trees, some of which are commonly planted in urban areas. Since only city hardiness zone, tree height and user functional preferences are used to produce the list, there may well be many species on the list that are unsuitable to the local context for a variety of reasons. A species may have particular structural, drainage, sun, pest, or soil pH limitations that should exclude it from use. Furthermore, since many native and exotic species are included, items may appear that are simply not available in the local trade. For these reasons, the list should be considered a beginning rather than an end. The list will need to be whittled down to meet local needs and limitations. Relevant cultural needs should be taken into account as well. The result will be a list of recommended species suited for local use that maximizes environmental services." | ABSTRACT: "The hypoxic zone on the Louisiana Continental Shelf (LCS) forms each summer due to nutrient‐enhanced primary production and seasonal stratification associated with freshwater discharges from the Mississippi/Atchafalaya River Basin (MARB). Recent field studies have identified highly productive shallow nearshore waters as an important component of shelf‐wide carbon production contributing to hypoxia formation. This study applied a three‐dimensional hydrodynamic‐biogeochemical model named CGEM (Coastal Generalized Ecosystem Model) to quantify the spatial and temporal patterns of hypoxia, carbon production, respiration, and transport between nearshore and middle shelf regions where hypoxia is most prevalent. We first demonstrate that our simulations reproduced spatial and temporal patterns of carbon production, respiration, and bottom‐water oxygen gradients compared to field observations. We used multiyear simulations to quantify transport of articulate organic carbon (POC) from nearshore areas where riverine organic matter and phytoplankton carbon production are greatest. The spatial displacement of carbon production and respiration in our simulations was created by westward and offshore POC flux via phytoplankton carbon flux in the surface layer and POC flux in the bottom layer, supporting heterotrophic respiration on the middle shelf where hypoxia is frequently observed. These results support existing studies suggesting the importance of offshore carbon flux to hypoxia formation, particularly on the west shelf where hypoxic conditions are most variable. " | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " | Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability. |
Specific Policy or Decision Context Cited
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None identified | Meeting water demands | None identified | None identified | None identified | None identified | None identified | Future rock lobster fisheries management | None identified | None identified | To assess the number of people who would be impacted by beach closures. | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | None identified | None identrified | None reported | None reported | None identified | None reported | None identified | None identified | None identified | None reported | None | N/A |
Biophysical Context
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No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Not applicable | No additional description provided | The UNBR watershed is comprised primarily of two main physiographic areas, the West Cross Timbers and the Grand Prairie Land Resource Areas. In the West Cross Timbers, soils are primarily fine sandy loams with sandy clay subsoils. Soils in the Grand Prairie area, on the other hand, are typically calcareous clays and clay loams (Ward et al. 1992). | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | Four separate beaches within the community of Barnstable | Four separate beaches within the community of Barnstable | No additional description provided | restored, enhanced and created wetlands | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | No additional description provided | Not applicable | No additional description provided | Waterfront districts on south Lake Michigan and south lake Erie | No additional description provided | Louisiana coastal continental shelf | None, Ocean ecosystems | Jashore city, Bangladesh |
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 | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | Conservation management strategies to reduce phosphorus losses | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | No scenarios presented | No scenarios presented | No scenarios presented | Sites, function or habitat focus | Varied wildflower planting mixes of annuals and perennials | N/A | N/A | No scenarios presented | geographic region | No scenarios presented | N/A | No scenarios presented | Coastal Shelf location | N/A | No scenarios presented |
EM ID
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
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 Only | 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 (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method Only | Method + Application | Method Only | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | 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 | 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 | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
em.detail.idHelp
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
EM Temporal Extent
em.detail.tempExtentHelp
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Not applicable | 2006 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | Not applicable | 1986-2115 | 1960-2001 | Jan 1, 2009 to Dec 31, 2011 | 2011 - 2016 | July 1, 2011 to June 31, 2016 | 2007-2008 | 2010-2012 | 2011-2012 | 2008 | 2008 | 1998-2008 | 2000-2010 |
Not applicable ?Comment:User defined model duration. |
2022 | Not applicable | 2003-2007 | Not applicable | 2000-2050 |
EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | Not applicable | time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | future time | past time | past time | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | both | both |
EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | discrete | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete |
discrete ?Comment:Modeller dependent |
discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 | 1 | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | 10 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Day | Day | Day | Day | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Season | Day | Year |
EM ID
em.detail.idHelp
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Not applicable | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Not applicable | Geopolitical | Not applicable | Physiographic or ecological | Other | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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Massachusetts Ocean | Guanica Bay watershed | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Not applicable | Table Mountain National Park Marine Protected Area | Upper North Bosque River watershed | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | East Midlands | Wetlands in idaho | Agricultural plots | Piedmont Ecoregion | Piedmont Ecoregion | Philadelphia | Continental U.S. | Not applicable | Great Lakes waterfront | Not applicable | Lousiana continental shelf | Not applicable | Jashore city, Bangladesh |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | Not applicable | 100-1000 km^2 | 100-1000 km^2 | 10-100 ha | 10-100 ha | 10-100 ha | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | Not applicable | 1000-10,000 km^2. | Not applicable | 100,000-1,000,000 km^2 | Not applicable | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
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 lumped (in all cases) | spatially lumped (in all 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 lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Point-based measures are continuous and boundary-free, assign each lot to its own unique neighborhood, and avoid aggregation effects while directly accounting for spillover and the variability of neighboring areas. |
spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:User defined scale, from plot to basin size. |
spatially lumped (in all cases) | Not applicable | spatially distributed (in at least some cases) | spatially lumped (in all cases) | 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 | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | Not applicable | 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 | Not applicable | Not applicable | area, for pixel or radial feature | Not applicable | map scale, for cartographic feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
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1 km x1 km | 30 m x 30 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | Not applicable | Not applicable | Not applicable | 10m x 10m | by beach site | by beach site | multiple unrelated locations | Not applicable | Not applicable | Not applicable | Not applicable | Point based | county | user defined | Not applicable | Not applicable | regions of shelf | Not applicable | 30m |
EM ID
em.detail.idHelp
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Logic- or rule-based | Analytic | Numeric | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | 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-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Yes | Yes | Yes | Yes | Not applicable | No | Yes | Yes | Yes | Yes | Not applicable | No | No | Yes | Yes | No | No | Not applicable | No | Not applicable | Yes | No | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No | Not applicable | No | No |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
No | No | Not applicable | No | No | No | No |
No ?Comment:Each outcome was fitted separatly, with R2 provided. See Variable Value comment for each Response. |
No | Not applicable | No | Not applicable | No | No | Yes |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | Yes | Yes | Yes | Yes | Not applicable |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
No | Yes | No | No | Not applicable | No | No | No | No | No | No | Not applicable | No | Not applicable | Yes | Not applicable | Yes |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | Not applicable | No | No | No | No | No | Not applicable | No | No | No | No | No | Unclear | Not applicable | No | Not applicable | No | Not applicable | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | Not applicable | No | No | No | Yes |
No ?Comment:n/a |
Not applicable | No | No | Yes | Yes | No | Yes | Not applicable | Yes | Not applicable | Yes | Not applicable | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
None |
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None | None | None | None | None | None |
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
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None |
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None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
Centroid Latitude
em.detail.ddLatHelp
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41.72 | 17.96 | 17.73 | 17.73 | 17.73 | 17.73 | Not applicable | -34.18 | 32.09 | 39.19 | 41.64 | 41.64 | 52.22 | 44.06 | 29.4 | 36.23 | 36.23 | 39.95 | 39.83 | Not applicable | 42.26 | Not applicable | 30.28 | Not applicable | 23.95 |
Centroid Longitude
em.detail.ddLongHelp
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-69.87 | -67.02 | -64.77 | -64.77 | -64.77 | -64.77 | Not applicable | 18.35 | -98.12 | -84.29 | -70.29 | -70.29 | -0.91 | -114.69 | -82.18 | -81.9 | -81.9 | -75.17 | -98.58 | Not applicable | -87.84 | Not applicable | -88.39 | Not applicable | 89.12 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | WGS84 | Not applicable | other |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Not applicable | Estimated | Not applicable | Provided |
EM ID
em.detail.idHelp
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EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Grasslands | Scrubland/Shrubland | Tundra | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Grasslands | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Near Coastal Marine and Estuarine | Open Ocean and Seas | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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None identified | 13 LULC were used | Coral reefs | Coral reefs | Coral reefs | Coral reefs | Not applicable | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Rangeland and forage fields for dairy | Mixed land cover suburban watershed | Saltwater beach | Saltwater beach | restored landfills and grasslands | created, restored and enhanced wetlands | Agricultural landscape | grasslands | grasslands | Urban and urban green space | All land of the continental US | Terrestrial | Lake Michigan & Lake Erie waterfront | Urban greenspace | Louisiana continental shelf | Pelagic | Urban city |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | 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 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to 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-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
EM Organismal Scale
em.detail.orgScaleHelp
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Species | Not applicable | Guild or Assemblage | Guild or Assemblage | Species | Guild or Assemblage | Species | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Species | Species | Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
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None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available | 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-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
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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-376 | EM-437 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-584 ![]() |
EM-605 ![]() |
EM-684 | EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-842 | EM-878 | EM-882 | EM-884 | EM-894 | EM-936 | EM-947 | EM-964 | EM-979 |
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None |
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None |
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None | None |
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None | None | None | None |
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