EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
EM Short Name
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Green biomass production, Central French Alps | FORCLIM v2.9, Santiam watershed, OR, USA | MIMES: For Massachusetts Ocean (v1.0) | VELMA soil temperature, Oregon, USA | 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) | VELMA v2.0, Ohio, USA | SolVES, Shoshone NF, WY | Waterfowl pairs, CREP wetlands, Iowa, USA | EcoAIM v.1.0 APG, MD | RUM: Valuing fishing quality, Michigan, 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 | ARIES: Crop pollination in Santa Fe, NM, USA | Human well-being index, Pensacola Bay, Florida | Pollutant dispersion by vegetation barriers | Air pollution removal by green roofs, Chicago, USA |
EM Full Name
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Green biomass production, Central French Alps | FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, 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) | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | SolVES, Social Values for Ecosystem Services, Shoshone National Forest, WY | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | EcoAIM v.1.0, Aberdeen Proving Ground, MD | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, 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 | Artificial intelligence for Ecosystem Services (ARIES); Crop pollination, Santa Fe, New Mexico, USA | Human well-being index (HWBI), Pensacola Bay, Florida | Pollutant dispersion by vegetation barriers | Air pollution removal by green roofs, Chigago, USA |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | None | InVEST | None | US EPA | None | None | None | None | US EPA | None | None | None | None | ARIES | US EPA | US EPA | None |
EM Source Document ID
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260 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
316 | 317 | 335 | 335 | 335 | 335 |
342 ?Comment:Webpage pdf users manual for model. |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
352 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
369 | 372 | 374 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
386 | 389 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
400 | 405 | 411 | 418 | 435 |
438 ?Comment:Document 439 is an additional source for this EM. |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Altman, I., R.Boumans, J. Roman, L. Kaufman | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | 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. and O. Gallego | 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 | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | 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 | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Yee, S.H., Paulukonis, E., Simmons, C., Russell, M., Fullford, R., Harwell, L., and L.M. Smith | Hashad, K. B. Yang, J. T. Steffens, R. W. Baldauf, P. Deshmukh, K. M. Zhang | Yang, J., Q. Yu and P. Gong |
Document Year
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2011 | 2007 | 2012 | 2013 | 2014 | 2014 | 2014 | 2014 | 2016 | 2018 | 2018 | 2018 | 2014 | 2010 | 2014 | 2014 | 2018 | 2013 | 2012 | 2015 | 2008 | 2018 | 2021 | 2021 | 2008 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | 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 (NTT) User Manual | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations | Valuing recreational fishing quality at rivers and streams | 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 | Towards globally customizable ecosystem service models | Projecting effects of land use change on human well being through changes in ecosystem services | Parameterizing pollutant dispersion downwind of roadside vegetation barriers | Quantifying air pollution removal by green roofs in Chicago |
Document Status
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Peer reviewed and published | Peer reviewed and published | 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 and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Not applicable | Published journal manuscript | webpage | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review | Published journal manuscript |
EM ID
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
Not applicable | Not applicable | http://www.afordablefutures.com/orientation-to-what-we-do | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | 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://github.com/integratedmodelling/im.aries.global | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Richard T. Busing | Irit Altman | Alex Abdelnour | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Jari Liski | Michelle Ward |
Ali Saleh ?Comment:Phone # 254-968-9079 |
Heather Golden | Benson Sherrouse | David Otis | Pieter Booth | Richard Melstrom | Kate K, Mulvaney | Sam Tarrant | Chris Murphy | Neal Williams | Sam Riffell | Javier Martinez | Susan Yee | K. Max Zhang | Jun Yang |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Boston University, Portland, Maine | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, 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 | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Exponent Inc., Bellevue WA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | 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 | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Gulf Ecosystem Measurement and Modeling Division, Center for Environmental Measurement and Modeling, US Environmental Prntection Agency, Gulf Breeze, FL 32561, USA | Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA | Department of Landscape Architecture and Horticulture, Temple University, 580 Meetinghouse Road, Ambler, PA 19002, USA. |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | rtbusing@aol.com | iritaltman@bu.edu | abdelnouralex@gmail.com | 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@tarleton.edu | Golden.Heather@epa.gov | bcsherrouse@usgs.gov | dotis@iastate.edu | pbooth@ramboll.com | melstrom@okstate.edu | Mulvaney.Kate@EPA.gov | sam.tarrant@rspb.org.uk | chris.murphy@idfg.idaho.gov | nmwilliams@ucdavis.edu | sriffell@cfr.msstate.edu | javier.martinez@bc3research.org | yee.susan@epa.gov | kz33@cornell.edu | juny@temple.edu |
EM ID
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045. | 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. | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | 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." | AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)." | 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: “Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders. With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, social value information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.” | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | [ABSTRACT: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. " | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT: "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:Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.] | ABSTRACT: "Changing patterns of land use, temperature, and precipitation are expected to impact ecosystem se1vices, including water quality and quantity, buffering of extreme events, soil quality, and biodiversity. Scenario ana lyses that link such impacts on ecosystem se1vices to human well-being may be valuable in anticipating potential consequences of change that are meaningful to people living in a community. Ecosystem se1vices provide munerous benefits to community well-being, including living standards, health, cultural fulfillment, education, and connection to nature. Yet assessments of impacts of ecosystem se1vices on human well-being have largely focused on human health or moneta1y benefits (e.g. market values). This study applies a human well-being modeling framework to demonsffate the potential impacts of alternative land use scenarios on multi-faceted components of human well-being through changes in ecosystem se1vices (i.e., ecological benefits functions). The modeling framework quantitatively defines these relationships in a way that can be used to project the influence of ecosystem se1vice flows on indicators of human well-being, alongside social se1vice flows and economic se1vice flows. Land use changes are linked to changing indicators of ecosystem se1vices through the application of ecological production functions. The approach is demonstrated for two future land use scenarios in a Florida watershed, representing different degrees of population growth and environmental resource protection. Increasing rates of land development were almost universally associated with declines in ecosystem se1vices indicators and associated indicators of well-being, as natural ecosystems were replaced by impe1vious surfaces that depleted the ability of ecosystems to buffer air pollutants, provide habitat for biodiversity, and retain rainwater. Scenarios with increases in indicators of ecosystem se1vices, however, did not necessarily translate into increases in indicators of well-being, due to cova1ying changes in social and economic se1vices indicators. The approach is broadly ffansferable to other communities or decision scenarios and se1ves to illustrate the potential impacts of changing land use on ecosystem se1vices and human well-being. " | ABSTRACT: "Communities living and working in near-road environments are exposed to elevated levels of traffic-related air pollution (TRAP), causing adverse health effects. Roadside vegetation may help reduce TRAP through enhanced deposition and mixing….there are no studies that developed a dispersion model to characterize pollutant concentrations downwind of vegetation barriers. To account for the physical mechanisms, by which the vegetation barrier deposits and disperses pollutants, we propose a multi-region approach that describes the parameters of the standard Gaussian equations in each region. The four regions include the vegetation, a downwind wake, a transition, and a recovery zone. For each region, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition which is a major factor in pollutant reduction by vegetation barriers. We generated data from 75 (CFD)-based simulations, using the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model, to parameterize the Gaussian-based equations. The simulations used reflected a wide range of vegetation barriers, with heights from 2-10 m, and various densities, represented by leaf area index values from 4-11, and evaluated under different urban conditions, represented by wind speeds from 1-5 m/s. The CTAG model has been evaluated against two field measurements to ensure that it can properly represent the vegetation barrier’s pollutant deposition and dispersion. The proposed multi-region Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing deposition. The multi-region model’s normalized mean error (NME) ranged between 0.18-0.3, the fractional bias (FB) ranged between -0.12-0.09, and R2 value ranged from 0.47-0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable range. Even though the multi-region model is parameterized for coniferous trees, our sensitivity study indicates that the parameterized Gaussian-based model can provide useful predictions for hedge/bushes vegetative barriers as well." ADDITIONAL DESCRIPTION: Detailed variable relationships are described in the source document. The VRD associated with the ESML entry provides variables in a simplified form. | ABSTRACT: "The level of air pollution removal by green roofs in Chicago was quantified using a dry deposition model. The result showed that a total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs in one year with O3 accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2 (7%). The highest level of air pollution removal occurred in May and the lowest in February. The annual removal per hectare of green roof was 85 kg/ha/yr. The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs. Although costly, the installation of green roofs could be justified in the long run if the environmental benefits were considered. The green roof can be used to supplement the use of urban trees in air pollution control, especially in situations where land and public funds are not readily available." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | Future rock lobster fisheries management | None identified | None identified | None | None identified | None reported | None identified | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | None identified | None identrified | None reported | None identified | None identified | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | No additional description provided | No additional description provided | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Not applicable | No additional description provided | No additional description provided | 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. | Rocky mountain conifer forests | Prairie pothole region of north-central Iowa | Chesapeake bay coastal plain, elev. 60ft. | stream and river reaches of Michigan | 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 | Fire watersheds near Albuquerque, NM. | N/A | Communities living and working in near-road environments | No additional description provided |
EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | Land Management (3); Climate Change (3) | 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) | No scenarios presented | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | N/A | No scenarios presented | N/A | targeted sport fish biomass | No scenarios presented | No scenarios presented | Sites, function or habitat focus | Varied wildflower planting mixes of annuals and perennials | N/A | N/A | N/A | None scenarios presented | No scenarios presented |
EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs differentiated by scenario combination. |
Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
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New or revised model | Application of existing model | 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 | Application of existing model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
EM Temporal Extent
em.detail.tempExtentHelp
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2007-2009 | 1990-2050 | Not applicable | 1969-2008 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | Not applicable | 1986-2115 | 35 yr | Jan 1, 2009 to Dec 31, 2011 | 2004-2008 | 2002-2007 | 2014 | 2008-2010 | July 1, 2011 to June 31, 2016 | 2007-2008 | 2010-2012 | 2011-2012 | 2008 | 2010 | 2010 | Not applicable | July 2006 to July 2007 |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | Not applicable | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | future time | future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Year | Year | Day | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Day | Day | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Month |
EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Not applicable | Geopolitical | Not applicable | Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Watershed/Catchment/HUC | 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 | Geopolitical | Geopolitical | Not applicable | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | South Santiam watershed | Massachusetts Ocean | H. J. Andrews LTER WS10 | 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 | Not applicable | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | National Forest | CREP (Conservation Reserve Enhancement Program) wetland sites | Aberdeen Proving Ground | HUCS in Michigan | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | East Midlands | Wetlands in idaho | Agricultural plots | Piedmont Ecoregion | Rwanda and Burndi | Pensacola Bay Region | Not applicable | Chicago |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 10-100 ha | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | Not applicable | 100-1000 km^2 | Not applicable | 10-100 ha | 1000-10,000 km^2. | 1-10 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 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 | 10,000-100,000 km^2 | 100-1000 km^2 | Not applicable | 100-1000 km^2 |
EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
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) ?Comment:See below, grain includes vertical, subsurface dimension. |
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) ?Comment:500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams. |
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) |
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 | volume, for 3-D 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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | 0.08 ha | 1 km x1 km | 30 m x 30 m surface pixel and 2-m depth soil column | 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 | 30m2 | multiple, individual, irregular shaped sites | 100m x 100m | reach in HUC | by beach site | multiple unrelated locations | Not applicable | Not applicable | Not applicable | 1km | county | user defined | plot (green roof) size |
EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Numeric | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic |
Statistical Estimation of EM
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EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | Yes | Yes | Yes | Yes | Not applicable | No | Not applicable | Yes | No | Unclear |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
No | Yes | Not applicable | No | No | Yes | Unclear | Unclear | Yes | Unclear |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | No | No | No | No | No | No | No | Not applicable | No | Not applicable |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
Yes | No | Not applicable | Yes | No | Not applicable | No | No | No | No | Not applicable | Not applicable | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None |
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None | None |
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None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | 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. |
Unclear | Yes | No | Unclear | No | No | No | Not applicable | No | No | No | No | No | Not applicable | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | Not applicable | No | Not applicable | No | No | No | No | No | No | Not applicable | No | No | No | No | Yes | Not applicable | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | No | No | Not applicable | No | Not applicable | No | No | No |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
No |
No ?Comment:n/a |
Not applicable | No | No | Yes | No | Yes | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | N/A | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
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None |
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None | None | None | None | None | None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
None | None |
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None |
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None |
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None | None | None | None | None | None |
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None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 44.24 | 41.72 | 44.25 | 17.73 | 17.73 | 17.73 | 17.73 | Not applicable | -34.18 | Not applicable | 39.19 | 43.98 | 42.62 | 39.46 | 45.12 | 41.64 | 52.22 | 44.06 | 29.4 | 36.23 | -2.59 | 30.05 | Not applicable | 41.88 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | -122.24 | -69.87 | -122.33 | -64.77 | -64.77 | -64.77 | -64.77 | Not applicable | 18.35 | Not applicable | -84.29 | 109.52 | -93.84 | 76.12 | 85.18 | -70.29 | -0.91 | -114.69 | -82.18 | -81.9 | 29.97 | -87.61 | Not applicable | 87.65 |
Centroid Datum
em.detail.datumHelp
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WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Not applicable | Provided |
EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Forests | Near Coastal Marine and Estuarine | Forests | 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 | Forests | Inland Wetlands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland | Rivers and Streams | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | primarily Conifer Forest | None identified | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | 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 | Agroecosystems | Mixed land cover suburban watershed | Montain forest | Wetlands buffered by grassland set in agricultural land | Coastal Plain | stream reaches | Saltwater beach | restored landfills and grasslands | created, restored and enhanced wetlands | Agricultural landscape | grasslands | varied | Mixed | Communities living and working in near-road environments | urban green roofs |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | 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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Species | 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 | Species | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Species | Guild or Assemblage | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
None Available |
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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 |
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None Available |
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None Available |
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None Available |
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None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
None |
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None |
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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-65 |
EM-208 ![]() |
EM-376 | EM-379 | EM-444 | EM-455 | EM-459 | EM-462 | EM-466 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-626 |
EM-632 ![]() |
EM-647 |
EM-660 ![]() |
EM-685 |
EM-697 ![]() |
EM-743 ![]() |
EM-784 ![]() |
EM-841 | EM-856 |
EM-880 ![]() |
EM-942 | EM-945 |
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None |
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None |
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None |
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None | None |
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None |
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None | None |