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
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EM ID
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Soil carbon and plant traits, Central French Alps | Landscape importance for recreation, Europe | EnviroAtlas - Water recharge | Blue crabs and SAV, Chesapeake Bay, USA | N removal by wetlands, Contiguous USA | Coral taxa and land development, St.Croix, VI, USA | Urban Temperature, Baltimore, MD, USA | InVEST crop pollination, California, USA | InVEST Coastal Blue Carbon | InVEST carbon storage and sequestration (v3.2.0) | VELMA plant-soil, Oregon, USA | Mangrove connectivity, St. Croix, USVI | Yasso 15 - soil carbon model | Yasso07 v1.0.1, Switzerland | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | N removal by wetland restoration, Midwest, USA | SolVES, Bridger-Teton NF, WY | EcoAIM v.1.0 APG, MD | Beach visitation, Barnstable, MA, USA | Bird abundance on restored landfills, UK |
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EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Soil carbon potential estimated from plant functional traits, Central French Alps | Landscape importance for recreation, Europe | US EPA EnviroAtlas - Annual water recharge by tree cover; Example is shown for Durham NC and vicinity, USA | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | Nitrogen removal by wetlands as a function of loading, Contiguous USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Urban Air Temperature Change, Baltimore, MD, USA | InVEST crop pollination, California, USA | InVEST v3.0 Coastal Blue Carbon | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) plant-soil, Oregon, USA | Mangrove connectivity (of reef), St. Croix, USVI | Yasso 15 - soil carbon | Yasso07 v1.0.1 forest litter decomposition, Switzerland | DeNitrification-DeComposition simulation of N2O flux Ireland | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | EcoAIM v.1.0, Aberdeen Proving Ground, MD | Beach visitation, Barnstable, Massachusetts, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK |
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EM Source or Collection
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Envision | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
None | US EPA | US EPA | i-Tree | USDA Forest Service | InVEST | InVEST | InVEST | US EPA | US EPA | None | None | None | None | None | None | US EPA | None |
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EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
260 | 260 | 260 | 228 |
223 ?Comment:Parameter default values used in the i-Tree Hydro model were obtained from the i-Tree website (Document ID 198, EM 137). |
292 ?Comment:Conference paper |
63 | 96 | 217 | 279 | 310 | 315 | 317 | 335 |
342 ?Comment:Webpage pdf users manual for model. |
343 | 358 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
369 | 374 | 386 | 406 |
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Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Haines-Young, R., Potschin, M. and Kienast, F. | US EPA Office of Research and Development - National Exposure Research Laboratory | Mykoniatis, N. and Ready, R. | Jordan, S., Stoffer, J. and Nestlerode, J. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Natural Capital Project | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton |
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Document Year
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2008 | 2011 | 2011 | 2011 | 2012 | 2013 | 2013 | 2011 | 2011 | 2016 | 2009 | 2014 | 2015 | 2013 | 2014 | 2016 | 2014 | 2010 | 2006 | 2014 | 2014 | 2018 | 2011 |
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Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | EnviroAtlas - Featured Community | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Modelling pollination services across agricultural landscapes | Blue Carbon model - InVEST (v3.0) | Carbon storage and sequestration - InVEST (v3.2.0) | 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 | Yasso 15 graphical user-interface manual | Validating tree litter decomposition in the Yasso07 carbon model | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not formally documented | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Conference proceedings | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | other | Website | Published journal manuscript | Published journal manuscript | Not applicable | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Michael R. Guzy | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Marion Potschin | EnviroAtlas Team | Nikolaos Mykoniatis | Steve Jordan | Leah Oliver | Gordon M. Heisler | Eric Lonsdorf | Gregg Verutes | The Natural Capital Project | Alex Abdelnour | Susan H. Yee | Jari Liski |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
M. Abdalla | William G. Crumpton | Benson Sherrouse | Pieter Booth | Kate K, Mulvaney | Lutfor Rahman |
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Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Not reported | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561 | National Health and Environmental Research Effects Laboratory | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Stanford University | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Dept. 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 | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Exponent Inc., Bellevue WA | Not reported | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK |
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Contact Email
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Not reported | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | marion.potschin@nottingham.ac.uk | enviroatlas@epa.gov | Not reported | steve.jordan@epa.gov | leah.oliver@epa.gov | gheisler@fs.fed.us | ericlonsdorf@lpzoo.org | gverutes@stanford.edu | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | yee.susan@epa.gov | jari.liski@ymparisto.fi | markus.didion@wsl.ch | abdallm@tcd.ie | crumpton@iastate.edu | bcsherrouse@usgs.gov | pbooth@ramboll.com | Mulvaney.Kate@EPA.gov | lutfor.rahman@northampton.ac.uk |
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EM ID
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "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., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter 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…Litter biomass production for each pixel was calculated and mapped using model estimates...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 litter mass. 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." | 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., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content 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…Fodder crude protein for each pixel was calculated and mapped using model estimates...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 fodder protein content. 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." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Recreation” ... The potential to deliver services is assumed to be influenced by land-use ... and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Recreation… is broadly defined as all areas where landscape properties are favourable for active recreation purposes." | The Water Recharge model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results. Calibration coefficients (0-1 with 1.0 = perfect fit) were calculated for peak flow, base flow, and balance flow (peak and base). To estimate the effect of trees at the block group level for Durham, the Hydro model was run for: Gauging Station Name: SANDY CREEK AT CORNWALLIS RD NEAR DURHAM, NC, Gauging Station Location: 35°58'59.6",-78°57'24.5", Gauging Station Number: 0209722970. After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed. To estimate block group effects, the block group was assumed to act similarly to the watershed in terms of hydrologic effects. To estimate the block group effect, the outputs of the watershed were determined for each possible combination of tree cover (0-100%) and impervious cover (0-100%). Thus, there were a total of 10,201 possible responses (101 x 101). For each block group, the percent tree cover and percent impervious cover combination (e.g., 30% tree / 20% impervious) was matched to the appropriate watershed hydrologic response output for that combination. The hydrologic response outputs were calculated as either percent change or absolute change in units of cubic meters of water per square meter of land area for water flow or kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level. | ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | ABSTRACT: "We compiled published data from wetland studies worldwide to estimate total Nr removal and to evaluate factors that influence removal rates. Over several orders of magnitude in wetland area and Nr loading rates, there is a positive, near-linear relationship between Nr removal and Nr loading. The linear model (null hypothesis) explains the data better than either a model of declining Nr removal efficiency with increasing Nr loading, or a Michaelis–Menten (saturation) model." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery. " | Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | 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 plant-soil model (Figure (A3)) that simulates ecosystem carbon storage and the cycling of C and N between a plant biomass layer and the active soil pools. Specifically, the plant-soil model simulates the interaction among aboveground plant biomass, soil organic carbon (SOC), soil nitrogen including dissolved nitrate (NO3), ammonium (NH4), and organic nitrogen, as well as DOC (equations (A7)–(A12)). Daily atmospheric inputs of wet and dry nitrogen deposition are accounted for in the ammonium pool of the shallow soil layer (equation (A13)). Uptake of ammonium and nitrate by plants is modeled using a Type II Michaelis-Menten function (equation (A14)). Loss of plant biomass is simulated through a density-dependent mortality. The mortality rate and the nitrogen uptake rate mimic the exponential increase in biomass mortality and the accelerated growth rate, respectively, as plants go through succession and reach equilibrium (equations (A14)–(A18)). Vertical transport of nutrients from one layer to another in a soil column is a function of water drainage (equations (A19)–(A22)). Decomposition of SOC follows first-order kinetics controlled by soil temperature and moisture content as described in the terrestrial ecosystem model (TEM) of Raich et al. [1991] (equations (A23)–(A26)). Nitrification (equations (A27)–(A30)) and denitrification (equations (A31)–(A34)) were simulated using the equations from the generalized model of N2 and N2O production of Parton et al. [1996, 2001] and Del Grosso et al. [2000]. [12] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water and nutrients (Figure (A1)). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow i | 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…An alternative method to estimate potential fisheries production is to quantify not just the percent coverages of key habitats (F1)–(F6), but the degree of connectivity among those habitats. Many species that utilize coral reef habitat as adults are dependent on mangrove or seagrass nursery habitats as juveniles (Nagelkerken et al., 2000; Dorenbosch et al., 2006). In the Caribbean, the community structure or adult biomass of more than 150 reef fish species was affected by the presence of mangroves in the vicinity of reefs (Mumby et al., 2004). The value of habitat for fish production will therefore depend on the degree of connectivity between reefs and nearby mangroves (Mumby, 2006) and can be estimated as Cij = D - √(mix-rix)2+(mjy-rjy)2 where Cij is the connectivity between each reef cell i and nearby mangrove cell j, and D is the maximum migratory distance between mangroves and reefs (assumed to be 10 km), weighted by the distance between cells (x,y coordinates) such that shorter distances result in greater connectivity. The row sums then give the total connectivity of each reef cell to mangroves." | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. | ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | [ABSTRACT: " "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, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | [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: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." |
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Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | None identified | None identified | None identified | Not applicable | None identified | Not applicable | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | climate change | None identified | None | None reported | To assess the number of people who would be impacted by beach closures. | None identified |
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Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Range of tree and impervious covers in urban setting | Submerged Aquatic Vegetation (SAV), eelgrass | Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested | nearshore; <1.5 km offshore; <12 m depth | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | No additional description provided | Land use land class; habitat type | Not applicable | 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 | Not applicable | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | No additional description provided | Rocky mountain conifer forests | Chesapeake bay coastal plain, elev. 60ft. | Four separate beaches within the community of Barnstable | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). |
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EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Essential or Facultative habitat | No scenarios presented | Not applicable | No scenarios presented | No scenarios presented | Land use land cover changes; habitat disturbance | Optional future scenarios for changed LULC and wood harvest | Forest management (harvest/no harvest) | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
fertilization | More conservative, average and less conservative nitrate loss rate | N/A | N/A | No scenarios presented | No scenarios presented |
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EM ID
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application | Method Only |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Application of existing model ?Comment:EnviroAtlas uses an application of the i-Tree Hydro 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 | Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
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EM ID
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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EM Temporal Extent
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1990-2050 | Not reported | 2007-2009 | Not reported | 2000 | 2008-2010 | 1993-2011 | 2004 | 2006-2007 | May 5-Sept 30 2006 | 2001-2002 | Not applicable | Not applicable | 1969-2008 | 2006-2007, 2010 | Not applicable | 1993-2013 | 1961-1990 | 1973-1999 | 2004-2008 | 2014 | 2011 - 2016 | Not applicable |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | future time | both | future time | Not applicable | Not applicable | past time | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | discrete | discrete | discrete | Not applicable | discrete | discrete | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable |
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EM Temporal Grain Size Value
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2 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | 1 | Not applicable | 1 | 1 | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Hour | Not applicable | Year | Year | Day | Not applicable | Year | Year | Day | Day | Not applicable | Not applicable | Day | Not applicable |
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EM ID
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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Bounding Type
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or Ecological | Geopolitical | Other | Not applicable | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Geopolitical | Point or points | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) |
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Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Central French Alps | Central French Alps | Central French Alps | The EU-25 plus Switzerland and Norway | Durham, NC and vicinity | Chesapeake Bay | Contiguous U.S. | St.Croix, U.S. Virgin Islands | Baltimore, MD | Agricultural landscape, Yolo County, Central Valley | Not applicable | Not applicable | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Not applicable | Switzerland | Oak Park Research centre | Upper Mississippi River and Ohio River basins | National Park | Aberdeen Proving Ground | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | East Midland |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | Not applicable | Not applicable | 10-100 ha | 100-1000 km^2 | Not applicable | 10,000-100,000 km^2 | 1-10 ha | >1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 ha | 1000-10,000 km^2. |
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EM ID
em.detail.idHelp
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?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) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (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 | volume, for 3-D feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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varies | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 1 km x 1 km | irregular | Not applicable | Not applicable | Not applicable | 10m x 10m | 30 m x 30 m | user-specified | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | Not applicable | 5 sites | Not applicable | 1 km2 | 30m2 | 100m x 100m | by beach site | multiple unrelated sites |
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EM ID
em.detail.idHelp
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic | Analytic | Analytic | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Numeric | Numeric | Numeric | Numeric | Analytic | Analytic |
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EM Determinism
em.detail.deterStochHelp
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stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Comment:Agent based modeling results in response indices. |
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EM ID
em.detail.idHelp
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | No | No | No | No | Yes | Yes | Yes | Yes | Yes | Unclear | Not applicable | Not applicable | Yes | Yes | Not applicable | No | Yes | No | No |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
Yes | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | Yes | No | No | Yes | Yes | Yes | Yes | Yes | No | Not applicable | Not applicable | No | No | Not applicable | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
No | Yes | Not applicable | No | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None |
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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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None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | Yes | No | Yes | No | Yes | No | No | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
Not applicable | Not applicable | No | Yes | Not applicable | Yes | Yes |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | Yes | Yes | Yes | No | No | Not applicable | Not applicable | No | No | Not applicable | No | No | No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | No | No | Unclear | Yes | Yes | No | No | No | Not applicable | Not applicable | Yes | No | Not applicable | No | No | No | No |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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None | None |
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None | None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
| None | None | None | None | None | None |
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None |
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None | None | None | None | None |
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None | None | None | None | None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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Centroid Latitude
em.detail.ddLatHelp
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44.11 | 45.05 | 45.05 | 45.05 | 50.53 | 35.99 | 36.99 | -9999 | 17.75 | 39.28 | 38.7 | -9999 | -9999 | 44.25 | 17.73 | Not applicable | 46.82 | 52.86 | 40.6 | 43.93 | 39.46 | 41.64 | 52.22 |
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Centroid Longitude
em.detail.ddLongHelp
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-123.09 | 6.4 | 6.4 | 6.4 | 7.6 | -78.96 | -75.95 | -9999 | -64.75 | -76.62 | -121.8 | -9999 | -9999 | -122.33 | -64.77 | Not applicable | 8.23 | 6.54 | -88.4 | 110.24 | 76.12 | -70.29 | -0.91 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | NAD83 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | WGS84 | Not applicable | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Provided | Provided | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Not applicable | Not applicable | Provided | Estimated | Not applicable | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated |
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EM ID
em.detail.idHelp
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Created Greenspace | None | Inland Wetlands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Inland Wetlands | Near Coastal Marine and Estuarine | Not applicable | Rivers and Streams | Ground Water | Forests | Near Coastal Marine and Estuarine | Forests | Grasslands | Scrubland/Shrubland | Tundra | Forests | Agroecosystems | Rivers and Streams | Inland Wetlands | Agroecosystems | Forests | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Agricultural-urban interface at river junction | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not applicable | Urban areas including streams | Yes | Wetlands (multiple types) | stony coral reef | Urban landscape and surrounding area | Cropland and surrounding landscape | user specified | Terrestrial environments, but not specified for methods | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coral reefs and mangroves | Not applicable | forests | farm pasture | Agroecosystems and associated drainage and wetlands | Montain forest | Coastal Plain | Saltwater beach | restored landfills and conserved grasslands |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Yes | 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 | Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community | Community | Community | Not applicable | Community | Yes | Not applicable | Guild or Assemblage | Not applicable | Species | Not applicable | Not applicable | Not applicable | Community | Species | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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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)
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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None |
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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)
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EM-12 |
EM-66 | EM-68 | EM-83 | EM-121 | EM-142 | EM-185 | EM-196 | EM-260 | EM-306 |
EM-338 |
EM-367 | EM-374 |
EM-380 |
EM-464 | EM-466 |
EM-467 |
EM-598 | EM-627 | EM-628 | EM-647 | EM-684 | EM-836 |
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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 |
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