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-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    EM Short Name
                
             
           
     
                            
                            
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                        ? | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Community flowering date, Central French Alps | Landscape importance for recreation, Europe | Land-use change and recreation, Europe | EnviroAtlas - Water recharge | Blue crabs and SAV, Chesapeake Bay, USA | Coral and land development, St.Croix, VI, 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) | HexSim, tule elk, California, USA | Denitrification rates, Guánica Bay, Puerto Rico | Yasso 15 - soil carbon model | EnviroAtlas - Restorable wetlands | Fish species richness, Puerto Rico, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | SolVES, Bridger-Teton NF, WY | EcoAIM v.1.0 APG, MD | Fish species richness, St. John, USVI, USA | Bird abundance on restored landfills, UK | 
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                    EM Full Name
                
                
             
           
     
                            
                                
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                        ? | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Community weighted mean flowering date, Central French Alps | Landscape importance for recreation, Europe | Land-use change effects on 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 | Coral colony density and land development, St.Croix, Virgin Islands, 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 | HexSim, tule elk, California, USA | Denitrification rates, Guánica Bay, Puerto Rico, USA | Yasso 15 - soil carbon | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Fish species richness, Puerto Rico, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | EcoAIM v.1.0, Aberdeen Proving Ground, MD | Fish species richness, St. John, USVI, 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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                        ? | EU Biodiversity Action 5 | 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 | US EPA | i-Tree | USDA Forest Service | InVEST | InVEST | InVEST | US EPA | US EPA | None | US EPA | EnviroAtlas | None | None | None | None | None | None | 
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                    EM Source Document ID
                
             
           
     | 260 | 260 | 260 | 228 | 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 | 96 | 63 | 96 | 217 | 279 | 310 | 315 | 328 ? Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. | 338 | 342 ? Comment:Webpage pdf users manual for model. | 262 | 355 | 358 | 369 | 374 | 355 | 406 | 
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                    Document Author
                
                
             
           
     
                            
                                
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                        ? | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Haines-Young, R., Potschin, M. and Kienast, F. | Haines-Young, R., Potschin, M. and Kienast, F. | US EPA Office of Research and Development - National Exposure Research Laboratory | Mykoniatis, N. and Ready, R. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | 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 | Huber, P. R., S. E. Greco, N. H. Schumaker, and J. Hobbs | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | US EPA Office of Research and Development - National Exposure Research Laboratory | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | 
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                    Document Year
                
                
             
           
     
                            
                                
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                        ? | 2011 | 2011 | 2011 | 2012 | 2012 | 2013 | 2013 | 2011 | 2011 | 2011 | 2016 | 2009 | 2014 | 2015 | 2014 | 2017 | 2016 | 2013 | 2007 | 2010 | 2014 | 2014 | 2007 | 2011 | 
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                    Document Title
                
             
           
     
                            
                            
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                        ? | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | 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 | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | 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) | A priori assessment of reintroduction strategies for a native ungulate: using HexSim to guide release site selection | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Yasso 15 graphical user-interface manual | EnviroAtlas - National | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | 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 | 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 | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | 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 | 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 | Peer reviewed and published | 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 | Published journal manuscript | other | Website | Published journal manuscript | Published journal manuscript | Not applicable | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | 
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                    EM ID
                
             
           
     
                            
                            
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                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 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/ | http://www.hexsim.net/download | 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.epa.gov/enviroatlas | Not applicable | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | |
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                    Contact Name
                
                
             
           
     
                            
                                
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                        ? | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Marion Potschin | Marion Potschin | EnviroAtlas Team | Nikolaos Mykoniatis | Leah Oliver | Steve Jordan | Leah Oliver | Gordon M. Heisler | Eric Lonsdorf | Gregg Verutes | The Natural Capital Project | P. R. Huber | Susan H. Yee | Jari Liski | EnviroAtlas Team | Simon Pittman | M. Abdalla | Benson Sherrouse | Pieter Booth | Simon Pittman | Lutfor Rahman | 
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                    Contact Address
                
             
           
     | 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 | 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 | National Health and Environmental Research Effects Laboratory | 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 | University of California, Davis, One Shields Ave., Davis, CA 95616, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | Not reported | 1305 East-West Highway, Silver Spring, MD 20910, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Exponent Inc., Bellevue WA | 1305 East-West Highway, Silver Spring, MD 20910, USA | 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
                
             
           
     | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | marion.potschin@nottingham.ac.uk | marion.potschin@nottingham.ac.uk | enviroatlas@epa.gov | Not reported | leah.oliver@epa.gov | steve.jordan@epa.gov | leah.oliver@epa.gov | gheisler@fs.fed.us | ericlonsdorf@lpzoo.org | gverutes@stanford.edu | invest@naturalcapitalproject.org | prhuber@ucdavis.edu | yee.susan@epa.gov | jari.liski@ymparisto.fi | enviroatlas@epa.gov | simon.pittman@noaa.gov | abdallm@tcd.ie | bcsherrouse@usgs.gov | pbooth@ramboll.com | simon.pittman@noaa.gov | lutfor.rahman@northampton.ac.uk | 
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                    EM ID
                
             
           
     
                            
                            
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                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    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., 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: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "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." | 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 novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | 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." | 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) | 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." | AUTHOR'S DESCRIPTION: "HexSim is a simulation framework within which PVA and other models are constructed. HexSim simulations can range from simple and parsimonious, at one extreme, to complex, data intensive, and biologically realistic at the other. Our tule elk simulations were moderately complex, capturing major life history events such as survival, reproduction and movement, while ignoring other details such as impact of environmental stochasticity or the spread of diseases through the population." "One of the features that distinguishes HexSim from its predecessor is the ability to model group, or herd, movement. This is accomplished through use of a ‘‘proto-disperser’’, an imaginary individual that explores the landscape, finds resources, and then serves as a movement target for the other group members who converge on this target. This feature allows for modeling of both individuals and groups. Another useful feature of HexSim is the barriers component. Multiple types of movement barriers can be included in the model, reflecting likely responses to various kinds of blockages to wildlife. Because many of these barriers tend to be human-related, this feature allows for assessing the potential impacts of multiple types of human infrastructure and landscape features on modeled species. This paper examines several reintroduction scenarios for returning an endemic elk subspecies (tule elk; Cervus elaphus nannodes) to a portion of its native range in California, USA." | AUTHOR'S DESCRIPTION: "Improving water quality was an objective of stakeholders in order to improve human health and reduce impacts to coral reef habitats. Four ecosystem services contributing to water quality were identified: denitrification...Denitrification rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature…" | 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." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | 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: " "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: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | 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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                        ? | None identified | None identified | None identified | None identified | None identified | None identified | Not applicable | Not applicable | None identified | Not applicable | None identified | None identified | None identified | None identified | As part of an ongoing restoration program, HexSim was used to evaluate a portion of the former range of tule elk to identify the release scenario producing the most elk and fewest human conflicts. | None identified | None identified | None Identified | None provided | climate change | None | None reported | None provided | None identified | 
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                    Biophysical Context
                
                
             
           
     | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | No additional description provided | Range of tree and impervious covers in urban setting | Submerged Aquatic Vegetation (SAV), eelgrass | nearshore; <1.5 km offshore; <12 m depth | 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 | Located in the Central Valley of California. | No additional description provided | Not applicable | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Rocky mountain conifer forests | Chesapeake bay coastal plain, elev. 60ft. | Hard and soft benthic habitat types approximately to the 33m isobath | 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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                        ? | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | No scenarios presented | Essential or Facultative habitat | Not applicable | 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 | Four release sites; Kesterson, Arena Plains, San Luis, and East Bear Creek. | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | fertilization | N/A | N/A | No scenarios presented | No scenarios presented | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    Method Only, Application of Method or Model Run
                
                
             
           
     
                            
                                
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                        ? | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | 
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                    New or Pre-existing EM?
                
                
             
           
     
                            
                                
                                    em.detail.newOrExistHelp
                                
                                
                            
                            
                        ? | 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 | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | 
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    EM Temporal Extent
                
                
             
           
     
                            
                                
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                        ? | Not reported | 2007-2009 | 2007-2008 | 2000 | 1990-2030 | 2008-2010 | 1993-2011 | 2006-2007 | 2004 | 2006-2007 | May 5-Sept 30 2006 | 2001-2002 | Not applicable | Not applicable | 25 years | 1989 - 2011 ? Comment:6/21/16 BH - Rates were assigned from literature, ranging from 1989 - 2006, and the denitrification rate for urban lawns comes from 2011 literature. | Not applicable | 2006-2013 | 2000-2005 | 1961-1990 | 2004-2008 | 2014 | 2000-2005 | Not applicable | 
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                    EM Time Dependence
                
                
             
           
     
                            
                                
                                    em.detail.timeDependencyHelp
                                
                                
                            
                            
                        ? | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | 
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                    EM Time Reference (Future/Past)
                
                
             
           
     
                            
                                
                                    em.detail.futurePastHelp
                                
                                
                            
                            
                        ? | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | both | Not applicable | Not applicable | Not applicable | Not applicable | 
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                    EM Time Continuity
                
                
             
           
     
                            
                                
                                    em.detail.continueDiscreteHelp
                                
                                
                            
                            
                        ? | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | 
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                    EM Temporal Grain Size Value
                
                
             
           
     
                            
                                
                                    em.detail.tempGrainSizeHelp
                                
                                
                            
                            
                        ? | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | 1 | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 
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                    EM Temporal Grain Size Unit
                
                
             
           
     
                            
                                
                                    em.detail.tempGrainSizeUnitHelp
                                
                                
                            
                            
                        ? | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Hour | Not applicable | Year | Year | Year | Not applicable | Year | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    Bounding Type
                
             
           
     
                            
                            
                                em.detail.boundingTypeHelp
                            
                        ? | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Physiographic or ecological | Physiographic or Ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or Ecological | Geopolitical | Other | Not applicable | Not applicable | Geopolitical | Watershed/Catchment/HUC | Not applicable | Geopolitical | Physiographic or ecological | Point or points | Geopolitical | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | 
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                    Spatial Extent Name
                
             
           
     
                            
                            
                                em.detail.extentNameHelp
                            
                        ? | Central French Alps | Central French Alps | Central French Alps | The EU-25 plus Switzerland and Norway | The EU-25 plus Switzerland and Norway | Durham, NC and vicinity | Chesapeake Bay | St. Croix, U.S. Virgin Islands | Contiguous U.S. | St.Croix, U.S. Virgin Islands | Baltimore, MD | Agricultural landscape, Yolo County, Central Valley | Not applicable | Not applicable | Grasslands Ecological Area | Guanica Bay watershed | Not applicable | conterminous United States | SW Puerto Rico, | Oak Park Research centre | National Park | Aberdeen Proving Ground | SW Puerto Rico, | East Midland | 
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                    Spatial Extent Area (Magnitude)
                
             
           
     
                            
                            
                                em.detail.extentAreaHelp
                            
                        ? | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 10-100 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 | 100-1000 km^2 | 1000-10,000 km^2. | Not applicable | >1,000,000 km^2 | 100-1000 km^2 | 1-10 ha | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    EM Spatial Distribution
                
                
             
           
     
                            
                                
                                    em.detail.distributeLumpHelp
                                
                                
                            
                            
                        ? | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially 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 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) ? 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
                            
                        ? | 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 | 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 | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | 
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                    Spatial Grain Size
                
             
           
     
                            
                            
                                em.detail.spGrainSizeHelp
                            
                        ? | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 1 km x 1 km | 1 km x 1 km | irregular | Not applicable | Not applicable | Not applicable | Not applicable | 10m x 10m | 30 m x 30 m | user-specified | application specific | Not reported | 30 m x 30 m | Not applicable | irregular | not reported | Not applicable | 30m2 | 100m x 100m | not reported | multiple unrelated sites | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    EM Computational Approach
                
                
             
           
     
                            
                                
                                    em.detail.emComputationalApproachHelp
                                
                                
                            
                            
                        ? | Analytic | Analytic | Analytic | Logic- or rule-based | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | 
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                    EM Determinism
                
                
             
           
     
                            
                                
                                    em.detail.deterStochHelp
                                
                                
                            
                            
                        ? | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | 
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                    Statistical Estimation of EM
                
             
           
     
                            
                            
                                em.detail.statisticalEstimationHelp
                            
                        ? | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    Model Calibration Reported?
                
             
           
     
                            
                            
                                em.detail.calibrationHelp
                            
                        ? | No | No | No | No | No | Yes | Yes | Yes | Yes | Yes | Yes | Unclear | Not applicable | Not applicable | Unclear | No | Not applicable | No | No | Yes | No | No ? Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) | No | Not applicable | 
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                    Model Goodness of Fit Reported?
                
                
             
           
     
                            
                                
                                    em.detail.goodnessFitHelp
                                
                                
                            
                            
                        ? | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Yes | Yes | Yes | No | Not applicable | Not applicable | Not applicable | No | Not applicable | No | Yes | Yes ? Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed | Yes | Not applicable | Yes | Not applicable | 
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                    Goodness of Fit (metric| value | unit)
                
                
             
           
     
                            
                                
                                    em.detail.goodnessFitValuesHelp
                                
                                
                            
                            
                        ? | 
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                    Model Operational Validation Reported?
                
                
             
           
     
                            
                                
                                    em.detail.validationHelp
                                
                                
                            
                            
                        ? | Yes | Yes | No | Yes | No | No | Yes | No | No | No | No | Yes ? Comment:Performed just for "Total pollinator abundance service score". | Not applicable | Not applicable | No | No | Not applicable | No | Yes | Yes | No | No | Yes | Not applicable | 
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                    Model Uncertainty Analysis Reported?
                
                
             
           
     
                            
                                
                                    em.detail.uncertaintyAnalysisHelp
                                
                                
                            
                            
                        ? | No | No | No | No | No | No | Yes | 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
                                
                                
                            
                            
                        ? | No | No | No | No | No | Unclear | Yes | No | Yes | No | No | No | Not applicable | Not applicable | No | No | Not applicable | No | Yes | 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
                            
                        ? | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | No | Not applicable | 
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
| None | None | None | None | None | None | 
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Centroid Lat/Long (Decimal Degree)
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    Centroid Latitude
                
                
             
           
     
                            
                                
                                    em.detail.ddLatHelp
                                
                                
                            
                            
                        ? | 45.05 | 45.05 | 45.05 | 50.53 | 50.53 | 35.99 | 36.99 | 17.75 | -9999 | 17.75 | 39.28 | 38.7 | -9999 | -9999 | 37.25 | 17.96 | Not applicable | 39.5 | 17.9 | 52.86 | 43.93 | 39.46 | 17.79 | 52.22 | 
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                    Centroid Longitude
                
                
             
           
     
                            
                                
                                    em.detail.ddLongHelp
                                
                                
                            
                            
                        ? | 6.4 | 6.4 | 6.4 | 7.6 | 7.6 | -78.96 | -75.95 | -64.75 | -9999 | -64.75 | -76.62 | -121.8 | -9999 | -9999 | -120.8 | -67.02 | Not applicable | -98.35 | 67.11 | 6.54 | 110.24 | 76.12 | -64.62 | -0.91 | 
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                    Centroid Datum
                
                
             
           
     
                            
                                
                                    em.detail.datumHelp
                                
                                
                            
                            
                        ? | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | None provided | NAD83 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | 
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                    Centroid Coordinates Status
                
                
             
           
     
                            
                                
                                    em.detail.coordinateStatusHelp
                                
                                
                            
                            
                        ? | Provided | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Not applicable | Not applicable | Estimated | Estimated | Not applicable | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    EM Environmental Sub-Class
                
                
             
           
     
                            
                                
                                    em.detail.emEnvironmentalSubclassHelp
                                
                                
                            
                            
                        ? | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Created Greenspace | None | Near Coastal Marine and Estuarine | 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 | Inland Wetlands | Forests | Agroecosystems | Grasslands | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Forests | Grasslands | Scrubland/Shrubland | Tundra | Agroecosystems | Near Coastal Marine and Estuarine | 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
                                
                                
                            
                            
                        ? | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not applicable | Not applicable | Urban areas including streams | Yes | stony coral reef | Wetlands (multiple types) | stony coral reef | Urban landscape and surrounding area | Cropland and surrounding landscape | user specified | Terrestrial environments, but not specified for methods | Terrestrial mosaic | Thirteen land use land cover classes were used | Not applicable | Terrestrial | shallow coral reefs | farm pasture | Montain forest | Coastal Plain | shallow coral reefs | restored landfills and conserved grasslands | 
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                    EM Ecological Scale
                
                
             
           
     
                            
                                
                                    em.detail.ecoScaleHelp
                                
                                
                            
                            
                        ? | Not applicable | Not applicable | 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 | 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 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 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 is finer than that of 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
                            
                        ? | EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
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                    EM Organismal Scale
                
                
             
           
     
                            
                                
                                    em.detail.orgScaleHelp
                                
                                
                            
                            
                        ? | Community | Community | Community | Not applicable | Not applicable | Community | Yes | Guild or Assemblage | Not applicable | Guild or Assemblage | Not applicable | Species | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Individual or population, within a species | 
Taxonomic level and name of organisms or groups identified
| EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
| 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 | 
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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)
| EM-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
| 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-66 | EM-68 | EM-71 | EM-121 | EM-125   | EM-142 | EM-185 | EM-194 | EM-196 | EM-260 | EM-306 | EM-338   | EM-367 | EM-374 | EM-403   | EM-424 | EM-466 | EM-492 | EM-590 | EM-598 | EM-628 | EM-647 | EM-699 | EM-836 | 
| None | 
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 | None | None | None | None | None | None | 
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