EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
EM Short Name
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EnviroAtlas-Air pollutant removal | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Community flowering date, Central French Alps | Soil carbon and plant traits, Central French Alps | Benthic habitat associations, Willapa Bay, OR, USA | Landscape importance for recreation, Europe | Land-use change and crop-based production, Europe | Flood regulation capacity, Etropole, Bulgaria | C Sequestration and De-N, Tampa Bay, FL, USA | Coastal protection, Europe | InVEST crop pollination, California, USA | EPA H2O, Tampa Bay Region, FL,USA | Decrease in wave runup, St. Croix, USVI | Value of a reef dive site, St. Croix, USVI | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | SolVES, Pike & San Isabel NF, WY | Alwife phosphorus flux in lakes, Connecticut, USA | WTP for a beach day, Massachusetts, USA | Northern Pintail recruits, CREP wetlands, IA, USA | Pollinators on landfill sites, United Kingdom | MMI method for aquatic surveys | Mourning dove abundance, Piedmont region, USA | Health, safety and greening urban space, PA, USA | GI toolkit users guide |
EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Community weighted mean flowering date, Central French Alps | Soil carbon potential estimated from plant functional traits, Central French Alps | Benthic macrofaunal habitat associations, Willapa Bay, OR, USA | Landscape importance for recreation, Europe | Land-use change effects on crop-based production, Europe | Flood regulation capacity of landscapes, Municipality of Etropole, Bulgaria | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Coastal protection, Europe | InVEST crop pollination, California, USA | EPA H2O, Tampa Bay Region, FL, USA | Decrease in wave runup (by reef), St. Croix, USVI | Value of a dive site (reef), St. Croix, USVI | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA | Northern Pintail duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Pollinating insects on landfill sites, East Midlands, United Kingdon | Multimetric Indice (MMI) method for large scale aquatic surveys | Mourning dove abundance, Piedmont ecoregion, USA | Health, safety and greening urban vacant space, Pennsylvania, USA | Green Infrastructure valuation toolkit users guide |
EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | InVEST | US EPA | US EPA | US EPA | None | None | None | None | US EPA | None | None | US EPA | None | None | None |
EM Source Document ID
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223 | 260 | 260 | 260 | 260 | 39 | 228 | 228 | 248 | 186 | 296 | 279 | 321 | 335 | 335 | 358 | 367 | 369 | 383 | 386 |
372 ?Comment:Document 373 is a secondary source for this EM. |
389 | 403 | 405 | 419 | 474 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Ferraro, S. P. and Cole, F. A. | Haines-Young, R., Potschin, M. and Kienast, F. | Haines-Young, R., Potschin, M. and Kienast, F. | Nedkov, S., Burkhard, B. | Russell, M. and Greening, H. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Stoddard, J.L., Herlihy, A.T., Peck, D.V., Hughes, R.M., Whittier, T.R., and E. Tarquinio | Riffel, S., Scognamillo, D., and L. W. Burger | Branas, C. C., R. A. Cheney, J. M. MacDonald, V. W. Tam, T. D. Jackson, and T. R. Ten Havey | Genecon LLP. |
Document Year
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2013 | 2011 | 2011 | 2011 | 2011 | 2007 | 2012 | 2012 | 2012 | 2013 | 2013 | 2009 | 2015 | 2014 | 2014 | 2010 | 2017 | 2014 | 2010 | 2018 | 2010 | 2013 | 2008 | 2008 | 2011 | 2010 |
Document Title
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EnviroAtlas - Featured Community | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA | 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 | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Estimating benefits in a recovering estuary: Tampa Bay, Florida | Assessment of coastal protection as an ecosystem service in Europe | Modelling pollination services across agricultural landscapes | EPA H20 User Manual | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | 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 | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | A process for creating multimetric indices for large-scale A process for creating multimetic indices for large-scale aquatic surveys | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | A difference-in-differences analysis of health, safety, and greening vacant urban space | Building natural value for sustainable economic development The green infrastructure valuation toolkit user guide |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report |
EM ID
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | Not applicable | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.merseyforest.org.uk/services/gi-val/ | |
Contact Name
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EnviroAtlas Team | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Steve Ferraro | Marion Potschin | Marion Potschin | Stoyan Nedkov | M. Russell | Camino Liquete | Eric Lonsdorf | Marc J. Russell, Ph.D. | Susan H. Yee | Susan H. Yee | M. Abdalla | Justin Bousquin | Benson Sherrouse | Derek C. West | Kate K, Mulvaney | David Otis | Sam Tarrant | John Stoddard | Sam Riffell | Charles C. Branas | The Mercey Forest |
Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365 | 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 | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Not reported | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | 200 SW 35th St., Corvallis, OR 97333 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 936, 423 Guardian Drive, Philadelphia, PA 19104-6021 | Moss Ln, Woolston, Warrington WA3 6QX, United Kingdom |
Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ferraro.steven@epa.gov | marion.potschin@nottingham.ac.uk | marion.potschin@nottingham.ac.uk | snedkov@abv.bg | Russell.Marc@epamail.epa.gov | camino.liquete@gmail.com | ericlonsdorf@lpzoo.org | russell.marc@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | abdallm@tcd.ie | bousquin.justin@epa.gov | bcsherrouse@usgs.gov | derek.west@yale.edu | Mulvaney.Kate@EPA.gov | dotis@iastate.edu | sam.tarrant@rspb.org.uk | stoddard.john@epa.gov | sriffell@cfr.msstate.edu | cbranas@upenn.edu | mail@merseyforest.org.uk |
EM ID
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... 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: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | 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: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | AUTHOR'S DESCRIPTION: "In this paper we report the results of 2 estuary-wide studies of benthic macrofaunal habitat associations in Willapa Bay, Washington, USA. This research is part of an effort to develop empirical models of biota-habitat associations that can be used to help identify critical habitats, prioritize habitats for environmental protection, index habitat suitability (U.S. Fish and Wildlife Service, 1980; Kapustka, 2003), perform habitat equivalency and compensatory restoration analyses (Fonseca et al., 2002; Kirsch et al., 2005), and as habitat value criteria in ecological risk assessments (Obery and Landis, 2002; Ferraro and Cole, 2004; Landis et al., 2004)." (491) | 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 (Crop-based production); 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: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….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." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. Based on spatial land cover units originating from CORINE and further data sets, these regulating ecosystem services were quantified and mapped. Resulting maps show the ecosystems’ flood regulating service capacities in the case study area of the Malki Iskar river basin above the town of Etropole in the northern part of Bulgaria...The resulting map of flood regulation supply capacities shows that the Etropole municipality’s area has relatively high capacities for flood regulation. Areas of high and very high relevant capacities cover about 34% of the study area." AUTHOR'S DESCRIPTION: "The capacities of the identified spatial units were assessed on a relative scale ranging from 0 to 5 (after Burkhard et al., 2009). A 0-value indicates that there is no relevant capacity to supply flood regulating services and a 5-value indicates the highest relevant capacity for the supply of these services in the case study region. Values of 2, 3 and 4 represent respective intermediate supply capacities. Of course it depends on the observer’s estimation and knowledge which function–service relations in general are supposed to be relevant. But, this scale offers an alternative relative evaluation scheme, avoiding the presentation of monetary or normative value-transfer results. The 0–5 capacity values’ classifications for the different land cover types were based on the spatial analyses of different biogeophysical and land use data combined with hydrological modeling as described before…The supply capacities of the land cover classes and soil types in the study area were assigned to every unit in their databases. GIS map layers, containing information about the capacity to supply flood regulation for every polygon, were created. The map of supply capacities of flood regulating ecosystem services was elaborated by overlaying the GIS map layers of the land cover and the soils’ capacities." | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | 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. " | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Another method to quantify recreational opportunities is to use survey data of tourists and recreational visitors to the reefs to generate statistical models to quantify the link between reef condition and production of recreation-related ecosystem services. Wielgus et al. (2003) used interviews with SCUBA divers in Israel to derive coefficients for a choice model in which willingness to pay for higher quality dive sites was determined in part by a weighted combination of factors identified with dive quality: Relative value of dive site = 0.1227(Scoral+Sfish+Acoral+Afish)+0.0565V where Scoral, Sfish are coral and fish richness, Acoral, Afish are abundances of fish and coral per square meter, and V is water visibility (meters)." | 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. | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | [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: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "We used existing studies in a meta-analysis to estimate appropriate benefit transfer values of consumer surplus per beach visit for Barnstable. The studies we include in the model are for beaches across the United States, allowing the metaregression model to be more broadly applicable to other beaches and for values to be adjusted based on appropriate site attributes...To identify relevant studies, we selected 25 studies of beach use and swimming from the Recreation Use Values Database (RUVD), where consumer surplus values are presented as value per day in 2016 dollars...We added beach length and history of closures to contextualize the model for our application by proxying water quality and site quality." Equation 1, page 11, provides the meta-regression. | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | Abstract: "Differences in sampling and laboratory protocols, differences in techniques used to evaluate metrics, and differing scales of calibration and application prohibit the use of many existing multimetric indices (MMIs) in large-scale bioassessments. We describe an approach to developing MMIs of ecological condition that is applicable to a variety of biological assemblage types and to spatially extensive (regional, national) aquatic resource surveys. The process involves testing the performance characteristics of candidate metrics in several categories that correspond to key dimensions of biotic condition. The performance characteristics include: information content (range), reproducibility, calibration for natural gradients, responsiveness to stressor gradients, and independence from other metrics. The best-performing metric from each category is included in the final MMI. The consistency of the process enables development of separate MMIs in different regions that can be combined in a national assessment and that are more comparable across regions and taxonomic groups than a set of independently developed MMIs would be. Range: Generally eliminate metrics if their range is <4 or if > 1/3 of samples have values = 0. Very few macroinvertebrate metrics are eliminated by this test. It does eliminate a large number of potentially poor metrics for assemblages with fewer taxa (e.g., fish). Reproducibility: We quantify metric reproducibility with a variant of the signal:noise ratio (S/N). S/N is the ratio of the variance among all sites (signal) to the variance of repeated visits to the same site (noise). S/N values 1 indicate that visiting a single site twice yields as much metric variability as visiting 2 different sites. Natural gradient calibration: Focusing solely on reference-site data and to quantify the remaining correspondence between the metric value and the natural gradient. Responsiveness: The ability of a metric to distinguish least-disturbed (reference) from most-disturbed sites. We identify the metrics that have the highest responsiveness (t-scores) within each class and aggregated ecoregion. Redundancy: We often consider metrics as too strongly correlated when their Pearson correlation coefficients at least-disturbed sites are > |0.71| (R2 = 0.5). | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | ABSTRACT: "Greening of vacant urban land may affect health and safety. The authors conducted a decade-long difference-indifferences analysis of the impact of a vacant lot greening program in Philadelphia, Pennsylvania, on health and safety outcomes. ‘‘Before’’ and ‘‘after’’ outcome differences among treated vacant lots were compared with matched groups of control vacant lots that were eligible but did not receive treatment. Control lots from 2 eligibility pools were randomly selected and matched to treated lots at a 3:1 ratio by city section. Random-effects regression models were fitted, along with alternative models and robustness checks. Across 4 sections of Philadelphia, 4,436 vacant lots totaling over 7.8 million square feet (about 725,000 m^2) were greened from 1999 to 2008. Regression adjusted estimates showed that vacant lot greening was associated with consistent reductions in gun assaults across all 4 sections of the city (P < 0.001) and consistent reductions in vandalism in 1 section of the city (P < 0.001). Regression-adjusted estimates also showed that vacant lot greening was associated with residents’ reporting less stress and more exercise in select sections of the city (P < 0.01). Once greened, vacant lots may reduce certain crimes and promote some aspects of health. Limitations of the current study are discussed. Community-based trials are warranted to further test these findings." REVIEWER'S COMMENTS: Regression models were fitted separately for point-based, tract-based, and block group-based outcomes, and for the four sections of Philadelphia separately and combined. This entry presents just the point-based outcomes for the whole of Philadelphia. | [The toolkit provides a very helpful introduction to the evidence demonstrating the benefits of green infrastructure interventions. It offers a structured argument that speaks the language of regeneration and economic developments. The 11 economic benefits structure provides a relatively simple high level means of presenting and communicating the benefits of green infrastructure projects in economic contexts, although it also brings some risks of double-counting (see Limitations below). The toolkit provides a structured approach to value green infrastructure benefits in monetary, quantitative and qualitative terms, with equal weight being applied to each of these three ways to present existing evidence. It can add value to and inform the decision-making process, particularly when used at an early stage to get broad brush figures and weigh pros and cons.The toolkit relies on current state-of-the-art evidence and valuation techniques for green infrastructure benefits. However, the toolkit also highlights the need for considerable improvement and expansion of the evidence base to enable future iterations to provide improved valuations. The toolkit helps make green infrastructure benefits ‘visible’ to potential funders. The inclusion of environmental benefits in cost benefit analysis is currently very difficult, often requiring professional assistance. Such assistance is frequently beyond the means of many groups seeking project funding. The toolkit is aimed at filling this gap, providing a means of scoping out the indicative benefits of green infrastructure using tools and approaches accessible to many projects and groups. However, whilst the toolkit provides a means of undertaking a broad Value for Money assessment, it must but emphasised that this is only indicative and cannot replace more rigorous formal project appraisal techniques.] |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | Restoration of seagrass | Supports global and EU biodiversity policy | None identified | None reported | None identified | None identified | climate change | None identified | None | Restoration and management of diadromous fish runs in coastal New England | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | None identified | None identified | None reported | None identified | None |
Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | benthic estuarine | No additional description provided | No additional description provided | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | No additional description provided | No additional description provided | Not applicable | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | Rocky mountain conifer forests | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | Four separate beaches within the community of Barnstable | Prairie Pothole Region of Iowa | No additional description provided | Aquatic systems | Conservation Reserve Program lands left to go fallow | No additional description provided | N/A |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | No scenarios presented | Habitat loss or restoration in Tampa Bay Estuary | No scenarios presented | No scenarios presented | Land Use, EGS algorithm values, | No scenarios presented | No scenarios presented | fertilization | N/A | N/A | current and historical run size | No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | N/A | No scenarios presented | N/A |
EM ID
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method Only |
New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
EM Temporal Extent
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2008-2010 | Not reported | 2007-2009 | 2007-2008 | Not reported | 1996,1998 | 2000 | 1990-2030 | Not reported | 1982-2010 | 1992-2010 | 2001-2002 | Not applicable | 2006-2007, 2010 | 2006-2007, 2010 | 1961-1990 | Not applicable | 2004-2008 | 1960"s and early 2000's | July 1, 2011 to June 31, 2016 | 1987-2007 | 2007-2008 | Not applicable | 2008 | 1998-2008 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 6, 10, and 30 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Hour | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Physiographic or Ecological | Geopolitical | Other |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Physiographic or ecological | Point or points | Not applicable | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Physiographic or ecological | Geopolitical | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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Durham NC and vicinity | Central French Alps | Central French Alps | Central French Alps | Central French Alps | Willapa Bay | The EU-25 plus Switzerland and Norway | The EU-25 plus Switzerland and Norway | Municipality of Etropole | Tampa Bay Estuary | Shoreline of the European Union-27 | Agricultural landscape, Yolo County, Central Valley | Tampa Bay region | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Oak Park Research centre | Not applicable | National Park | Bride Lake and Linsley Pond | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | CREP (Conservation Reserve Enhancement Program | East Midlands | Not applicable | Piedmont Ecoregion | Philadelphia | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 1-10 ha | Not applicable | 1000-10,000 km^2. | 10-100 ha | 10-100 ha | 10,000-100,000 km^2 | 1000-10,000 km^2. | Not applicable | 100,000-1,000,000 km^2 | 100-1000 km^2 | Not applicable |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
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 distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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) | spatially distributed (in at least some cases) | Not applicable | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Point-based measures are continuous and boundary-free, assign each lot to its own unique neighborhood, and avoid aggregation effects while directly accounting for spillover and the variability of neighboring areas. |
spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | Not applicable | 1 km x 1 km | 1 km x 1 km | Distributed by land cover and soil type polygons | 1 ha | Irregular | 30 m x 30 m | 30m x 30m | 10 m x 10 m | 10 m x 10 m | Not applicable | Not reported | 30m2 | Not applicable | by beach site | multiple, individual, irregular sites | multiple unrelated locations | Not applicable | Not applicable | Point based | Not reported |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | No | No | No | No | Yes | No | No | No | Yes | No | Unclear | No | Yes | Yes | Yes | Not applicable | No | Yes | Yes | Unclear | Not applicable | Not applicable | Yes | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | Yes | Yes | No | Yes | No | No | No | No | No | No | No | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | Yes | No | Yes | No | Not applicable | Not applicable | No |
No ?Comment:Each outcome was fitted separatly, with R2 provided. See Variable Value comment for each Response. |
Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None | None | None | None | None | None | None | None |
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None |
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None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | Yes | No | No | No | Yes | No | No | No | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
No | Yes | Yes | Yes | Not applicable | No | No | No | No | Not applicable | Not applicable | No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | Not applicable | Not applicable | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Yes |
Yes ?Comment:p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables. |
No | Not applicable | Yes | Yes | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Yes | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
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None |
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None |
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None | None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
None | None | None | None | None |
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None | None | None |
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None | None |
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None | None | None | None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
Centroid Latitude
em.detail.ddLatHelp
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35.99 | 45.05 | 45.05 | 45.05 | 45.05 | 46.24 | 50.53 | 50.53 | 42.8 | 27.95 | 48.2 | 38.7 | 28.05 | 17.73 | 17.73 | 52.86 | Not applicable | 38.7 | 41.33 | 41.64 | 42.62 | 52.22 | Not applicable | 36.23 | 39.95 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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-78.96 | 6.4 | 6.4 | 6.4 | 6.4 | -124.06 | 7.6 | 7.6 | 24 | -82.47 | 16.35 | -121.8 | -82.52 | -64.77 | -64.77 | 6.54 | Not applicable | 105.89 | -72.24 | -70.29 | -93.84 | -0.91 | Not applicable | -81.9 | -75.17 | Not applicable |
Centroid Datum
em.detail.datumHelp
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None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Provided | Provided | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Created Greenspace | Atmosphere | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | 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 | Lakes and Ponds | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Forests | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Aquatic Environment (sub-classes not fully specified) | Grasslands | Created Greenspace | Not applicable |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows. | Drowned river valley estuary | Not applicable | Not applicable | Mountainous flood-prone region | Subtropical Estuary | Coastal zones | Cropland and surrounding landscape | All terestrial landcover and waterbodies | Coral reefs | Coral reefs | farm pasture | Restored wetlands | Montain forest | Coastal lakes and ponds and associated streams | Saltwater beach | Wetlands buffered by grassland within agroecosystems | restored landfills and grasslands | Multiple | grasslands | Urban and urban green space | Multiple |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Other or unclear (comment) ?Comment:Used in both large and small scale context depending upon survey |
Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community | Community | Community | Community | Species | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Individual or population, within a species | Individual or population, within a species | Guild or Assemblage | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available |
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None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-83 | EM-105 | EM-121 |
EM-122 ![]() |
EM-132 | EM-195 | EM-320 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-617 | EM-629 |
EM-661 ![]() |
EM-682 | EM-704 |
EM-709 ![]() |
EM-820 | EM-843 | EM-878 | EM-1004 |
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None |
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None | None |
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None |
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None |
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None |