EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
EM Short Name
em.detail.shortNameHelp
?
|
i-Tree Eco: Carbon storage & sequestration, USA | EnviroAtlas-Air pollutant removal | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Community flowering date, Central French Alps | Benthic habitat associations, Willapa Bay, OR, USA | Landscape importance for recreation, Europe | Flood regulation capacity, Etropole, Bulgaria | C Sequestration and De-N, Tampa Bay, FL, USA | Coastal protection, Europe | ARIES flood regulation, Puget Sound Region, USA | 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 | SolVES, Pike & San Isabel NF, WY | WTP for a beach day, Massachusetts, USA | Northern Pintail recruits, CREP wetlands, IA, USA | Pollinators on landfill sites, United Kingdom | Mourning dove abundance, Piedmont region, USA | Invertebrate community index, Alabama | ARIES: Crop pollination in Santa Fe, NM, USA | Health, safety and greening urban space, PA, USA | CEASAR and TRACER models, EU |
EM Full Name
em.detail.fullNameHelp
?
|
i-Tree Eco carbon storage and sequestration (trees), USA | 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 | Benthic macrofaunal habitat associations, Willapa Bay, OR, USA | Landscape importance for recreation, Europe | Flood regulation capacity of landscapes, Municipality of Etropole, Bulgaria | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Coastal protection, Europe | ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | 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 | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | 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 | Mourning dove abundance, Piedmont ecoregion, USA | Invertebrate community index, Choctawhatchee-Pea Rivers watershed, Alabama | Artificial intelligence for Ecosystem Services (ARIES); Crop pollination, Santa Fe, New Mexico, USA | Health, safety and greening urban vacant space, Pennsylvania, USA | Modelling remediation scenarios in historical mining catchments |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
i-Tree | USDA Forest Service |
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 | US EPA | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | ARIES | InVEST | US EPA | US EPA | US EPA | None | None | US EPA | None | None | None | None | ARIES | None | None |
EM Source Document ID
|
195 | 223 | 260 | 260 | 260 | 39 | 228 | 248 | 186 | 296 | 302 | 279 | 321 | 335 | 335 | 358 | 369 | 386 |
372 ?Comment:Document 373 is a secondary source for this EM. |
389 | 405 | 409 | 411 | 419 | 467 |
Document Author
em.detail.documentAuthorHelp
?
|
Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | 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. | Ferraro, S. P. and Cole, F. A. | 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. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | 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. | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | 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 | Riffel, S., Scognamillo, D., and L. W. Burger | Bennett, H.H., Mullen, M.W., Stewart, P.M., Sawyer, J.A., and E. C. Webber | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Branas, C. C., R. A. Cheney, J. M. MacDonald, V. W. Tam, T. D. Jackson, and T. R. Ten Havey | Gamarra, J. G., Brewer, P. A., Macklin, M. G., & Martin, K. |
Document Year
em.detail.documentYearHelp
?
|
2013 | 2013 | 2011 | 2011 | 2011 | 2007 | 2012 | 2012 | 2013 | 2013 | 2014 | 2009 | 2015 | 2014 | 2014 | 2010 | 2014 | 2018 | 2010 | 2013 | 2008 | 2004 | 2018 | 2011 | 2014 |
Document Title
em.detail.sourceIdHelp
?
|
Carbon storage and sequestration by trees in urban and community areas of the United States | 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 | Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA | 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 | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | 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 | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | 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 | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Development of an invertebrate community index for an Alabama coastal plain watershed | Towards globally customizable ecosystem service models | A difference-in-differences analysis of health, safety, and greening vacant urban space | Modelling remediation scenarios in historical mining catchments |
Document Status
em.detail.statusCategoryHelp
?
|
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
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | 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 EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://aries.integratedmodelling.org/ | 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 | https://github.com/integratedmodelling/im.aries.global | Not applicable | Not applicable | |
Contact Name
em.detail.contactNameHelp
?
|
David J. Nowak | EnviroAtlas Team | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Steve Ferraro | Marion Potschin | Stoyan Nedkov | M. Russell | Camino Liquete | Ken Bagstad | Eric Lonsdorf | Marc J. Russell, Ph.D. | Susan H. Yee | Susan H. Yee | M. Abdalla | Benson Sherrouse | Kate K, Mulvaney | David Otis | Sam Tarrant | Sam Riffell | E. Cliff Webber | Javier Martinez | Charles C. Branas | Javier G. P. Gamarra |
Contact Address
|
USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | 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 | 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 | 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 | Geosciences and Environmental Change Science Center, US Geological Survey | 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 | USGS, 5522 Research Park Dr., Baltimore, MD 21228, 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. | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Troy State University, 4004 Clairmont Avenue South, Birmingham, Alabama 35222 progress. | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 936, 423 Guardian Drive, Philadelphia, PA 19104-6021 | Institute of Biological, Environmental and Rural Sciences, Aberystwyth, SY23 3DB, UK |
Contact Email
|
dnowak@fs.fed.us | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ferraro.steven@epa.gov | marion.potschin@nottingham.ac.uk | snedkov@abv.bg | Russell.Marc@epamail.epa.gov | camino.liquete@gmail.com | kjbagstad@usgs.gov | ericlonsdorf@lpzoo.org | russell.marc@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | abdallm@tcd.ie | bcsherrouse@usgs.gov | Mulvaney.Kate@EPA.gov | dotis@iastate.edu | sam.tarrant@rspb.org.uk | sriffell@cfr.msstate.edu | hbennett1978@hotmail.com | javier.martinez@bc3research.org | cbranas@upenn.edu | jgg@aber.ac.uk |
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)." | 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." | 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: "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." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures." | 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. | [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: "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:"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: "Macroinvertebrates were collected from 49 randomly selected sites from first through sixth-order streams in the Choctawhatchee-Pea Rivers watershed and were identified to genus level. Thirty-eight candidate metrics were examined, and an invertebrate community index (ICI) was calibrated by eliminating metrics that failed to separate impaired from unimpaired streams. Each site was scored with those metrics, and narrative scores were assigned based on ICI scores. Least impacted sites scored significantly lower than sites impacted by row crop agriculture, cattle, and urban land uses. Conditions in the watershed suggest that the entire area has experienced degradation through past and current land use practices. An initial validation of the index was performed and is described. Additional evaluations of the index are in progress." | [Abstract:Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.] | ABSTRACT: "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. | Local remediation measures, particularly those undertaken in historical mining areas, can often be ineffective or even deleterious because erosion and sedimentation processes operate at spatial scales beyond those typically used in point-source remediation. Based on realistic simulations of a hybrid landscape evolution model combined with stochastic rainfall generation, we demonstrate that similar remediation strategies may result in differing effects across three contrasting European catchments depending on their topographic and hydrologic regimes. Based on these results, we propose a conceptual model of catchment-scale remediation effectiveness based on three basic catchment characteristics: the degree of contaminant source coupling, the ratio of contaminated to non-contaminated sediment delivery, and the frequency of sediment transport events. |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
Not reported | 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 identified | None reported | None identified | None identified | climate change | None | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | None identified | None reported | None reported | None identified | None identified | None identified |
Biophysical Context
|
Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | 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 | benthic estuarine | 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 | 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 | Rocky mountain conifer forests | Four separate beaches within the community of Barnstable | Prairie Pothole Region of Iowa | No additional description provided | Conservation Reserve Program lands left to go fallow | 1st through 6th order streams on low elevation coastal plains | Fire watersheds near Albuquerque, NM. | No additional description provided | Rver system catchments associated with mining sites distributed across Europe |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Habitat loss or restoration in Tampa Bay Estuary | No scenarios presented | No scenarios presented | No scenarios presented | Land Use, EGS algorithm values, | No scenarios presented | No scenarios presented | fertilization | N/A | No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A | N/A | No scenarios presented | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | 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 | Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
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 | 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 | Application of existing model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1989-2010 | 2008-2010 | Not reported | 2007-2009 | 2007-2008 | 1996,1998 | 2000 | Not reported | 1982-2010 | 1992-2010 | 1971-2006 | 2001-2002 | Not applicable | 2006-2007, 2010 | 2006-2007, 2010 | 1961-1990 | 2004-2008 | July 1, 2011 to June 31, 2016 | 1987-2007 | 2007-2008 | 2008 | 2002 | 2010 | 1998-2008 | 1800-2100 |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | 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-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
future time | future time | 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 | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | discrete | 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 | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | continuous |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
1 | 1 | 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 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Year | Hour | 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 | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or ecological | 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 | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Watershed/Catchment/HUC |
Spatial Extent Name
em.detail.extentNameHelp
?
|
United States | Durham NC and vicinity | Central French Alps | Central French Alps | Central French Alps | Willapa Bay | The EU-25 plus Switzerland and Norway | Municipality of Etropole | Tampa Bay Estuary | Shoreline of the European Union-27 | Puget Sound Region | Agricultural landscape, Yolo County, Central Valley | Tampa Bay region | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Oak Park Research centre | National Park | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | CREP (Conservation Reserve Enhancement Program | East Midlands | Piedmont Ecoregion | Choctawhatchee-Pea rivers watershed | Rwanda and Burndi | Philadelphia | Ystwyth, Ampoi, and Naracauli |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
>1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 1-10 ha | 1000-10,000 km^2. | 10-100 ha | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 100-1000 km^2 | 100-1000 km^2 |
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) |
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 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 distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?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
?
|
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 | Not applicable | 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 | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | 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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
1 m^2 | irregular | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | Not applicable | 1 km x 1 km | Distributed by land cover and soil type polygons | 1 ha | Irregular | 200m x 200m | 30 m x 30 m | 30m x 30m | 10 m x 10 m | 10 m x 10 m | Not applicable | 30m2 | by beach site | multiple, individual, irregular sites | multiple unrelated locations | Not applicable | Not applicable | 1km | Point based | Not reported |
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Unclear | No | No | No | Yes | No | No | Yes | No | No | Unclear | No | Yes | Yes | Yes | No | Yes | Unclear | Not applicable | Yes |
Yes ?Comment:Culled metrics that did not distinguish between impaired and unimpaired sites. |
Unclear | No | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | Yes | Yes | Yes | 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 |
Yes | Yes | No | Not applicable | No | No | No |
No ?Comment:Each outcome was fitted separatly, with R2 provided. See Variable Value comment for each Response. |
No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None |
|
|
|
|
None | None | None | None | None | None | None | None | None |
|
|
|
None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | Yes | Yes | No | No | Yes | No | No | No | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
No | Yes | Yes | Yes | No | No | No | Not applicable | No | Yes | No | No | Yes |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No |
Yes ?Comment:p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables. |
No | Not applicable | Yes | Yes | No | No | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
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 | Not applicable | Not applicable | Unclear | Yes | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
|
|
|
|
None |
|
|
None |
|
|
|
|
None | None |
|
|
|
|
|
|
|
|
|
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
None | None | None | None | None |
|
None | None |
|
|
None | None | None |
|
|
None | None |
|
None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
Centroid Latitude
em.detail.ddLatHelp
?
|
40.16 | 35.99 | 45.05 | 45.05 | 45.05 | 46.24 | 50.53 | 42.8 | 27.95 | 48.2 | 48 | 38.7 | 28.05 | 17.73 | 17.73 | 52.86 | 38.7 | 41.64 | 42.62 | 52.22 | 36.23 | 31.39 | -2.59 | 39.95 |
52.5 ?Comment:There are 3 locations provided in this study with latitudes of 52.5, 46, and 40 as well as longitudes of -4, 10, and 25, respectively. |
Centroid Longitude
em.detail.ddLongHelp
?
|
-99.79 | -78.96 | 6.4 | 6.4 | 6.4 | -124.06 | 7.6 | 24 | -82.47 | 16.35 | -123 | -121.8 | -82.52 | -64.77 | -64.77 | 6.54 | 105.89 | -70.29 | -93.84 | -0.91 | -81.9 | -85.71 | 29.97 | -75.17 | -4 |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Provided | Provided | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated |
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Forests | Created Greenspace | Created Greenspace | Atmosphere | 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) | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | 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 | Forests | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Grasslands | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Urban forests | Urban and vicinity | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Drowned river valley estuary | Not applicable | Mountainous flood-prone region | Subtropical Estuary | Coastal zones | Terrestrial environment surrounding a large estuary | Cropland and surrounding landscape | All terestrial landcover and waterbodies | Coral reefs | Coral reefs | farm pasture | Montain forest | Saltwater beach | Wetlands buffered by grassland within agroecosystems | restored landfills and grasslands | grasslands | 1st - 6th order streams | varied | Urban and urban green space | Watershed catchment |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Not applicable | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Not applicable | 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 | Individual or population, within a species | Species |
Other (Comment) ?Comment:To species but focused on functional group classes |
Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
None Available | None Available | None Available | None Available | None Available |
|
None Available | None Available | None Available | None Available | None Available |
|
None Available | None Available | None Available | None Available | None Available | None Available |
|
|
|
None Available |
|
None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
|
|
None |
|
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-24 |
EM-59 ![]() |
EM-66 | EM-68 | EM-71 | EM-105 | EM-121 | EM-132 | EM-195 | EM-320 | EM-326 |
EM-338 ![]() |
EM-392 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-843 | EM-850 | EM-856 | EM-878 | EM-997 |
|
|
None |
|
None |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
None |
|
None | None |
|
|