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-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
EM Short Name
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Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Community flowering date, Central French Alps | Runoff potential of pesticides, Europe | InVEST nutrient retention, Hood Canal, WA, USA | Land-use change and crop-based production, Europe | Flood regulation supply-demand, Etropole, Bulgaria | InVEST - Water provision, Francoli River, Spain | C Sequestration and De-N, Tampa Bay, FL, USA | ARIES open Space, Puget Sound Region, USA | Coastal protection, Europe | EPA H2O, Tampa Bay Region, FL,USA | SIRHI, St. Croix, USVI | 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 | Health, safety and greening urban space, PA, USA |
EM Full Name
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Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Community weighted mean flowering date, Central French Alps | Runoff potential of pesticides, Europe | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) nutrient retention, Hood Canal, WA, USA | Land-use change effects on crop-based production, Europe | Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA | Coastal protection, Europe | EPA H2O, Tampa Bay Region, FL, USA | SIRHI (SImplified Reef Health Index), St. Croix, USVI | 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 | Health, safety and greening urban vacant space, Pennsylvania, USA |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | InVEST | EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST | US EPA | ARIES | EU Biodiversity Action 5 | US EPA | US EPA | US EPA | US EPA | None | None | US EPA | None | None | None |
EM Source Document ID
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260 | 260 | 260 | 254 | 205 | 228 | 248 | 280 | 186 | 302 | 296 | 321 | 335 | 335 | 335 | 358 | 369 | 386 |
372 ?Comment:Document 373 is a secondary source for this EM. |
389 | 419 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Schriever, C. A., and Liess, M. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Haines-Young, R., Potschin, M. and Kienast, F. | Nedkov, S., Burkhard, B. | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Russell, M. and Greening, H. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | 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 | 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 | Branas, C. C., R. A. Cheney, J. M. MacDonald, V. W. Tam, T. D. Jackson, and T. R. Ten Havey |
Document Year
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2011 | 2011 | 2011 | 2007 | 2013 | 2012 | 2012 | 2013 | 2013 | 2014 | 2013 | 2015 | 2014 | 2014 | 2014 | 2010 | 2014 | 2018 | 2010 | 2013 | 2011 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecological risk of agricultural pesticide runoff | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | 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 | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | Estimating benefits in a recovering estuary: Tampa Bay, Florida | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Assessment of coastal protection as an ecosystem service in Europe | 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 | 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 | A difference-in-differences analysis of health, safety, and greening vacant urban space |
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 |
Comments on Status
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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 journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
EM ID
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://aries.integratedmodelling.org/ | Not applicable | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | Not applicable | Not applicable | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Carola Alexandra Schriever | J.E. Toft | Marion Potschin | Stoyan Nedkov | Montse Marquès | M. Russell | Ken Bagstad | Camino Liquete | Marc J. Russell, Ph.D. | Susan H. Yee | Susan H. Yee | Susan H. Yee | M. Abdalla | Benson Sherrouse | Kate K, Mulvaney | David Otis | Sam Tarrant | Charles C. Branas |
Contact Address
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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 | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | 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 | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | Geosciences and Environmental Change Science Center, US Geological Survey | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | 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 | 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 Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 936, 423 Guardian Drive, Philadelphia, PA 19104-6021 |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | carola.schriever@ufz.de | jetoft@stanford.edu | marion.potschin@nottingham.ac.uk | snedkov@abv.bg | montserrat.marques@fundacio.urv.cat | Russell.Marc@epamail.epa.gov | kjbagstad@usgs.gov | camino.liquete@gmail.com | russell.marc@epa.gov | yee.susan@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 | cbranas@upenn.edu |
EM ID
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Litter biomass production for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on litter mass. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | InVEST Nutrient Retention Model 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. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub-watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparameterized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)" (2) "We used the InVEST Nutrient Retention model to quantify the total nitrogen load for each subwatershed. Inputs to the Nutrient Retention model include water yield, land use and land cover, and nutrient loading and filtration rates (Table 1; Conte et al., 2011; Tallis et al., 2011). The nutrient model quantifies natural and anthropogenic sources of total nitrogen within each subwatershed, allowing managers to identify subwatersheds potentially at risk of contributing excessive nitrogen loads given the predicted development and climate future." ( P. 4) | 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. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa." | 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. AUTHOR'S DESCRIPTION: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | 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: "...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: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape." | 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." | 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...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati | 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: "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. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Land use change | None identified | None identified | None identified | Restoration of seagrass | None identified | Supports global and EU biodiversity policy | None reported | None identified | None identified | None identified | climate change | None | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | None identified | None identified |
Biophysical Context
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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 | Not applicable | 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. | Mediteranean coastal mountains | 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 | 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 | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Future land use and land cover; climate change | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | No scenarios presented | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | 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 | No scenarios presented | fertilization | N/A | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
EM ID
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | 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 | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
EM Temporal Extent
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Not reported | 2007-2009 | 2007-2008 | 2000 | 2005-7; 2035-45 | 1990-2030 | Not reported | 1971-2100 | 1982-2010 | 2000-2011 | 1992-2010 | Not applicable | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 1961-1990 | 2004-2008 | July 1, 2011 to June 31, 2016 | 1987-2007 | 2007-2008 | 1998-2008 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | 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 |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | 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 |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | 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 |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | Not applicable | 6, 10, and 30 | 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 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Day | Not applicable | Year | 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 |
EM ID
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or ecological | Geopolitical |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | 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) | Geopolitical |
Spatial Extent Name
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Central French Alps | Central French Alps | Central French Alps | EU-15 | Hood Canal | The EU-25 plus Switzerland and Norway | Municipality of Etropole | Francoli River | Tampa Bay Estuary | Puget Sound Region | Shoreline of the European Union-27 | Tampa Bay region | Coastal zone surrounding St. Croix | 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 | Philadelphia |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 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. | 100-1000 km^2 |
EM ID
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Distributed by land cover and soil type polygons |
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 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. |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | 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 | 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) | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 10 km x 10 km | 30 m x 30 m | 1 km x 1 km | Distributed by irregular land cover and soil type polygons | 30m x 30m | 1 ha | 200m x 200m | Irregular | 30m x 30m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | Not applicable | 30m2 | by beach site | multiple, individual, irregular sites | multiple unrelated locations | Point based |
EM ID
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Other or unclear (comment) | Logic- or rule-based | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
Model Calibration Reported?
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No | No | No | No | Yes | No | No | No | Yes | No | No | No | Yes | Yes | Yes | Yes | No | Yes | Unclear | Not applicable | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | Yes | Yes | No | No | No | 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 ?Comment:Each outcome was fitted separatly, with R2 provided. See Variable Value comment for each Response. |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None | None |
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None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | Yes | No | No | Yes | No | No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
No | No | No | No | Yes | Yes | Yes | Yes | No | No | No | Not applicable | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | Yes | Yes | 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 | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | No | No | 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 |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
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None |
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None | None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
None | None | None | None |
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None | None | None |
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None |
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None | None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 45.05 | 50.01 | 47.8 | 50.53 | 42.8 | 41.26 | 27.95 | 48 | 48.2 | 28.05 | 17.73 | 17.73 | 17.73 | 52.86 | 38.7 | 41.64 | 42.62 | 52.22 | 39.95 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | 6.4 | 4.67 | -122.7 | 7.6 | 24 | 1.18 | -82.47 | -123 | 16.35 | -82.52 | -64.77 | -64.77 | -64.77 | 6.54 | 105.89 | -70.29 | -93.84 | -0.91 | -75.17 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | 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 | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Rivers and Streams | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Forests | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Created Greenspace |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Arable lands in near-stream environments | glacier-carved saltwater fjord | Not applicable | Mountainous flood-prone region | Coastal mountains | Subtropical Estuary | Terrestrial environment surrounding a large estuary | Coastal zones | All terestrial landcover and waterbodies | Coral reefs | Coral reefs | Coral reefs | farm pasture | Montain forest | Saltwater beach | Wetlands buffered by grassland within agroecosystems | restored landfills and grasslands | Urban and urban green space |
EM Ecological Scale
em.detail.ecoScaleHelp
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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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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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-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
None |
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None | None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-66 | EM-68 | EM-71 | EM-92 |
EM-112 ![]() |
EM-122 ![]() |
EM-133 |
EM-148 ![]() |
EM-195 | EM-315 | EM-320 | EM-392 | EM-418 | EM-450 | EM-455 | EM-598 | EM-629 | EM-682 | EM-704 |
EM-709 ![]() |
EM-878 |
None |
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None | None | None |
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None |
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None |
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