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-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | Litter biomass production, Central French Alps | Cultural ES and plant traits, Central French Alps | Runoff potential of pesticides, Europe | Annual profit from agriculture, South Australia | Cultural ecosystem services, Bilbao, Spain | ARIES sediment regulation, Puget Sound Region, USA | InVEST crop pollination, NJ and PA, USA | MIMES: For Massachusetts Ocean (v1.0) | EnviroAtlas-Carbon sequestered by trees | Chinook salmon value (household), Yaquina Bay, OR | N removal by wetland restoration, Midwest, USA | SolVES, Pike & San Isabel NF, WY | P8 UCM | REQI (River Ecosystem Quality Index), Italy | Beach visitation, Barnstable, MA, USA | Wild bees over 26 yrs of restored prairie, IL, USA | WASP method | CommunityViz, Albany county, Wyoming |
EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | Litter biomass production, Central French Alps | Cultural ecosystem service estimated from plant functional traits, Central French Alps | Runoff potential of pesticides, Europe | Annual profit from agriculture, South Australia | Cultural ecosystem services, Bilbao, Spain | ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA | InVEST crop pollination, New Jersey and Pennsylvania, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | P8 Urban Catchment model method | REQI (River Ecosystem Quality Index), Marecchia River, Italy | Beach visitation, Barnstable, Massachusetts, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Water Quality Analysis Simulation Program Model method | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming. |
EM Source or Collection
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i-Tree | USDA Forest Service | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | None |
None ?Comment:EU Mapping Studies |
ARIES | InVEST | US EPA | US EPA | EnviroAtlas | i-Tree | US EPA | None | None | None | None | US EPA | None | None | None |
EM Source Document ID
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195 | 260 | 260 | 254 | 243 | 191 | 302 | 279 | 316 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
324 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
369 |
377 ?Comment:Published to the web. Previously versions prepared for EPA. |
378 | 386 | 401 | 472 |
479 ?Comment:Published as a report by the University of Wyoming, but no record of peer review. |
Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | 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. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | 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 | Altman, I., R.Boumans, J. Roman, L. Kaufman | US EPA Office of Research and Development - National Exposure Research Laboratory | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Walker, W. Jr., and J.D. Walker | Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Environmental Protection Agency | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. |
Document Year
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2013 | 2011 | 2011 | 2007 | 2011 | 2013 | 2014 | 2009 | 2012 | 2013 | 2012 | 2006 | 2014 | 2015 | 2014 | 2018 | 2017 | 2024 | 2003 |
Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | 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 | Carbon payments and low-cost conservation | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Modelling pollination services across agricultural landscapes | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | EnviroAtlas - Featured Community | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | P8 Urban Catchment Model Version 3.5 | Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy) | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Water Quality Assessment Simulation Program | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming |
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 | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
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 report | Published on US EPA EnviroAtlas website | Published journal manuscript | Published report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published report |
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
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.afordablefutures.com/orientation-to-what-we-do | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | http://www.wwwalker.net/p8/v35/webhelp/splash.htm | Not applicable | Not applicable | Not applicable | https://www.epa.gov/hydrowq/wasp8-download | https://communityviz.com/ | |
Contact Name
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David J. Nowak | Sandra Lavorel | Sandra Lavorel | Carola Alexandra Schriever | Neville D. Crossman | Izaskun Casado-Arzuaga | Ken Bagstad | Eric Lonsdorf | Irit Altman | EnviroAtlas Team | Stephen Jordan | William G. Crumpton | Benson Sherrouse | William Walker Jr., PhD | Elisa Morri | Kate K, Mulvaney | Sean R. Griffin | Environmental Protection Agency | Scott Lieske |
Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | 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 | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | Geosciences and Environmental Change Science Center, US Geological Survey | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Boston University, Portland, Maine | Not reported | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Concord, Massachusetts | Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy | Not reported | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | 1200 Pennsylvania Avenue, NW Washington, DC 20460 | Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 |
Contact Email
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dnowak@fs.fed.us | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | carola.schriever@ufz.de | neville.crossman@csiro.au | izaskun.casado@ehu.es | kjbagstad@usgs.gov | ericlonsdorf@lpzoo.org | iritaltman@bu.edu | enviroatlas@epa.gov | jordan.steve@epa.gov | crumpton@iastate.edu | bcsherrouse@usgs.gov | bill@wwwalker.net | elisa.morri@uniurb.it | Mulvaney.Kate@EPA.gov | srgriffin108@gmail.com | Google email group | lieske@uwyo.edu |
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
Summary Description
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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)." | 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." AUTHOR'S DESCRIPTION: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions." | 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." | ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns from agriculture and from carbon plantings." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | 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 mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape." | 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: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. " | ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed." | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | Web description: " The Water Quality Analysis Simulation Program (WASP) is an enhancement of the original WASP (Di Toro et al., 1983; Connolly and Winfield, 1984; Ambrose, R.B. et al., 1988). This model helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions. WASP is a dynamic compartment-modeling program for aquatic systems, including both the water column and the underlying benthos. WASP allows the user to investigate 1, 2, and 3 dimensional systems, and a variety of pollutant types. The state variables for the given modules are given in the table below. The time varying processes of advection, dispersion, point and diffuse mass loading and boundary exchange are represented in the model. WASP also can be linked with hydrodynamic and sediment transport models that can provide flows, depths velocities, temperature, salinity and sediment fluxes. This release of WASP contains the inclusion of the sediment diagenesis model linked to the Advanced Eutrophication sub model, which predicted sediment oxygen demand and nutrient fluxes from the underlying sediments " | The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process. |
Specific Policy or Decision Context Cited
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Not reported | None identified | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | None identified | None identified | None identified | None identified | None identified | None identified | None | None identified | None identified | To assess the number of people who would be impacted by beach closures. | None identified | Not applicable | None provided |
Biophysical Context
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Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Not applicable | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | Northern Spain; Bizkaia region | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Yaquina Bay estuary | No additional description provided | Rocky mountain conifer forests | Urban setting | No additional description provided | Four separate beaches within the community of Barnstable | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | segments of streams modeled | Groundwater recharge area, City of Laramie |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | More conservative, average and less conservative nitrate loss rate | N/A | N/A | No scenarios presented | No scenarios presented | No scenarios presented | n/a | Current conditions |
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
Method Only, Application of Method or Model Run
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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) | Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application (multiple runs exist) |
New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | 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 | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
Document ID for related EM
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None | Doc-260 | None | Doc-255 | Doc-256 | Doc-257 | Doc-244 | None | Doc-303 | Doc-305 | Doc-279 | None | Doc-345 | Doc-324 | None | Doc-369 | None | None | Doc-386 | Doc-387 | None | None | Doc-473 |
EM ID for related EM
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None | EM-65 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-82 | EM-83 | None | None | None | None | EM-340 | EM-338 | None | None | EM-603 | EM-397 | None | EM-626 | EM-628 | None | None | EM-682 | EM-685 | EM-683 | EM-686 | None | None | EM-1003 | EM-1014 | EM-1005 | EM-1015 | EM-1016 |
EM Modeling Approach
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
EM Temporal Extent
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1989-2010 | Not reported | Not reported | 2000 | 2002-2008 | 2000 - 2007 | 1971-2005 | 2000-2002 | Not applicable | 2010-2013 | 2003-2008 | 1973-1999 | 2004-2008 | Not applicable |
1996-2003 ?Comment:All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map. |
2011 - 2016 | 1988-2014 | Not applicable | 2000 |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | past time | Not applicable | future time | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable |
discrete ?Comment:Time frame is modeler dependent |
Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Day | Not applicable | Hour | Not applicable | Day | Not applicable | Day | Not applicable |
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Other | Physiographic or ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
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United States | Central French Alps | Central French Alps | EU-15 | Agricultural districts of the state of South Australia | Bilbao Metropolitan Greenbelt | Puget Sound Region | Central New Jersey and east-central Pennsylvania | Massachusetts Ocean | Durham NC and vicinity | Pacific Northwest | Upper Mississippi River and Ohio River basins | National Park | Not applicable | Marecchia river catchment | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Nachusa Grasslands | Not applicable | Laramie City's aquifer protection area |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | Not applicable | 100-1000 km^2 | 10-100 ha | 10-100 km^2 | Not applicable | 10-100 km^2 |
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
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) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Census block groups |
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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 | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | 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) | length, for linear feature (e.g., stream mile) | Not applicable |
Spatial Grain Size
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1 m^2 | 20 m x 20 m | 20 m x 20 m | 10 km x 10 km | 1 ha | 2 m x 2 m | 200m x 200m | 30 m x 30 m | 1 km x1 km | irregular | Not applicable | 1 km2 | 30m2 | Not applicable | 500 m x 1000 m | by beach site | Area varies by site | stream segment | Not applicable |
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | 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-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
Model Calibration Reported?
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No | No | No | No | No | No | Yes | Unclear | No | No | No | No | No | Yes | Not applicable | Yes | No | Unclear | Unclear |
Model Goodness of Fit Reported?
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No | Yes | No | No | No | No | No | No | No | No | No | No | Yes | Not applicable | Not applicable | No | No | Unclear | No |
Goodness of Fit (metric| value | unit)
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None |
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None | None | None | None | None | None | None | None | None | None |
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None | None | None | None | None | None |
Model Operational Validation Reported?
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No | Yes | No | No | No | Yes | No |
Yes ?Comment:Aggregate native bee abundance on watermelon flowers was measured at 23 sites in 2005. Species richness was measured using the specimens collected from watermelon flowers at the end of the sampling period. |
No | No | Yes |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | Not applicable |
Yes ?Comment:R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF. |
No | No | Unclear | Unclear |
Model Uncertainty Analysis Reported?
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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 | Yes | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | No | Unclear | Unclear |
Model Sensitivity Analysis Reported?
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No | No | No | Yes | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | Yes | No | Unclear | Unclear |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 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-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
None | None | None | None | None | None | None | None |
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None |
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None | None | None | None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
Centroid Latitude
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40.16 | 45.05 | 45.05 | 50.01 | -34.9 | 43.25 | 48 | 40.2 | 41.72 | 35.99 | 44.62 | 40.6 | 38.7 | Not applicable | 43.89 | 41.64 | 41.89 | Not applicable | 41.31 |
Centroid Longitude
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-99.79 | 6.4 | 6.4 | 4.67 | 138.7 | -2.92 | -123 | -74.8 | -69.87 | -78.96 | -124.02 | -88.4 | 105.89 | Not applicable | 12.3 | -70.29 | -89.34 | Not applicable | -105.46 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Provided | Not applicable | Estimated |
EM ID
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
EM Environmental Sub-Class
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Forests | Created Greenspace | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Agroecosystems | Forests | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Urban forests | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Arable lands in near-stream environments | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | none | Terrestrial environment surrounding a large estuary | Cropland and surrounding landscape | None identified | Urban and vicinity | Yaquina Bay estuary and ocean | Agroecosystems and associated drainage and wetlands | Montain forest | Urban catchments | Riparian zone along major river | Saltwater beach | Restored prairie, prairie remnants, and cropland | Stream segment | watershed |
EM Ecological Scale
em.detail.ecoScaleHelp
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Zone within an ecosystem | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
EM Organismal Scale
em.detail.orgScaleHelp
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Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Community | Community | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Species | Species | Not applicable | Other (multiple scales) | Not applicable | Not applicable | Not applicable |
Species ?Comment:Bird species for faunistic index of conservation. |
Not applicable | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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None Available | None Available |
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-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
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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-24 | EM-66 | EM-81 | EM-92 | EM-126 | EM-193 | EM-327 | EM-339 | EM-376 | EM-493 | EM-604 | EM-627 | EM-629 | EM-656 | EM-657 | EM-684 |
EM-788 ![]() |
EM-1002 | EM-1013 |
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