EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
EM Short Name
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EnviroAtlas-Air pollutant removal | Divergence in flowering date, Central French Alps | Cultural ES and plant traits, Central French Alps | Fish species habitat value, Tampa Bay, FL, USA | Landscape importance for wildlife products, Europe | Land-use change and recreation, Europe | Flood regulation capacity, Etropole, Bulgaria | Evoland v3.5 (unbounded growth), Eugene, OR, USA | InVEST crop pollination, NJ and PA, USA | InVEST (v1.004) Carbon, Indonesia | Nitrogen fixation rates, Guánica Bay, Puerto Rico | Land capability classification | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | ESII Tool method | Wild bees over 26 yrs of restored prairie, IL, USA | Red-winged blackbird abun, Piedmont region, USA | Air pollution removal by green roofs, Chicago, USA | IPaC, USFWS, USA | EPA Stormwater Manamgement Model | Salmonid toxicity to heavy metals, USA | CMAQ chemical transport model, UK |
EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Functional divergence in flowering date, Central French Alps | Cultural ecosystem service estimated from plant functional traits, Central French Alps | Fish species habitat value, Tampa Bay, FL, USA | Landscape importance for wildlife products, Europe | Land-use change effects on recreation, Europe | Flood regulation capacity of landscapes, Municipality of Etropole, Bulgaria | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | InVEST crop pollination, New Jersey and Pennsylvania, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004) carbon storage and sequestration, Sumatra, Indonesia | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | Land capability classification | DeNitrification-DeComposition simulation of N2O flux Ireland | ESII (Ecosystem Services Identification & Inventory) Tool method | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Red-winged blackbird abundance, Piedmont ecoregion, USA | Air pollution removal by green roofs, Chigago, USA | Information for Planning and Conservation tool, USFWS, U.S. | Storm Water Management Model User's Manual Version 5.2 | Chinook salmon and steelhead toxicity to heavy metals, USA | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | Envision | InVEST | InVEST | US EPA | None | None | None | None | None | None | None | US EPA | US EPA | None |
EM Source Document ID
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223 | 260 | 260 | 187 | 228 | 228 | 248 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
279 | 309 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
340 | 358 |
391 ?Comment:Website for online user support |
401 | 405 |
438 ?Comment:Document 439 is an additional source for this EM. |
451 ?Comment:Assume peer reviewed at least internally by USFWS |
452 | 462 | 483 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Haines-Young, R., Potschin, M. and Kienast, F. | Haines-Young, R., Potschin, M. and Kienast, F. | Nedkov, S., Burkhard, B. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | United States Department of Agriculture - Natural Resources Conservation Service | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | EcoMetrix Solutions Group (ESG) | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Riffel, S., Scognamillo, D., and L. W. Burger | Yang, J., Q. Yu and P. Gong | U.S. Fish and Wildlife Service | Rossman, L. A., M., Simon | Chapman, G. | Chemel, C., Fisher, B.E.A., Kong, X., Francis, X.V., Sokhi, R.S., Good, N., Collins, W.J. and Folberth, G.A. |
Document Year
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2013 | 2011 | 2011 | 2016 | 2012 | 2012 | 2012 | 2008 | 2009 | 2014 | 2017 | 2013 | 2010 | 2016 | 2017 | 2008 | 2008 | None | 2022 | 1978 | 2014 |
Document Title
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EnviroAtlas - Featured Community | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Modelling pollination services across agricultural landscapes | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | National Soil Survey Handbook - Part 622 - Interpretative Groups | 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 | ESII Tool | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Quantifying air pollution removal by green roofs in Chicago | Information for Planning and Consultation (IPaC | Storm Water Management Model User's Manual Version 5.2 | Toxicities of Cadmium, Copper, and Zinc to Four Juvenile Toxicities of Cadmium, Copper, and Zinc to Four Juvenile Stages of Chinook Salmon and Steelhead | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
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 | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published EPA report | Published journal manuscript | Published journal manuscript |
EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | http://www.naturalcapitalproject.org/models/crop_pollination.html | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | http://www.dndc.sr.unh.edu | https://www.esiitool.com/ | Not applicable | Not applicable | Not applicable | https://ipac.ecosphere.fws.gov/ | https://www.epa.gov/water-research/storm-water-management-model-swmm | Not applicable | https://www.epa.gov/cmaq/download-cmaq | |
Contact Name
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EnviroAtlas Team | Sandra Lavorel | Sandra Lavorel | Richard Fulford | Marion Potschin | Marion Potschin | Stoyan Nedkov | Michael R. Guzy | Eric Lonsdorf | Nirmal K. Bhagabati | Susan H. Yee | United States Department of Agriculture | M. Abdalla | Not reported | Sean R. Griffin | Sam Riffell | Jun Yang | USFWS | David Burden | Gary Chapman | B.E.A. Fisher |
Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | Oregon State University, Dept. of Biological and Ecological Engineering | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Not reported | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Landscape Architecture and Horticulture, Temple University, 580 Meetinghouse Road, Ambler, PA 19002, USA. | 911 NE 11th Avenue Portland, OR 97232 | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 | Corvallis Environmental Research Laboratory, Western Fish Toxicology Station U.S. Environmental Protection Agency, Corvallis, Oregon 97330 | Little Beeches, Headley Road, Leatherhead KT22 8PT, UK. |
Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | Fulford.Richard@epa.gov | marion.potschin@nottingham.ac.uk | marion.potschin@nottingham.ac.uk | snedkov@abv.bg | Not reported | ericlonsdorf@lpzoo.org | nirmal.bhagabati@wwfus.org | yee.susan@epa.gov | http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | abdallm@tcd.ie | Not reported | srgriffin108@gmail.com | sriffell@cfr.msstate.edu | juny@temple.edu | fwhq_ipac@fws.gov | burden.david@epa.gov | N/A | None provided |
EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The 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: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Wildlife Products” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. Based on spatial land cover units originating from CORINE and further data sets, these regulating ecosystem services were quantified and mapped. Resulting maps show the ecosystems’ flood regulating service capacities in the case study area of the Malki Iskar river basin above the town of Etropole in the northern part of Bulgaria...The resulting map of flood regulation supply capacities shows that the Etropole municipality’s area has relatively high capacities for flood regulation. Areas of high and very high relevant capacities cover about 34% of the study area." AUTHOR'S DESCRIPTION: "The capacities of the identified spatial units were assessed on a relative scale ranging from 0 to 5 (after Burkhard et al., 2009). A 0-value indicates that there is no relevant capacity to supply flood regulating services and a 5-value indicates the highest relevant capacity for the supply of these services in the case study region. Values of 2, 3 and 4 represent respective intermediate supply capacities. Of course it depends on the observer’s estimation and knowledge which function–service relations in general are supposed to be relevant. But, this scale offers an alternative relative evaluation scheme, avoiding the presentation of monetary or normative value-transfer results. The 0–5 capacity values’ classifications for the different land cover types were based on the spatial analyses of different biogeophysical and land use data combined with hydrological modeling as described before…The supply capacities of the land cover classes and soil types in the study area were assigned to every unit in their databases. GIS map layers, containing information about the capacity to supply flood regulation for every polygon, were created. The map of supply capacities of flood regulating ecosystem services was elaborated by overlaying the GIS map layers of the land cover and the soils’ capacities." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." | Please note: This ESML entry describes 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: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... We mapped biomass carbon by assigning carbon values (in ton ha_1) for aboveground, belowground, and dead organic matter to each LULC class based on values from literature, as described in Tallis et al. (2010). We mapped soil carbon separately, as large quantities of carbon are stored in peat soil (Page et al., 2011). We estimated total losses in peat carbon over 50 years into the future scenarios, using reported annual emission rates for specific LULC transitions on peat (Uryu et al., 2008)...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] | 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. | AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance." | 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." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT: "The level of air pollution removal by green roofs in Chicago was quantified using a dry deposition model. The result showed that a total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs in one year with O3 accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2 (7%). The highest level of air pollution removal occurred in May and the lowest in February. The annual removal per hectare of green roof was 85 kg/ha/yr. The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs. Although costly, the installation of green roofs could be justified in the long run if the environmental benefits were considered. The green roof can be used to supplement the use of urban trees in air pollution control, especially in situations where land and public funds are not readily available." | IPaC is a project planning tool that streamlines the USFWS environmental review process. Explores species and habitat: See if any listed species, critical habitat, migratory birds or other natural resources may be impacted by your project. Using the map tool, explore other resources in your location, such as wetlands, wildlife refuges, GAP land cover, and other important biological resources. Conduct a regulatory review: Log in and define a project to get an official species list and evaluate potential impacts on resources managed by the U.S. Fish and Wildlife Service. Follow IPaC's Endangered Species Act (ESA) Review process—a streamlined, step-by-step consultation process available in select areas for certain project types, agencies, and species. Build a Consultation Package: Consultation Package Builder (CPB) replaces and improves on the original Impact Analysis by providing an interactive, step-by-step process to help you prepare a full consultation package leveraging U.S. Fish and Wildlife Service data and recommendations, including conservation measures designed to help you avoid or minimize effects to listed species. |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
ABSTRACT: "Continuous-flow toxicity tests were conducted to determine the relative tolerances of newly hatched alevins, swim-up alevins, parr, and smolts of chinook salmon (Oncorhynchus tshawytscha) and steelhead (Salmo gairdneri) to cadmium, copper, and zinc. Newly hatched alevins were much more tolerant to cadmium and, to a lesser extent, to zinc than were later juvenile forms. However, the later progression from swim-up alevin, through parr, to smolt was accompanied by a slight increase in metal tolerance. The 96-h LC50 values for all four life stages ranged from 1.0 to >27ug Cd/liter, 17 to 38ug Cu/liter, and 93 to 815ug Zn/liter. Steelhead were consistently more sensitive to these metals than were chinook salmon. When a sensitive life stage for acute toxicity tests with metals is sought, the more resistant newly hatched alevins should be avoided. Although tolerance may increase with age, all later juvenile life stages are more sensitive and should give similar results. | This paper shows how the advanced chemical transport model CMAQ can be used to estimate future levels of PM2.5 in the UK, the key air pollutant in terms of human health effects, but one which is largely made up from the formation of secondary particulate in the atmosphere. By adding the primary particulate contribution from typical urban roads and including a margin for error, it is concluded that the current indicative limit value for PM2.5 will largely be met in 2020 assuming 2006 meteorological conditions. Contributions to annual average regional PM2.5 concentration from wild fires in Europe in 2006 and from possible climate change between 2006 and 2020 are shown to be small compared with the change in PM2.5 concentration arising from changes in emissions between 2006 and 2020. The contribution from emissions from major industrial sources regulated in the UK is estimated from additional CMAQ calculations. The potential source strength of these emissions is a useful indicator of the linearity of the response of the atmosphere to changes in emissions. Uncertainties in the modelling of regional and local sources are taken into account based on previous evaluations of the models. Future actual trends in emissions mean that exceedences of limit values may arise, and these and further research into PM2.5 health effects will need to be part of the future strategy to manage PM2.5 concentrations. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identifed | None identified | None identified | None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None provided | None provided | climate change | None identified | None identified | None reported | None identified | Determination of Effects on ESA listed taxa. | NA | NA | None identified |
Biophysical Context
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No additional description provided | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | 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. | No additional description provided | No additional description provided | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Not applicable | 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. | Conservation Reserve Program lands left to go fallow | No additional description provided | N/A | NA | Microcosms | United kingdom atmosphere |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented | No scenarios presented | fertilization | No scenarios presented | No scenarios presented | N/A | No scenarios presented | N/A | NA | Life stage | 2020 European emissions scenario |
EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application |
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 | Application of existing 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 | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
EM Temporal Extent
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2008-2010 | 2007-2008 | Not reported | 2006-2011 | 2000 | 1990-2030 | Not reported | 1990-2050 | 2000-2002 | 2008-2020 | 1978 - 2009 | Not applicable | 1961-1990 | Not applicable | 1988-2014 | 2008 | July 2006 to July 2007 | Not applicable | Not applicable | 1978 | 2006-2020 |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | Not applicable | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both | Not applicable | both |
EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | continuous | Not applicable | discrete |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 2 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 14 |
EM Temporal Grain Size Unit
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Hour | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Month | Not applicable | Not applicable | Not applicable | Year |
EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Other | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Not applicable | Point or points | Not applicable | Physiographic or ecological | Physiographic or ecological | Geopolitical | Not applicable | No location (no locational reference given) | Geopolitical | Geopolitical |
Spatial Extent Name
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Durham NC and vicinity | Central French Alps | Central French Alps | Tampa Bay | The EU-25 plus Switzerland and Norway | The EU-25 plus Switzerland and Norway | Municipality of Etropole | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Central New Jersey and east-central Pennsylvania | central Sumatra | Guanica Bay watershed | Not applicable | Oak Park Research centre | Not applicable | Nachusa Grasslands | Piedmont Ecoregion | Chicago | Not applicable | Not applicable | Northwest | United Kingdom |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 100-1000 km^2 | Not applicable | 1-10 ha | Not applicable | 10-100 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | Not applicable | Not applicable | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 |
EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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) | Not applicable | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:map units delineated by user based on project. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
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irregular | 20 m x 20 m | 20 m x 20 m | 1 km^2 | 1 km x 1 km | 1 km x 1 km | Distributed by land cover and soil type polygons | varies | 30 m x 30 m | 30 m x 30 m | HUC | Not applicable | Not applicable | map units | Area varies by site | Not applicable | plot (green roof) size | Not applicable | mm | Not applicable | Not applicable |
EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | Logic- or rule-based | Logic- or rule-based | Analytic | Numeric | Analytic | Analytic | Analytic | Not applicable | Numeric | Analytic | Analytic | Analytic | Analytic | Other or unclear (comment) | Numeric | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | Not applicable | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
Model Calibration Reported?
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Unclear | No | No | No | No | No | No | Unclear | Unclear | No | No | Not applicable | Yes | Not applicable | No | Yes | Unclear | Not applicable | Not applicable | No | Yes |
Model Goodness of Fit Reported?
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No | Yes | No | No | No | No | No | No | No | No | No | Not applicable |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | No | No | No | Not applicable | Not applicable | No |
Yes ?Comment:Two versions of CMAQ (v4.6 and v4.7) were used to assess performance. Both values are provided here respectively. |
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 | None |
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Model Operational Validation Reported?
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No | No | No | No | Yes | No | No | 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 | No | Yes | Not applicable | No | No | No | Not applicable | Not applicable | No | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Not applicable | No | No | No | Not applicable | Not applicable | No | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Not applicable | No | Yes | No | Not applicable | Not applicable | Yes | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
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None |
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
None | None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
Centroid Latitude
em.detail.ddLatHelp
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35.99 | 45.05 | 45.05 | 27.74 | 50.53 | 50.53 | 42.8 | 44.11 | 40.2 | 0 | 17.96 | Not applicable | 52.86 | Not applicable | 41.89 | 36.23 | 41.88 | Not applicable | Not applicable | 44.53 | 54 |
Centroid Longitude
em.detail.ddLongHelp
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-78.96 | 6.4 | 6.4 | -82.57 | 7.6 | 7.6 | 24 | -123.09 | -74.8 | 102 | -67.02 | Not applicable | 6.54 | Not applicable | -89.34 | -81.9 | 87.65 | Not applicable | Not applicable | 123.25 | 4 |
Centroid Datum
em.detail.datumHelp
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None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | None provided | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Not applicable | Provided | Not applicable | Provided | Estimated | Provided | Not applicable | Not applicable | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Created Greenspace | Atmosphere | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Grasslands | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Tundra | Ice and Snow | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Atmosphere |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Not applicable | Not applicable | Mountainous flood-prone region | Agricultural-urban interface at river junction | Cropland and surrounding landscape | 104 land use land cover classes | Tropical terrestrial | None identified | farm pasture | Not applicable | Restored prairie, prairie remnants, and cropland | grasslands | urban green roofs | None | User-defined catchments | Modeling stream exposure | United Kingdom atmosphere |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Zone within an ecosystem | 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 is finer than that of the Environmental Sub-class | Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Not applicable | Other or unclear (comment) | 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-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community | Community | Species | Not applicable | Not applicable | Not applicable | Not applicable | Species | Community | Not applicable | Not applicable | Not applicable | Not applicable | Species | Species | Not applicable |
Other (Comment) ?Comment:ESA designations include species and Ecological Significan Units of species |
Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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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-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
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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-59 ![]() |
EM-79 | EM-81 |
EM-102 ![]() |
EM-119 |
EM-125 ![]() |
EM-132 |
EM-333 ![]() |
EM-339 |
EM-349 ![]() |
EM-432 | EM-434 | EM-598 | EM-712 |
EM-788 ![]() |
EM-845 | EM-945 | EM-967 | EM-968 |
EM-984 ![]() |
EM-1021 |
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None | None |
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None |
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None |
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None |
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None |
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