Large subsurface treatment systems (LSTS) and rapid infiltration basins (RIB) are preferred onsite wastewater treatments compared to direct discharge of treated wastewater to streams and adjacent facilities. Discharge of these wastewater treatments may result in contaminant loading to aquifers that also serve as drinking water sources downgradient from the discharge site. Until recently, few studies have characterized the contribution of micropollutants (e.g. pharmaceuticals, fragrances, flame retardants, etc.) to receiving aquifers. We conducted a pilot project to characterize the occurrence of micropollutants in groundwater downgradient from 7 on-site treatment systems in Minnesota, USA: 5 community LSTS and 2 municipal RIB. One downgradient monitoring well was sampled three times at each facility over one year. Of 223 micropollutants analyzed, 35 were detected. Total sample concentrations ranged from 90 to 4,039 ng/L. Sulfamethoxazole (antibiotic) was detected in all samples at concentrations from 7 to 965 ng/L. Other pharmaceuticals (0.12–1,000 ng/L), organophosphorus flame retardants (10–500 ng/L), and other anthropogenic chemicals (4–775 ng/L) were also detected. The numbers and concentrations of micropollutants detected were inversely related to dissolved oxygen and depth to water. Ratios of pharmaceutical concentrations to human-health screening values were <0.10 for most samples. However, concentrations of carbamazepine and sulfamethoxazole exceeded screening values at two sites. Study results illustrate that large on-site wastewater systems designed to discharge to permeable soil or shallow groundwater effectively deliver pharmaceuticals and other micropollutants to groundwater aquifers and could contribute micropollutants to drinking water via water supply wells.
Epithermal gold-silver deposits are vein, stockwork, disseminated, and replacement deposits that are mined primarily for their gold and silver contents; some deposits also contain substantial resources of lead, zinc, copper, and (or) mercury. These deposits form in the uppermost parts of the crust, at depths less than about 1,500 meters below the water table, and at temperatures below about 300 °C. Most epithermal gold-silver deposits are genetically related to hydrothermal systems associated with subaerial volcanism and intrusion of calc-alkaline magmas along convergent plate margins. These deposits formed throughout most of geologic time, although most known deposits are Cenozoic, which reflects preferential preservation of these shallowly formed deposits in tectonically unstable regions. Epithermal gold-silver deposits range in size from tens of thousands to greater than 1 billion metric tons of ore and have gold contents of 0.1 to greater than 30 grams per metric ton and silver contents of less than 1 to several thousand grams per metric ton. Historically, these deposits have been an important source of gold and silver and are estimated to contain about 8 percent of global gold. The wide range of tonnage-grade characteristics makes epithermal gold-silver deposits an attractive target for small and large exploration and mining companies.
This report constitutes a new descriptive model for epithermal gold-silver deposits. It summarizes characteristics of known deposits, including their geological, geophysical, geochemical, and geoenvironmental aspects. Models concerning the genesis of epithermal gold-silver deposits are discussed. The application of descriptive and genetic aspects of the model to mineral exploration and resource assessment of undiscovered deposits is described. Finally, areas where additional research is needed to better understand the genesis of these deposits are identified. An extensive summary table outlining the characteristics of about 100 epithermal gold-silver deposits is included as an appendix; this summary table includes most of the world’s largest epithermal gold-silver deposits, and many smaller, well-studied deposits.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit models for resource assessment (Investigations Report 2010–5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070Q","usgsCitation":"John, D.A., Vikre, P.G., du Bray, E.A., Blakely, R.J., Fey, D.L., Rockwell, B.W., Mauk, J.L., Anderson, E.D., and Graybeal, F.T., 2018, Descriptive models for epithermal gold-silver deposits: U.S. Geological Survey Scientific Investigations Report 2010–5070–Q, 247 p., https://doi.org/10.3133/sir20105070Q.","productDescription":"Report: xi, 246 p.; 1 Figure; 3 Appendixes","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069851","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":359100,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2010/5070/q/sir20105070q_appendix2.xlsx","text":"Appendix 2","size":"19 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2010-5070 Chapter Q Appendix 2","linkHelpText":"Grade and tonnage data and data sources for epithermal gold deposits"},{"id":359099,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2010/5070/q/sir20105070q_appendix1.xlsx","text":"Appendix 1","size":"55 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2010-5070 Chapter Q Appendix 1","linkHelpText":"Characteristics of epithermal gold-silver deposits"},{"id":359096,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5070/q/coverthb.jpg"},{"id":359097,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/q/sir20105070q.pdf","text":"Report","size":"40.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5070 Chapter Q"},{"id":359098,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2010/5070/q/sir20105070q_figA1.pdf","text":"Figure A1","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5070 Chapter Q Figure A1"},{"id":359101,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2010/5070/q/sir20105070q_appendix3.xlsx","text":"Appendix 3","size":"4 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2010-5070 Chapter Q Appendix 3","linkHelpText":"Compilation of isotopic data for epithermal gold-silver mineral deposits"}],"contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Accounting for migration and connectivity of mobile species across the annual cycle can present challenges for conservation and management efforts. The use of stable isotope approaches to examine the movements and ecology of wildlife has been widespread over the past two decades. Hydrogen stable isotope (δ2H) composition, in particular, has been frequently used to provide insight into the origin of migratory species, although isotopes of other elements are sometimes used. These intrinsic markers can yield valuable information about distributions of wildlife on a broad scale, with reduced labor and expense compared to tracking and telemetry. Many of the applications of isotopes to migratory species to date have addressed connectivity and origin, and studies in support of conservation biology are less common. In addition, there are few guides for how to best employ these methods for management. Therefore, we provide an overview for the wildlife conservation and management community on how stable isotope methods may be applied to conservation problems and a primer on the process for assigning geographic origins to terrestrial wildlife. We also discuss best practices for employing environmental isoscapes (isotopic distributions across landscapes), rescaling functions, and the assumptions required for assignment to origin while highlighting emerging issues in the modeling process. Finally, we provide example applications to illustrate these principles, and we explore strengths and limitations of this approach in a conservation context.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2018.10.019","usgsCitation":"Vander Zanden, H.B., Nelson, D.M., Wunder, M., Conkling, T., and Katzner, T., 2018, Application of isoscapes to determine geographic origin of terrestrial wildlife for conservation and management: Biological Conservation, v. 228, p. 268-280, https://doi.org/10.1016/j.biocon.2018.10.019.","productDescription":"13 p.","startPage":"268","endPage":"280","ipdsId":"IP-096404","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":366325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"228","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vander Zanden, Hanna B.","contributorId":217914,"corporation":false,"usgs":false,"family":"Vander Zanden","given":"Hanna","email":"","middleInitial":"B.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":767805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, David M.","contributorId":175098,"corporation":false,"usgs":false,"family":"Nelson","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13479,"text":"University of Maryland Center for Environmental Science, Appalachian Laboratory, 301 Braddock Road, Frostburg, Maryland","active":true,"usgs":false}],"preferred":false,"id":767806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wunder, Michael B.","contributorId":80599,"corporation":false,"usgs":false,"family":"Wunder","given":"Michael B.","affiliations":[{"id":6674,"text":"Department of Integrative Biology, University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":767807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conkling, Tara 0000-0003-1926-8106","orcid":"https://orcid.org/0000-0003-1926-8106","contributorId":217915,"corporation":false,"usgs":true,"family":"Conkling","given":"Tara","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":767808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":767804,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273410,"text":"70273410 - 2018 - Sulfur cycle in the Valles Caldera volcanic complex, New Mexico – Letter 1: Sulfate sources in aqueous system, and implications for S isotope record in Gale Crater on Mars","interactions":[],"lastModifiedDate":"2026-01-14T14:29:24.274387","indexId":"70273410","displayToPublicDate":"2018-11-07T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Sulfur cycle in the Valles Caldera volcanic complex, New Mexico – Letter 1: Sulfate sources in aqueous system, and implications for S isotope record in Gale Crater on Mars","docAbstract":"Initial in situ sulfur (S) isotope measurements of the Martian bedrock in Gale Crater have revealed an unexpectedly wide range of δ34S values (−47 to +28%). Generally, it is unclear what processes could have contributed to these large isotope fractionations. Therefore, we studied S sources and aqueous SO2−4 cycling in the Valles Caldera volcanic complex, New Mexico to better understand S isotope fractionations related to S degassing, hydrothermal activity, and low-temperature processes in aqueous environment. Overall, our study demonstrates that volcanic systems show large spatial heterogeneity in δ34S. Magmatic S sources are obvious in steam-dominated H2S degassing and precipitation of secondary minerals from hydrothermal fluids with low δ34S values of +0.9 ±3%. Locally, however, hydrothermal processes have resulted in more negative δ34S values in sulfide minerals (−18 to −4%) and more positive δ34S values in sulfate minerals (−1 to +3%). Major aqueous SO2−4 sources are oxidation of H2S from modern hydrothermal gas emission, and oxidation and dissolution of sulfide and sulfate minerals present in the hydrothermally altered bedrock and crater-lake sediments. The δ34S of aqueous SO2−4 in surface water and groundwater varies widely (−8 to +5%) and is similar to major S endmembers that undergo oxidation and/or dissolution by active hydrological system. Minor SO2−4 contributions with more positive δ34S values (+9 to +14%) come from deeply circulating geothermal fluids and negligible amounts from atmospheric deposition (+5 to +7% in snow). Elevated SO2−4contents are mainly associated with modern and past H2S emissions and oxidations near the surface. On regional scale, however, most of the intracaldera bedrock is S-depleted, thus the SO2−4contents are usually low in the surface aquatic system and younger sedimentary lake deposits formed at times of negligible near surface hydrothermal activity. In general, magmatic-hydrothermal processes apparently cause the largest δ34S variation in S-bearing minerals on volcanic terrains. Therefore, we infer that the measured wide range of δ34S values in the Gale sediments by the Curiosity rover on Mars can be explained by S isotope composition of magmatic-hydrothermal sulfide and sulfate minerals that were present in the initial igneous/volcanic rocks prior to crater formation. Later aqueous processes involved oxidation and dissolution of S minerals initially present in these rocks and led to subsequent formation of diagenetic fluids and alteration products enriched in SO2−4 with relatively large δ34S variation. Additionally, physical erosion, transport and deposition of detrital hydrothermal S minerals from igneous/volcanic rocks might be in part responsible for the measured wide range of δ34S in Gale Crater. These unique S isotope results, measured in situ on another planet for the first time, imply the importance of magmatic-hydrothermal fluids in S transport on early Mars and their subsequent alteration in low-temperature aqueous environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2018.10.036","usgsCitation":"Szynkiewicz, A., Goff, F.E., Vaniman, D., and Pribil, M., 2018, Sulfur cycle in the Valles Caldera volcanic complex, New Mexico – Letter 1: Sulfate sources in aqueous system, and implications for S isotope record in Gale Crater on Mars: Earth and Planetary Science Letters, v. 506, p. 540-551, https://doi.org/10.1016/j.epsl.2018.10.036.","productDescription":"12 p.","startPage":"540","endPage":"551","ipdsId":"IP-101952","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":498587,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Gale Crater, Mars, Valles Caldera volcanic complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.23576880836745,\n 36.186869071716416\n ],\n [\n -108.23576880836745,\n 35.36300791120573\n ],\n [\n -107.20620355256943,\n 35.36300791120573\n ],\n [\n -107.20620355256943,\n 36.186869071716416\n ],\n [\n -108.23576880836745,\n 36.186869071716416\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"506","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Szynkiewicz, Anna","contributorId":365045,"corporation":false,"usgs":false,"family":"Szynkiewicz","given":"Anna","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":953619,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goff, Fraser E.","contributorId":291490,"corporation":false,"usgs":false,"family":"Goff","given":"Fraser","email":"","middleInitial":"E.","affiliations":[{"id":12545,"text":"USGS retired","active":true,"usgs":false}],"preferred":false,"id":953620,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaniman, David","contributorId":173231,"corporation":false,"usgs":false,"family":"Vaniman","given":"David","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":953621,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pribil, Michael J. 0000-0003-4859-8673 mpribil@usgs.gov","orcid":"https://orcid.org/0000-0003-4859-8673","contributorId":141158,"corporation":false,"usgs":true,"family":"Pribil","given":"Michael","email":"mpribil@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":953622,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199785,"text":"ofr20181156 - 2018 - First comprehensive list of non-native species established in three major regions of the United States","interactions":[],"lastModifiedDate":"2018-11-13T14:40:52","indexId":"ofr20181156","displayToPublicDate":"2018-11-06T16:20:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1156","displayTitle":"First Comprehensive List of Non-Native Species Established in Three Major Regions of the United States","title":"First comprehensive list of non-native species established in three major regions of the United States","docAbstract":"Invasive species are a subset of non-native (or alien) species, and knowing what species are non-native to a region is a first step to managing invasive species. People have been compiling non-native and invasive species lists ever since these species started causing harm, yet national non-native species lists are neither universal, nor common. Non-native species lists serve diverse purposes: watch lists for preventing invasions, inventory and monitoring lists for research and modeling, regulatory lists for species control, and nonregulatory lists for raising awareness. This diversity of purpose and the lists’ variation in geographic scope make compiling comprehensive lists of established (or naturalized) species for large regions difficult. However, listing what species are non-native in an area helps measure Essential Biodiversity Variables for invasive species monitoring and mount an effective response to established non-native species. In total, 1,166 authoritative sources were reviewed to compile the first comprehensive non-native species list for three large regions of the United States: Alaska, Hawaii, and the conterminous United States (lower 48 States). The list contains 11,344 unique names: 598 taxa for Alaska, 5,848 taxa for Hawaii, and 6,675 taxa for the conterminous United States. The list is available to the public from U.S. Geological Survey ScienceBase (https://doi.org/10.5066/P9E5K160), and the intent, though not a guarantee, is to update the list as non-native species become established in, or are eliminated from, the United States. The list has been used to annotate non-native species occurrence records in the U.S. Geological Survey all-taxa mapping application, Biodiversity Information Serving Our Nation (BISON, https://bison.usgs.gov).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181156","collaboration":" ","usgsCitation":"Simpson, A., and Eyler, M.C., 2018, First comprehensive list of non-native species established in three major regions of the United States: U.S. Geological Survey Open-File Report 2018-1156, 15 p., https://doi.org/10.3133/ofr20181156.","productDescription":"v; 15 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090660","costCenters":[{"id":38106,"text":"Science Analytics and Synthesis Program ","active":true,"usgs":true}],"links":[{"id":437693,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E5K160","text":"USGS data release","linkHelpText":"A comprehensive list of non-native species established in three major regions of the United States: Version 3.0"},{"id":358802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1156/coverthb.jpg"},{"id":358803,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1156/ofr20181156.pdf","text":"Report","size":"1.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1156"}],"country":"United States","contact":"Director, Science Analytics Synthesis Program
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West 6th Avenue and Kipling Street
Lakewood, CO 80225
Wind and other energy development are expanding rapidly and on an unprecedented scale within the range of the Golden Eagle (Aquila chrysaetos) while other anthropogenic-related changes, wildfires, invasive plants, drought, and climate change are altering or destroying native habitats occupied by Golden Eagles. However, the potential effects of these factors on North American Golden Eagle populations are largely unknown and the most recent evidence indicates that the population in the western United States is declining slightly. Impediments to evaluating the potential effects of energy development projects on wintering Golden Eagles include issues of scale and a paucity of available information about eagle winter use areas and ecology. We applied a predictive model of eagle winter distribution developed for Idaho and Montana, to Idaho, Utah, Nevada and eastern Oregon to help identify potential wintering areas and identify risks that occur in those areas. The model identifies ~40% of the four state study area as potentially suitable eagle winter habitat and provides a basis for spatial assessment of possible risk factors to eagles wintering there. We used eBird and Christmas Bird Count citizen science datasets for an independent evaluation of the accuracy of our predictive distribution model. The model was robust, accurately predicting the presence of wintering Golden Eagles significantly more often than expected. We used digital environmental datasets (layers) of potential risk factors, in conjunction with model predicted eagle distribution, to better understand and estimate the extent of risks to the wintering eagle population in the study area. These layers represent available data for some of the factors previously identified as risks in the landscape to wintering Golden Eagles. The majority of predicted eagle wintering areas occurred where there was little habitat fragmentation (<10%). All predicted winter areas contained at least one potential risk factor (e.g., potential for energy development); 39.4% of predicted winter areas contained at least two known risk factors. The greatest number of risks often occurred where the human footprint was highest and where eagles were less likely to occur during winter. Our results can be used to help prioritize field surveys for identifying important Golden Eagle winter areas in the western United States and determine potential locations where energy development is least likely to have negative effects on wintering eagles. Survey efforts can be allocated in consideration of management and conservation objectives based on predicted habitat suitability and risk factors. For example, surveys for areas of high suitability and low risk can identify places to focus management for conservation of eagle winter areas. Further, sites proposed for wind energy development could be reviewed initially based on model predicted eagle wintering areas and then surveyed to determine if permitting for development is appropriate.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Machine learning for ecology and sustainable natural resource management","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-96978-7_19","usgsCitation":"Craig, E.H., Fuller, M.R., Craig, T.H., and Huettmann, F., 2018, Assessment of potential risks from renewable energy development and other anthropogenic factors to wintering Golden Eagles in the western United States, chap. of Machine learning for ecology and sustainable natural resource management, p. 379-407, https://doi.org/10.1007/978-3-319-96978-7_19.","productDescription":"29 p.","startPage":"379","endPage":"407","ipdsId":"IP-097959","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":361650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-06","publicationStatus":"PW","contributors":{"editors":[{"text":"Humphries, Grant","contributorId":213887,"corporation":false,"usgs":false,"family":"Humphries","given":"Grant","email":"","affiliations":[],"preferred":false,"id":758612,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Magness, Dawn","contributorId":147692,"corporation":false,"usgs":false,"family":"Magness","given":"Dawn","affiliations":[{"id":16903,"text":"U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, 99669, USA","active":true,"usgs":false}],"preferred":false,"id":758613,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Huettmann, Falk","contributorId":15663,"corporation":false,"usgs":false,"family":"Huettmann","given":"Falk","email":"","affiliations":[],"preferred":false,"id":758614,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Craig, Erica H.","contributorId":176469,"corporation":false,"usgs":false,"family":"Craig","given":"Erica","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":758021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Mark R. 0000-0001-7459-1729 mark_fuller@usgs.gov","orcid":"https://orcid.org/0000-0001-7459-1729","contributorId":2296,"corporation":false,"usgs":true,"family":"Fuller","given":"Mark","email":"mark_fuller@usgs.gov","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":758022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Craig, Tim H.","contributorId":213558,"corporation":false,"usgs":false,"family":"Craig","given":"Tim","email":"","middleInitial":"H.","affiliations":[{"id":27672,"text":"Aquila Environmental","active":true,"usgs":false}],"preferred":false,"id":758023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huettmann, Falk","contributorId":15663,"corporation":false,"usgs":false,"family":"Huettmann","given":"Falk","email":"","affiliations":[],"preferred":false,"id":758024,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199978,"text":"sir20185137 - 2018 - Revised groundwater-flow model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015","interactions":[],"lastModifiedDate":"2019-03-27T11:06:00","indexId":"sir20185137","displayToPublicDate":"2018-11-06T08:06:51","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5137","displayTitle":"Revised Groundwater-flow Model of the Glacial Aquifer System North of Aberdeen, South Dakota, Through Water Year 2015","title":"Revised groundwater-flow model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015","docAbstract":"The city of Aberdeen, in northeastern South Dakota, requires an expanded and sustainable supply of water to meet current and future demands. Conceptual and numerical models of the glacial aquifer system in the area north of Aberdeen were developed by the U.S. Geological Survey in cooperation with the City of Aberdeen in 2012. The U.S. Geological Survey, in cooperation with the City of Aberdeen, completed a study to revise the original numerical groundwater-flow model using data through water year (WY) 2015 to aid the City of Aberdeen in their development of plans and strategies for a sustainable water supply and to increase understanding of the glacial aquifer system and groundwater-flow system near Aberdeen. The original model was revised to improve the fit between model-simulated values and observed (measured or estimated) data, provide greater insight into surface-water interactions, and improve the usefulness of the model for water-supply planning. The revised groundwater-flow model (hereafter referred to as the “revised model”) presented in this report supersedes the original model.
The purpose of this report is to describe a revised groundwater-flow model including data collection, model calibration, and model results for the glacial aquifer system including the Elm, Middle James, and Deep James aquifers north of Aberdeen, South Dakota, using updated hydrologic data through WY 2015. The original numerical model was revised in several ways. The model was modified by adding four new layers, which included a surficial layer, two intervening confining layers, and a shale bedrock layer. The revised model provides an improved understanding of the groundwater-flow system in comparison to the original model.
The principal aquifers of the model area include portions of the Elm, Middle James, and Deep James aquifers. The lithologic information used to define and describe the aquifers in the model area was unaltered; however, aquifer properties and boundary conditions were reviewed and updated using geological information reported by the South Dakota Department of Environmental and Natural Resources and information obtained from geophysical investigations for this study. The horizontal extent of the Elm, Middle James, and Deep James aquifers was unaltered from the original model. The thickness of the Deep James aquifer was modified based on interpretations from the geophysical investigations. In general, groundwater in the Elm aquifer flowed from northwest to southeast and locally towards rivers and streams. Similarly, in the Middle James and Deep James aquifers, groundwater also typically flowed southeast.
The revisions made to the original model include use of the following MODFLOW stress packages: Recharge, Evapotranspiration, Time-Variant Specified Head, Wells, Drains, and Stream Flow Routing, all of which were updated from the original model except for the Stream Flow Routing Package, which replaced the River Package used in the original model. Model calibration is the process of estimating model parameters to minimize the differences, or residuals, between observed data and simulated values; therefore, Parameter ESTimation (PEST) software was used to optimize model input parameters by matching model-simulated values to observed data. Calibration parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, specific yield, specific storage, and vertical streambed conductance for stream and drain cells. Multipliers were used to calibrate the recharge and evapotranspiration stresses. Evapotranspiration extinction depth also was adjusted during model calibration.
Comparisons to the original model are described to highlight the changes made in the revised model. In general, the revised model adequately simulates the natural system and compares favorably with observed hydrologic data. Simulated water levels were evaluated by comparing them to single water-level observations at selected well locations. The selected wells were the same wells used in the original model. The coefficient of determination value between simulated and observed water levels for the revised model was 0.89 and included simulated and observed values from October 1, 1974 (WY 1975), through September 30, 2015 (WY 2015). The coefficient of determination value for the original model was 0.94 and included simulated and observed values from October 1, 1974, through September 30, 2009. The difference may indicate that the original model could have been overfit to hydraulic head observations because base flow was not simulated. The additional data used in the revised model included some climatically wetter, more extreme periods, such as 2011, in which annual precipitation was 30.9 inches. Average annual precipitation for the original model timeframe, which included data from WYs 1975–2009, was 20.26 inches. Additional precipitation data for WYs 2010–15, included in the revised model timeframe, resulted in an average annual precipitation for WYs 1975–2015 in the model area of 20.6 inches. The larger variability in climate data coupled with the additional water-level data could explain the lower coefficient of determination for water levels in the revised model.
The revised model was used to calculate various groundwater-budget components for steady-state and transient conditions for WYs 1975–2015. The time-variant specified-head cells in the revised model had the largest change when compared to the original steady-state model for inflows and outflows. Comparing the transient budget components between the original and the revised models indicated that inflow from recharge and time-variant specified-head cells had the greatest effect on groundwater inflows, and outflow from storage had the greatest effect on groundwater outflows. The simulated potentiometric contours from the revised model were compared with (1) the observed (interpreted) potentiometric surface (layer 2) and the hydraulic head values (layers 4 and 6) and (2) the simulated contours from the original model. The simulated hydraulic gradients and general direction of groundwater flow in the Elm aquifer in the revised model generally matched the observed potentiometric contours, the simulated potentiometric contours from the original model, and general flow directions interpreted to be perpendicular to the contours. Minor discrepancies between simulated potentiometric contours from the revised model and the observed potentiometric contours may be due to the lack of observed data in the model area.
The revised model was designed to reduce the limitations of the original model. The revisions were validated by comparing the results of the original model with the revised model. A primary benefit of the revised model is the inclusion of the surficial deposits and the confining units as explicit layers in the model. The addition of the surficial layer was beneficial for three primary reasons: (1) more accurate representation of recharge from precipitation, (2) more accurate representation of groundwater evapotranspiration, and (3) more accurate representation of groundwater and surface-water interactions. The groundwater model is a numeric approximation of a complex physical hydrologic system, and the revised model data were interpolated in regions with sparse data. Additionally, model discretization included averaged and interpolated values for water use, withdrawal rates, and hydraulic conductivity. The revised model provides a useful estimate for hydraulic gradients, groundwater-flow directions, and aquifer response to groundwater withdrawals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185137","collaboration":"Prepared in cooperation with the City of Aberdeen","usgsCitation":"Valder, J.F., Eldridge, W.G., Davis, K.W., Medler, C.J., and Koth, K.R., 2018, Revised groundwater-flow model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015: U.S. Geological Survey Scientific Investigations Report 2018–5137, 56 p., https://doi.org/10.3133/sir20185137.","productDescription":"Report: viii, 56 p.; Data Release","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-080010","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":359157,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JVNFLY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015"},{"id":359156,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5137/sir20185137.pdf","text":"Report","size":"4.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5137"},{"id":359155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5137/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Aberdeen","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.6,\n 45.45\n ],\n [\n -98.27,\n 45.45\n ],\n [\n -98.27,\n 45.7\n ],\n [\n -98.6,\n 45.7\n ],\n [\n -98.6,\n 45.45\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
The geologic map of the San Antonio Mountain area in northern New Mexico and southern Colorado is located along the west-central part of the San Luis Valley. The San Luis Valley is the geomorphic expression of the San Luis Basin, an extensional basin associated with the northern Rio Grande rift. Deposits within the map area record volcanic, sedimentary, and tectonic processes over the last ~33 million years. Oldest exposed deposits include Oligocene volcanic rocks associated with the southeast San Juan Mountains locus of volcanism within the Southern Rocky Mountains volcanic field. Overlying deposits of the Southern Rocky Mountains volcanic field are volcaniclastic sedimentary rocks interbedded with predominantly basaltic lava flows of Oligocene to Miocene age. Basalt to rhyolite volcanic rocks of the Pliocene to Pleistocene Taos Plateau volcanic field unconformably overlie Oligocene to Miocene volcanic and sedimentary deposits. Superposed on the Tertiary deposits are Pleistocene to Holocene alluvial and colluvial deposits.
North- to northwest-trending faults displace rocks within the map area. Magnitude of deformation is broadly correlative with age of the deposits inasmuch as Oligocene to Miocene rocks display a greater degree of fault displacement and east tilting than Pliocene volcanic rocks. Within the map area, faults displace Oligocene to Miocene deposits 10–30 meters with generally down-to-west offset, and the units dip eastward 3–7 degrees. Pliocene volcanic rocks exhibit shallower eastward dips inferred primarily from the slope of upper lava flow surfaces that dip eastward from 1–3 degrees and lava flows are generally displaced less than 5 meters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3417","usgsCitation":"Turner, K.J., Thompson, R.A., Cosca, M.A., Shroba, R.R., Chan, C.F., and Morgan, L.E., 2018, Geologic map of the San Antonio Mountain area, northern New Mexico and southern Colorado: U.S. Geological Survey Scientific Investigations Map 3417, scale 1:50,000, https://doi.org/10.3133/sim3417.","productDescription":"2 Plates: 54.57 x 45.00 inches; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-096384","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":359163,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72N51M5","text":"USGS data release","linkHelpText":"Data release of geospatial map database, argon geochronology and geochemistry data for: Geologic map of the San Antonio Mountain area, northern New Mexico and southern Colorado"},{"id":359166,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3417/sim3417_ReadMe.txt","text":"Read Me","size":"8.0 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3417 Read Me"},{"id":359159,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3417/sim3417_georeferenced.pdf","text":"Georeferenced Map","size":"23.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3417 Georeferenced Map"},{"id":359154,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3417/sim3417.pdf","text":"Map","size":"30.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3417 Map"},{"id":359153,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3417/coverthb.jpg"}],"country":"United States","state":"Colorado, New Mexico","otherGeospatial":"San Antonio Mountain area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.02936536993761,\n 37.00446329492584\n ],\n [\n -106.48758186305275,\n 37.00536016826341\n ],\n [\n -106.31462759849488,\n 36.86352300557036\n ],\n [\n -106.28655060749503,\n 36.615124233650306\n ],\n [\n -106.0203807328178,\n 36.611518344641\n ],\n [\n -106.02936536993761,\n 37.00446329492584\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 10.7 trillion cubic feet of potential coalbed gas resources in the Kutei and Barito Basin Provinces of Indonesia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20183055","usgsCitation":"Schenk, C.J., Mercier, T.J., Le, P.A., Tennyson, M.E., Finn, T.M., Brownfield, M.E., Marra, K.R., Gaswirth, S.B., Leathers-Miller, H.M., Pitman, J.K., and Drake, R.M., II, 2018, Assessment of coalbed gas resources in the Kutei and Barito Basin Provinces, Indonesia, 2018: U.S. Geological Survey Fact Sheet 2018–3055, 2 p., https://doi.org/10.3133/fs20183055.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-098259","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":359095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3055/fs20183055.pdf","text":"Report","size":"2.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3055"},{"id":359094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3055/coverthb.jpg"}],"country":"Indonesia","otherGeospatial":"Barito Basin Province, Kutei Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 113,\n -4.5\n ],\n [\n 120,\n -4.5\n ],\n [\n 120,\n 2\n ],\n [\n 113,\n 2\n ],\n [\n 113,\n -4.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The U.S. Geological Survey (USGS) has developed the National Land Cover Database (NLCD) to provide consistent land cover and land cover change products for the nation since 2001. As one of products in the NLCD, the percent impervious surface area (ISA), which was estimated with Landsat imagery, represents the fraction of human-made impervious area in a 30-m grid and has been used to quantify urban land cover types and extents for the United States. However, it is still a challenge to clearly determine urban land cover intensity and extents using remote sensing data with spatial and spectral resolutions similar to Landsat in part because of highly heterogeneous features of urban land cover. Most urban areas, especially in low intensity development areas, exhibit sub-pixel characteristics that mix impervious surface with other land covers (e.g., grass and trees) in the 30-m resolution satellite imagery. Furthermore, the influence of highly heterogeneous features in many urban areas and how they alter the spectral signature of urban landscapes has not yet been fully studied. Recent advances in remote sensing technology have provided multiple spectral and spatial resolution data from several satellites including WorldView (WV), Sentinel-2, and the Landsat Operational Land Imager (OLI). Remote sensing images having different spectral bands and high spatial resolution provide the potential to derive detailed information on the nature and properties of different surface materials on the urban ground. This study focuses on performance of mapping impervious surface using data collected from WorldView-3, Sentinel-2, and Landsat OLI. We compared ISA results estimated from these sensors and evaluated benefits and limitations of radiometric and spatial resolutions for mapping impervious surface in a study area on the Eastern corridor between Washington, D.C., and Baltimore, where developed impervious surface containing both residential housings, office buildings, and roads, in the United States. The impact of different band combinations in Sentinel-2 imagery on mapping urban impervious surface and urban land cover was also evaluated.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"Jul 22-27, 2018","conferenceLocation":"Valencia, Spain","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS.2018.8518013","usgsCitation":"Xian, G.Z., Shi, H., Dewitz, J., and Wu, Z., 2018, Analysis of different sensor performances in impervious surface mapping, in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, Jul 22-27, 2018, p. 8189-8192, https://doi.org/10.1109/IGARSS.2018.8518013.","productDescription":"4 p.","startPage":"8189","endPage":"8192","ipdsId":"IP-093474","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":377825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewitz, Jon 0000-0002-0458-212X dewitz@usgs.gov","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":2401,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","email":"dewitz@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":797261,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796924,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200814,"text":"70200814 - 2018 - Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus","interactions":[],"lastModifiedDate":"2018-11-13T13:24:06","indexId":"70200814","displayToPublicDate":"2018-11-05T09:12:19","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus","title":"Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus","docAbstract":"Multi-state occupancy modeling can often improve assessments of habitat use and site quality when animal activity or behavior data are available. We examine the use of the approach for evaluating foraging habitat suitability of the endangered Hawaiian hoary bat (Lasiurus cinereus semotus) from classifications of site occupancy based on flight activity levels and feeding behavior. In addition, we used data from separate visual and auditory sources, namely thermal videography and acoustic (echolocation) detectors, jointly deployed at sample sites to compare the effectiveness of each method in the context of occupancy modeling. Video-derived observations demonstrated higher and more accurate estimates of the prevalence of high bat flight activity and feeding events than acoustic sampling methods. Elevated levels of acoustic activity by Hawaiian hoary bats were found to be related primarily to beetle biomass in this study. The approach may have a variety of applications in bat research, including inference about species-resource relationships, habitat quality and the extent to which species intensively use areas for activities such as foraging.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0205150","usgsCitation":"Gorresen, P., Brinck, K.W., DeLisle, M.A., Montoya-Aiona, K., Pinzari, C., and Bonaccorso, F., 2018, Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus: PLoS ONE, v. 13, no. 10, p. 1-14, https://doi.org/10.1371/journal.pone.0205150.","productDescription":"e0205150; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-099393","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":460815,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0205150","text":"Publisher Index Page"},{"id":437695,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PPSHLW","text":"USGS data release","linkHelpText":"Oahu multi-state occupancy models of foraging habitat use by Hawaiian hoary bats 2017"},{"id":359218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-31","publicationStatus":"PW","scienceBaseUri":"5be2b6b0e4b0b3fc5cf5b0bf","contributors":{"authors":[{"text":"Gorresen, P. Marcos 0000-0002-0707-9212","orcid":"https://orcid.org/0000-0002-0707-9212","contributorId":196628,"corporation":false,"usgs":false,"family":"Gorresen","given":"P. Marcos","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":750750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":150936,"corporation":false,"usgs":false,"family":"Brinck","given":"Kevin","email":"kbrinck@usgs.gov","middleInitial":"W.","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":750751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeLisle, Megan A.","contributorId":210453,"corporation":false,"usgs":false,"family":"DeLisle","given":"Megan","email":"","middleInitial":"A.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":750752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montoya-Aiona, Kristina 0000-0002-1776-5443 kmontoya-aiona@usgs.gov","orcid":"https://orcid.org/0000-0002-1776-5443","contributorId":5899,"corporation":false,"usgs":true,"family":"Montoya-Aiona","given":"Kristina","email":"kmontoya-aiona@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":750753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pinzari, Corinna A. 0000-0001-9794-7564","orcid":"https://orcid.org/0000-0001-9794-7564","contributorId":208455,"corporation":false,"usgs":false,"family":"Pinzari","given":"Corinna A.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":750754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bonaccorso, Frank 0000-0002-5490-3083 fbonaccorso@usgs.gov","orcid":"https://orcid.org/0000-0002-5490-3083","contributorId":143709,"corporation":false,"usgs":true,"family":"Bonaccorso","given":"Frank","email":"fbonaccorso@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":750749,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70200824,"text":"70200824 - 2018 - Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling","interactions":[],"lastModifiedDate":"2019-01-28T08:51:33","indexId":"70200824","displayToPublicDate":"2018-11-05T08:56:28","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling","docAbstract":"A wide range of research shows that nutrient availability strongly influences terrestrial carbon (C) cycling and shapes ecosystem responses to environmental changes and hence terrestrial feedbacks to climate. Nonetheless, our understanding of nutrient controls remains far from complete and poorly quantified, at least partly due to a lack of informative, comparable, and accessible datasets at regional-to-global scales. A growing research infrastructure of multi-site networks are providing valuable data on C fluxes and stocks and are monitoring their responses to global environmental change and measuring responses to experimental treatments. These networks thus provide an opportunity for improving our understanding of C-nutrient cycle interactions and our ability to model them. However, coherent information on how nutrient cycling interacts with observed C cycle patterns is still generally lacking. Here, we argue that complementing available C-cycle measurements from monitoring and experimental sites with data characterizing nutrient availability will greatly enhance their power and will improve our capacity to forecast future trajectories of terrestrial C cycling and climate. Therefore, we propose a set of complementary measurements that are relatively easy to conduct routinely at any site or experiment and that, in combination with C cycle observations, can provide a robust characterization of the effects of nutrient availability across sites. In addition, we discuss the power of different observable variables for informing the formulation of models and constraining their predictions. Most widely available measurements of nutrient availability often do not align well with current modelling needs. This highlights the importance to foster the interaction between the empirical and modelling communities for setting future research priorities.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/aaeae7","usgsCitation":"Vicca, S., Stocker, B., Reed, S.C., Wieder, W.R., Bahn, M., Fay, P.A., Janssens, I., Lambers, H., Penuelas, J., Piao, S., Rebel, K., Sardans, J., Sigurdsson, B.D., Van Sundert, K., Wang, Y., Zaehle, S., and Ciais, P., 2018, Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling: Environmental Research Letters, v. 13, p. 1-13, https://doi.org/10.1088/1748-9326/aaeae7.","productDescription":"Article 125006; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-101808","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468261,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aaeae7","text":"Publisher Index 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The dry samples were consistently enriched for hydrogenase genes, indicating that molecular hydrogen may serve as an energy source for the desiccated soil communities. The water pulse was associated with a restructuring of the metatranscriptome, particularly for the biocrusts. Biocrusts increased transcripts for photosynthesis and carbon fixation, suggesting a rapid resuscitation upon wetting. In contrast, the trampled surface soils showed a much smaller response to wetting, indicating that trampling altered the metabolic response of the community. Finally, several biogeochemical cycling genes in carbon and nitrogen cycling were assessed for their change in abundance due to wetting in the biocrusts. Different transcripts encoding the same gene product did not show a consensus response, with some more abundant in dry or wet biocrusts, highlighting the challenges in relating transcript abundance to biogeochemical cycling rates. These observations demonstrate that metatranscriptome sequencing was able to distinguish alterations in the function of arid soil microbial communities at two varying temporal scales, a long-term ecosystems disturbance through foot trampling, and a short term wetting pulse. 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We quantified the potential of natural climate solutions (NCS)—21 conservation, restoration, and improved land management interventions on natural and agricultural lands—to increase carbon storage and avoid greenhouse gas emissions in the United States. We found a maximum potential of 1.2 (0.9 to 1.6) Pg CO2e year−1, the equivalent of 21% of current net annual emissions of the United States. At current carbon market prices (USD 10 per Mg CO2e), 299 Tg CO2e year−1could be achieved. NCS would also provide air and water filtration, flood control, soil health, wildlife habitat, and climate resilience benefits.
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Herbivores alter plant biodiversity (species richness) in many of the world’s ecosystems, but the magnitude and the direction of herbivore effects on biodiversity vary widely within and among ecosystems. One current theory predicts that herbivores enhance plant biodiversity at high productivity but have the opposite effect at low productivity. Yet, empirical support for the importance of site productivity as a mediator of these herbivore impacts is equivocal. Here, we synthesize data from 252 large-herbivore exclusion studies, spanning a 20-fold range in site productivity, to test an alternative hypothesis—that herbivore-induced changes in the competitive environment determine the response of plant biodiversity to herbivory irrespective of productivity. Under this hypothesis, when herbivores reduce the abundance (biomass, cover) of dominant species (for example, because the dominant plant is palatable), additional resources become available to support new species, thereby increasing biodiversity. By contrast, if herbivores promote high dominance by increasing the abundance of herbivory-resistant, unpalatable species, then resource availability for other species decreases reducing biodiversity. We show that herbivore-induced change in dominance, independent of site productivity or precipitation (a proxy for productivity), is the best predictor of herbivore effects on biodiversity in grassland and savannah sites. Given that most herbaceous ecosystems are dominated by one or a few species, altering the competitive environment via herbivores or by other means may be an effective strategy for conserving biodiversity in grasslands and savannahs globally.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-018-0696-y","usgsCitation":"Koerner, S., Smith, M.D., Burkepile, D.E., Hanan, N.P., Avolio, M.L., Collins, S., Knapp, A., Lemoine, N.P., Forrestel, E.J., Eby, S., Thompson, D.I., Aguado-Santacruz, G.A., Anderson, J.P., Anderson, T.M., Angassa, A., Bagchi, S., Bakker, E.S., Bastin, G., Baur, L.E., Beard, K., Beever, E., Bohlen, P.J., Boughton, E.H., Canestro, D., Cesa, A., Chaneton, E., Cheng, J., D’Antonio, C.M., Deleglise, C., Dembele, F., Dorrough, J., Eldridge, D.J., Fernandez-Going, B., Fernandez-Lugo, S., Fraser, L.H., Freedman, B., Garcia-Salgado, G., Goheen, J.R., Guo, L., Husheer, S., Karembe, M., Knops, J.M., Kraaij, T., Kulmatiski, A., Kytoviita, M., Lezama, F., Loucougaray, G., Loydi, A., Milchunas, D.G., Milton, S.J., Morgan, J.W., Moxham, C., Nehring, K.C., Olff, H., Palmer, T.M., Rebollo, S., Riginos, C., Risch, A., Rueda, M., Sankaran, M., Sasaki, T., Schoenecker, K.A., Schultz, N.L., Schutz, M., Schwabe, A., Siebert, F., Smit, C., Stahlheber, K.A., Storm, C., Strong, D.J., Su, J., Tiruvaimozhi, Y.V., Tyler, C., Val, J., Vandegehuchte, M.L., Veblen, K.E., Vermeire, L., Ward, D., Wu, J., Young, T.P., Yu, Q., and Zelikova, T.J., 2018, Change in dominance determines herbivore effects on plant biodiversity: Nature Ecology & Evolution, v. 2, p. 1925-1932, https://doi.org/10.1038/s41559-018-0696-y.","productDescription":"8 p.","startPage":"1925","endPage":"1932","ipdsId":"IP-077293","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468263,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1038/s41559-018-0696-y","text":"External Repository"},{"id":359127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-29","publicationStatus":"PW","scienceBaseUri":"5bf67cf1e4b045bfcae2cfec","contributors":{"authors":[{"text":"Koerner, Sally E.","contributorId":208281,"corporation":false,"usgs":false,"family":"Koerner","given":"Sally E.","affiliations":[{"id":37771,"text":"Dept. of Integrative Biology, U. of S. 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L.","contributorId":210432,"corporation":false,"usgs":false,"family":"Vandegehuchte","given":"Martijn","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":750692,"contributorType":{"id":1,"text":"Authors"},"rank":75},{"text":"Veblen, Kari E.","contributorId":76872,"corporation":false,"usgs":false,"family":"Veblen","given":"Kari","email":"","middleInitial":"E.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":750693,"contributorType":{"id":1,"text":"Authors"},"rank":76},{"text":"Vermeire, Lance","contributorId":139806,"corporation":false,"usgs":false,"family":"Vermeire","given":"Lance","affiliations":[{"id":13278,"text":"USDA-ARS Fort Keogh LARRL. 243 Fort Keogh Road, Miles City, MT","active":true,"usgs":false}],"preferred":false,"id":750694,"contributorType":{"id":1,"text":"Authors"},"rank":77},{"text":"Ward, David","contributorId":140493,"corporation":false,"usgs":false,"family":"Ward","given":"David","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":750695,"contributorType":{"id":1,"text":"Authors"},"rank":78},{"text":"Wu, Jianshuang","contributorId":210433,"corporation":false,"usgs":false,"family":"Wu","given":"Jianshuang","email":"","affiliations":[],"preferred":false,"id":750696,"contributorType":{"id":1,"text":"Authors"},"rank":79},{"text":"Young, Truman P.","contributorId":210434,"corporation":false,"usgs":false,"family":"Young","given":"Truman","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":750697,"contributorType":{"id":1,"text":"Authors"},"rank":80},{"text":"Yu, Qiang","contributorId":104821,"corporation":false,"usgs":true,"family":"Yu","given":"Qiang","email":"","affiliations":[],"preferred":false,"id":750698,"contributorType":{"id":1,"text":"Authors"},"rank":81},{"text":"Zelikova, Tamara J.","contributorId":76615,"corporation":false,"usgs":true,"family":"Zelikova","given":"Tamara","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":750699,"contributorType":{"id":1,"text":"Authors"},"rank":82}]}} ,{"id":70200806,"text":"70200806 - 2018 - Bank‐derived material dominates fluvial sediment in a suburban Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2018-11-02T14:00:32","indexId":"70200806","displayToPublicDate":"2018-11-02T14:00:27","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Bank‐derived material dominates fluvial sediment in a suburban Chesapeake Bay watershed","docAbstract":"Excess fine sediment is a leading cause of ecological degradation within the Chesapeake Bay watershed. To effectively target sediment mitigation measures, it is necessary to identify and quantify the delivery of sediment sources to local waterbodies.
This study examines the contributions of sediment sources within Upper Difficult Run, a suburbanized watershed in Fairfax County, Virginia. A source sediment library was constructed from stream banks, forest soils, and road dust. Target sediments were collected from fine channel deposits and suspended sediment during 16 storm events from 2008 to 2012. Apportionment of targets to sources was performed using Sed_SAT, a publicly available toolkit for sediment fingerprinting.
Bed sediment was dominated by stream bank material (mean: 98%), with minor contributions from forests (2%). Suspended fine sediments were also dominated by stream banks (suspended sediment concentration‐weighted mean: 91%), with minor contributions from roads (8%) and forests (<1%). Stream banks dominated at all discharges, and on the rising limb and at peak flow, sediment concentrations increased due to bank material rather than surface erosion.
Sediment budget data indicated that direct bank erosion was insufficient to account for the suspended load derived from stream banks. However, bank‐derived sediment re‐mobilized from in‐channel storage could account for this difference and, combined, resulted in a sediment delivery ratio of 0.847 for all bank‐derived sediments.
Results demonstrate that stream bank erosion is responsible for the majority of fine sediment in this suburban watershed of the Chesapeake Bay drainage area. Thus, management actions to control upland sources of sediment may have limited effect on the sediment conditions of Upper Difficult Run, whereas efforts focusing on bank stabilization, channel restoration, and/or stormwater management to reduce bank erosion may improve the ecological condition of these waterbodies.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3325","usgsCitation":"Cashman, M.J., Gellis, A.C., Gorman Sanisaca, L.E., Noe, G.E., Cogliandro, V., and Baker, A., 2018, Bank‐derived material dominates fluvial sediment in a suburban Chesapeake Bay watershed: River Research and Applications, v. 34, no. 8, p. 1032-1044, https://doi.org/10.1002/rra.3325.","productDescription":"13 p.","startPage":"1032","endPage":"1044","ipdsId":"IP-087831","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":359118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Upper Difficult Run","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.325,\n 38.8417\n ],\n [\n -77.3667,\n 38.8417\n ],\n [\n -77.3667,\n 38.8917\n ],\n [\n -77.325,\n 38.8917\n ],\n [\n -77.325,\n 38.8417\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-25","publicationStatus":"PW","scienceBaseUri":"5c10a8fce4b034bf6a7e4eca","contributors":{"authors":[{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":750621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":750624,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cogliandro, Vanessa","contributorId":210383,"corporation":false,"usgs":false,"family":"Cogliandro","given":"Vanessa","email":"","affiliations":[{"id":38109,"text":"Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Feo di Vito, Reggio Calabria, Italy","active":true,"usgs":false}],"preferred":false,"id":750625,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, Anna 0000-0001-8194-7535 abaker@usgs.gov","orcid":"https://orcid.org/0000-0001-8194-7535","contributorId":210384,"corporation":false,"usgs":true,"family":"Baker","given":"Anna","email":"abaker@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750626,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227791,"text":"70227791 - 2018 - Time-varying predatory behavior is primary predictor of fine-scale movement of wildland-urban cougars","interactions":[],"lastModifiedDate":"2022-01-31T14:55:07.503174","indexId":"70227791","displayToPublicDate":"2018-11-02T08:40:21","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Time-varying predatory behavior is primary predictor of fine-scale movement of wildland-urban cougars","docAbstract":"Background
While many species have suffered from the detrimental impacts of increasing human population growth, some species, such as cougars (Puma concolor), have been observed using human-modified landscapes. However, human-modified habitat can be a source of both increased risk and increased food availability, particularly for large carnivores. Assessing preferential use of the landscape is important for managing wildlife and can be particularly useful in transitional habitats, such as at the wildland-urban interface. Preferential use is often evaluated using resource selection functions (RSFs), which are focused on quantifying habitat preference using either a temporally static framework or researcher-defined temporal delineations. Many applications of RSFs do not incorporate time-varying landscape availability or temporally-varying behavior, which may mask conflict and avoidance behavior.
Methods
Contemporary approaches to incorporate landscape availability into the assessment of habitat selection include spatio-temporal point process models, step selection functions, and continuous-time Markov chain (CTMC) models; in contrast with the other methods, the CTMC model allows for explicit inference on animal movement in continuous-time. We used a hierarchical version of the CTMC framework to model speed and directionality of fine-scale movement by a population of cougars inhabiting the Front Range of Colorado, U.S.A., an area exhibiting rapid population growth and increased recreational use, as a function of individual variation and time-varying responses to landscape covariates.
Results
We found evidence for individual- and daily temporal-variability in cougar response to landscape characteristics. Distance to nearest kill site emerged as the most important driver of movement at a population-level. We also detected seasonal differences in average response to elevation, heat loading, and distance to roads. Motility was also a function of amount of development, with cougars moving faster in developed areas than in undeveloped areas.
Conclusions
The time-varying framework allowed us to detect temporal variability that would be masked in a generalized linear model, and improved the within-sample predictive ability of the model. The high degree of individual variation suggests that, if agencies want to minimize human-wildlife conflict management options should be varied and flexible. However, due to the effect of recursive behavior on cougar movement, likely related to the location and timing of potential kill-sites, kill-site identification tools may be useful for identifying areas of potential conflict.
","language":"English","publisher":"BioMed Central","doi":"10.1186/s40462-018-0140-6","usgsCitation":"Buderman, F.E., Hooten, M., Alldredge, M.W., Hanks, E., and Ivan, J., 2018, Time-varying predatory behavior is primary predictor of fine-scale movement of wildland-urban cougars: Movement Ecology, v. 6, p. 1-16, https://doi.org/10.1186/s40462-018-0140-6.","productDescription":"22, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-092873","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468265,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-018-0140-6","text":"Publisher Index Page"},{"id":395133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Front Range, Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.0015869140625,\n 39.26203141523749\n ],\n [\n -104.78759765625,\n 39.26203141523749\n ],\n [\n -104.78759765625,\n 40.451127265872316\n ],\n [\n -106.0015869140625,\n 40.451127265872316\n ],\n [\n -106.0015869140625,\n 39.26203141523749\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2018-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Buderman, Frances E.","contributorId":171634,"corporation":false,"usgs":false,"family":"Buderman","given":"Frances","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":832263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":832264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alldredge, Mathew W.","contributorId":147536,"corporation":false,"usgs":false,"family":"Alldredge","given":"Mathew","email":"","middleInitial":"W.","affiliations":[{"id":16861,"text":"Colorado Parks and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":832265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Ephraim M.","contributorId":265331,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim M.","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":832266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ivan, Jacob S.","contributorId":200243,"corporation":false,"usgs":false,"family":"Ivan","given":"Jacob S.","affiliations":[],"preferred":false,"id":832267,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199518,"text":"70199518 - 2018 - Monitoring wadeable stream habitat conditions in Southeast Coast Network parks: Protocol narrative","interactions":[],"lastModifiedDate":"2018-11-16T17:24:25","indexId":"70199518","displayToPublicDate":"2018-11-01T17:24:16","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SECN/NRR—2018/1715 ","title":"Monitoring wadeable stream habitat conditions in Southeast Coast Network parks: Protocol narrative","docAbstract":"The Southeast Coast Network (SECN) has initiated a monitoring effort to assess habitat conditions in wadeable streams at national parks, recreation areas, battlefields, and monuments in Alabama, Georgia, and South Carolina. This monitoring effort includes Chattahoochee River National Recreation Area, Kennesaw Mountain National Battlefield Park, Congaree National Park, Horseshoe Bend National Military Park, and Ocmulgee National Monument.
Stream habitat monitoring was implemented in 2016, and focuses specifically on providing relevant data to assess the physical condition of Piedmont and upper Coastal Plain streams with respect to aquatic and riparian habitats and how these habitats may be changing over time. The habitat assessment methods proposed in this protocol rely on standard data collection methods and standard operating procedures currently in use by the U.S. Geological Survey, U.S. Environmental Protection Agency, and U.S. Forest Service that have been modified to better meet the needs of National Park Service (NPS) managers.
The Southeast Coast Network’s wadeable stream protocol was developed to begin a monitoring program that will provide insight into the status of, and trends in, stream and riparian habitat conditions. The number of reaches surveyed at each park is dependent on the spatial extent of the park and the total number of wadeable streams that are present within park boundaries. Regardless of the size of the park and the number of reaches that are to be monitored, selected reaches (1) are representative of the processes influencing the streams in each park; (2) can address current and anticipated management concerns, and (3) offer the most utility for future complementary studies.
","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"McDonald, J.M., Gregory, M., Riley, J.W., and Starkey, E.N., 2018, Monitoring wadeable stream habitat conditions in Southeast Coast Network parks: Protocol narrative: Natural Resource Report NPS/SECN/NRR—2018/1715 , xiii, 103 p.","productDescription":"xiii, 103 p.","ipdsId":"IP-066204","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":359534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":357534,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2254874"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.890625,\n 29.49698759653577\n ],\n [\n -75.34423828125,\n 29.49698759653577\n ],\n [\n -75.34423828125,\n 36.56260003738545\n ],\n [\n -87.890625,\n 36.56260003738545\n ],\n [\n -87.890625,\n 29.49698759653577\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5befe5bae4b045bfcadf7f2e","contributors":{"authors":[{"text":"McDonald, Jacob M.","contributorId":208029,"corporation":false,"usgs":false,"family":"McDonald","given":"Jacob","email":"","middleInitial":"M.","affiliations":[{"id":37679,"text":"National Park Service Southeast Coast Inventory and Monitoring Unit","active":true,"usgs":false}],"preferred":false,"id":745742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gregory, Mark B.","contributorId":151024,"corporation":false,"usgs":false,"family":"Gregory","given":"Mark B.","affiliations":[],"preferred":false,"id":745741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Riley, Jeffrey W. 0000-0001-5525-3134 jriley@usgs.gov","orcid":"https://orcid.org/0000-0001-5525-3134","contributorId":3605,"corporation":false,"usgs":true,"family":"Riley","given":"Jeffrey","email":"jriley@usgs.gov","middleInitial":"W.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":745740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starkey, Eric N.","contributorId":208030,"corporation":false,"usgs":false,"family":"Starkey","given":"Eric","email":"","middleInitial":"N.","affiliations":[{"id":37679,"text":"National Park Service Southeast Coast Inventory and Monitoring Unit","active":true,"usgs":false}],"preferred":false,"id":745743,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200790,"text":"70200790 - 2018 - California mallards: a review","interactions":[],"lastModifiedDate":"2018-11-01T17:14:43","indexId":"70200790","displayToPublicDate":"2018-11-01T17:14:40","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1153,"text":"California Fish and Game","active":true,"publicationSubtype":{"id":10}},"title":"California mallards: a review","docAbstract":"Mallards (Anas platyrhynchos) are the most abundant breeding waterfowl species in California and are important to waterfowl hunters in the state. California is unique among major North American wintering waterfowl areas, in that most mallards harvested in California are also produced in California, meaning that California must provide both high quality wintering and breeding habitats for mallard populations to remain stable. California’s breeding and wintering mallard population estimates have generally declined since the mid-1990s. Herein, we synthesized existing information on the ecology of breeding mallards in California and summarize key demographic rates. In general, demographic estimates differed substantially from other mallard populations in North America, highlighting the importance of separate management of western mallard populations. We suggest long-term research and monitoring activities to help improve management.
","language":"English","publisher":"California Department of Fish and Wildlife","usgsCitation":"Feldheim, C.L., Ackerman, J., Oldenburger, S.L., Eadie, J.M., Fleskes, J., and Yarris, G.S., 2018, California mallards: a review: California Fish and Game, v. 104, no. 2, p. 49-66.","productDescription":"18 p.","startPage":"49","endPage":"66","ipdsId":"IP-088524","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":359093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":359058,"type":{"id":15,"text":"Index Page"},"url":"https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=161088&inline"}],"volume":"104","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10a8fce4b034bf6a7e4ecc","contributors":{"authors":[{"text":"Feldheim, Cliff L.","contributorId":206561,"corporation":false,"usgs":false,"family":"Feldheim","given":"Cliff","email":"","middleInitial":"L.","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":750523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":750522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oldenburger, Shaun L.","contributorId":177598,"corporation":false,"usgs":false,"family":"Oldenburger","given":"Shaun","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":750524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eadie, John M.","contributorId":65219,"corporation":false,"usgs":false,"family":"Eadie","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":7082,"text":"University of California - Davis","active":true,"usgs":false}],"preferred":false,"id":750525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fleskes, Joseph P. 0000-0001-5388-6675","orcid":"https://orcid.org/0000-0001-5388-6675","contributorId":210345,"corporation":false,"usgs":false,"family":"Fleskes","given":"Joseph P.","affiliations":[],"preferred":false,"id":750526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yarris, Gregory S.","contributorId":210346,"corporation":false,"usgs":false,"family":"Yarris","given":"Gregory","email":"","middleInitial":"S.","affiliations":[{"id":24747,"text":"California Waterfowl Association","active":true,"usgs":false}],"preferred":false,"id":750527,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199846,"text":"70199846 - 2018 - Increasing soil organic carbon to mitigate greenhouse gases and increase climate resiliency for California","interactions":[],"lastModifiedDate":"2018-11-16T17:07:34","indexId":"70199846","displayToPublicDate":"2018-11-01T17:07:31","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesNumber":"CCCA4-CNRA-2018-006","title":"Increasing soil organic carbon to mitigate greenhouse gases and increase climate resiliency for California","docAbstract":"Rising air temperatures are projected to continue to drive up urban, agricultural, and rangeland water use, straining both surface and groundwater resources. Scientific studies have shown that managing farms, ranches, and public lands to increase soil carbon can increase soil waterholding capacity and increase hydrologic benefits such as increased baseflows and aquifer recharge, reduced flooding and erosion, and reduced climate-related water deficits. Coincident improvements in forage and crop yields are also indicated, while simultaneously sequestering carbon, reducing atmospheric greenhouse gases and mitigating climate change. This study was developed to consider the multiple benefits of increasing the organic matter content of soils across California’s working lands.
Study results indicate that a one-time ¼” application of compost to rangelands can lead to carbon sequestration rates in soils that are maximized after approximately 15 years, and more than offset greenhouse gas emissions stimulated by the compost addition for at least five decades longer. Modeled increases in total soil organic matter of 3% enhanced hydrologic benefits across 97% of working lands, and reduced climate change impacts. Economic valuation indicated all benefits increasing over time, demonstrating a large potential for the California carbon market to support incentives in regionalizing the impacts in the coming decades. Socioeconomic and related land use pressures pose barriers to implementing management practices to increase soil organic matter by driving conversion of rangeland to urban or to more greenhouse-gas emission intensive agriculture. Results can be effectively used with land use change scenarios to identify where on California’s working lands hydrologic benefits of soil organic matter enhancement coincide with development risk, highlighting counties in California in which there may be resilience to climate change when strategic soil management and land conservation are combined.
","language":"English","publisher":"California Natural Resources Agency","usgsCitation":"Flint, L.E., Flint, A.L., Stern, M.A., Mayer, A., Silver, W.L., Casey, C., Franco, F., Byrd, K.B., Sleeter, B.M., Alvarez, P., Creque, J., Estrada, T., and Cameron, D., 2018, Increasing soil organic carbon to mitigate greenhouse gases and increase climate resiliency for California, 113 p.","productDescription":"113 p.","ipdsId":"IP-094187","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":359532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":357952,"type":{"id":11,"text":"Document"},"url":"https://www.climateassessment.ca.gov/techreports/docs/20180827-Agriculture_CCCA4-CNRA-2018-006.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United 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EHMA Water and Wastewater Infrastructure research specifically provides insight into natural factors in the environment as well as those water-infrastructure components and processes (such as source-water corrosivity, treatment, plumbing, and so forth) that might influence human exposure to chemical and microbial contaminants at the residential tap. This infrastructure-exposure research role is fulfilled uniquely by the USGS and not by the U.S. Environmental Protection Agency (EPA), other agencies, or municipalities that focus on regulatory and policy activities and related compliance. The USGS approach to assessing the possible links between human health and chemical contaminant and pathogen exposure in drinking water is conducted in collaboration with public health experts and includes comprehensive characterization of the presence/absence and concentrations of more than 500 organic and 27 inorganic chemical constituents at the point of use (tap).
Laboratory results for lead and other inorganic contaminants in Chicago, Illinois, and East Chicago, Indiana, residential tapwater are being released to ensure the timely release of quality-assured data to participants in the study. Concentrations of lead and other inorganic constituents were assessed in drinking water at the point of use (kitchen tap or filter) in 45 residential locations and in two locations within each of the two Chicago water purification plants and the two East Chicago water filtration plants during July–December 2017. Three methods were used for analyzing lead. The most sensitive method had a reporting limit of 0.020 micrograms per liter (µg/L). When using the most sensitive analytical method, lead was detected in 39 of 45 residential tapwater samples, with concentrations ranging from less than 0.020 µg/L to 5.31 µg/L (median of the detected values = 0.481 µg/L). Concentrations of lead also were detected in Lake Michigan intake water at all water purification/filtration plant facilities at concentrations ranging from 0.083 to 0.330 µg/L, but were not detected above the reporting limit in any samples of treated, pre-distribution drinking water at any of the water purification/filtration plant facilities.
Because the USGS Water and Wastewater Infrastructure project in the Greater Chicago Area is focused on the potential human exposure to a broad suite of organic and inorganic contaminants in drinking water and is not focused specifically on lead, the sampling protocol did not include “first-draw,” stagnant sampling and samples were collected with point-of-use treatment in place, if present. Thus, the lead results reported herein are not appropriate for assessment of compliance with the EPA 1991 Lead and Copper Rule. Information resources for lead mitigation and water filtration are provided.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181071","collaboration":"Prepared in cooperation with the City of Chicago, Department of Water Management; City of East Chicago, Utilities Department; Indiana Department of Environmental Management, Drinking Water Branch; National Institutes of Health/National Institute of Environmental Health Sciences (NIH/NIEHS); University of Illinois at Chicago, School of Public Health","usgsCitation":"Romanok, K.M., Kolpin, D.W., Meppelink, S.M., Focazio, M.J., Argos, M., Hollingsworth, M.E., McCleskey, R.B., Putz, A.R., Stark, A., Weis, C.P., Zehraoui, A., and Bradley, P.M., 2018, Concentrations of lead and other inorganic constituents in samples of raw intake and treated drinking water from the municipal water filtration plant and residential tapwater in Chicago, Illinois, and East Chicago, Indiana, July–December 2017: U.S. Geological Survey Open-File Report 2018–1071, 10 p., https://doi.org/10.3133/ofr20181071.","productDescription":"Report: iv, 10 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094493","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":358915,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181098","text":"Open-File Report 2018–1098","linkHelpText":"- Methods Used for the Collection and Analysis of Chemical and Biological Data for the Tapwater Exposure Study, United States, 2016–17"},{"id":358912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1071/coverthb.jpg"},{"id":358914,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/F70R9NN0","text":"USGS data release ","description":"USGS data release ","linkHelpText":"Occurrence and Concentrations of Trace Elements in Discrete Tapwater Samples Collected in Chicago, Illinois and East Chicago, Indiana, 2017"},{"id":358913,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1071/ofr20181071.pdf","text":"Report","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1071"}],"country":"United States","state":"Illinois, Indiana","city":"Chicago, East Chicago","contact":"Director, South Atlantic Water Science Center
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720 Gracern Road
Columbia, SC 29210
In 2016, the U.S. Geological Survey (USGS) Environmental Health Mission Area, initiated the Tapwater Exposure Study as part of an infrastructure project to assess human exposure to potential threats from complex mixtures of contaminants. In the pilot phase (2016), samples were collected from 11 States throughout the United States, and in the second phase (2017), the study focused on the Greater Chicago area, including North and South Chicago, Illinois, and East Chicago, Indiana. Residential tapwater samples were collected at private residences during both phases, and during the first phase, samples were collected from Federal office buildings and from one office 19-liter water-bottle source. During the second phase, raw intake and treated (pre-distributional) water samples also were collected from four drinking-water treatment facilities in the Greater Chicago area. Samples were sent to laboratories at the USGS, U.S. Environmental Protection Agency, National Institute of Environmental Health Sciences, and Colorado School of Mines Center for Environmental Risk Assessment, for potential drinking-water pathogens, chemical, and bioassay analyses. These analyses included more than 400 chemicals such as trace elements, steroid hormones, pharmaceuticals, volatile organic compounds, pesticides, per- and polyfluorinated alkyl substances, cyanotoxins, and other organic compounds. The in vitro bioassay analyses included estrogen, androgen, and glucocorticoid receptor activity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181098","collaboration":"Prepared in cooperation with the Colorado School of Mines, Center for Environmental Risk Assessment; National Institutes of Health/National Institute of Environmental Health Sciences (NIH/NIEHS), National Toxicology Program Laboratory; University of Illinois at Chicago, School of Public Health; U.S. Environmental Protection Agency, National Exposure Research Laboratory; U.S. Environmental Protection Agency, National Health and Environmental Effects Laboratory","usgsCitation":"Romanok, K.M., Kolpin, D.W., Meppelink, S.M., Argos, M., Brown, J.B., DeVito, M.J., Dietze, J.E., Givens, C.E., Gray, J.L., Higgins, C.P., Hladik, M.L., Iwanowicz, L.R., Loftin, K.A., McCleskey, R.B., McDonough, C.A., Meyer, M.T., Strynar, M.J., Weis, C.P., Wilson, V.S., and Bradley, P.M., 2018, Methods used for the collection and analysis of chemical and biological data for the Tapwater Exposure Study, United States, 2016–17: U.S. Geological Survey Open-File Report 2018–1098, 79 p., https://doi.org/10.3133/ofr20181098.","productDescription":"Report: ix, 79 p.; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094498","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":358920,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1098/coverthb.jpg"},{"id":358921,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1098/ofr20181098.pdf","text":"Report","size":"1.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1098"},{"id":358923,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181071","text":"Open-File 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U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210