Bayesian statistical analyses of paleoseismic data result in the probabilistic determination of earthquake times using geochronological data evaluated in the context of a stratigraphic model. However, a fundamental problem in paleoseismology is how to use the Bayesian approach to model sparse and/or conflicting geochronological datasets, such as those derived from sites exhibiting episodic sedimentary and pedogenic processes in moderate‐ to high‐energy environments (e.g., a normal‐faulted alluvial fan). Using paleoseismic data for the Corner Canyon site on the Salt Lake City segment of the Wasatch fault zone (Utah), we develop an approach by which multiple Bayesian models are combined to generate an earthquake history at a site. This approach accommodates mutually exclusive interpretations of the geochronological data and thereby limits the influence of sparse data, stratigraphically inconsistent ages, or a single, subjective model interpretation. For the Corner Canyon site, we integrate four OxCal Bayesian models to generate a chronology of six events between
Local topographically driven processes – such as wind drifting, avalanching, and shading – are known to alter the relationship between the mass balance of small cirque glaciers and regional climate. Yet partitioning such local effects from regional climate influence has proven difficult, creating uncertainty in the climate representativeness of some glaciers. We address this problem for Sperry Glacier in Glacier National Park, USA, using field-measured surface mass balance, geodetic constraints on mass balance, and regional climate data recorded at a network of meteorological and snow stations. Geodetically derived mass changes during 1950–1960, 1960–2005, and 2005–2014 document average mass change rates during each period at −0.22 ± 0.12, −0.18 ± 0.05, and −0.10 ± 0.03 m w.e. yr−1, respectively. A correlation of field-measured mass balance and regional climate variables closely (i.e., within 0.08 m w.e. yr−1) predicts the geodetically measured mass loss from 2005 to 2014. However, this correlation overestimates glacier mass balance for 1950–1960 by +1.20 ± 0.95 m w.e. yr−1. Our analysis suggests that local effects, not represented in regional climate variables, have become a more dominant driver of the net mass balance as the glacier lost 0.50 km2 and retreated further into its cirque.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-12-2109-2018","usgsCitation":"Florentine, C., Harper, J.T., Fagre, D.B., Moore, J., and Peitzsch, E.H., 2018, Local topography increasingly influences the mass balance of a retreating cirque glacier: The Cryosphere, v. 12, p. 2109-2122, https://doi.org/10.5194/tc-12-2109-2018.","productDescription":"14 p.","startPage":"2109","endPage":"2122","ipdsId":"IP-094543","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468631,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-12-2109-2018","text":"Publisher Index Page"},{"id":355348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.67,\n 48.1667\n ],\n [\n -113.33,\n 48.1667\n ],\n [\n -113.33,\n 48.9167\n ],\n [\n -114.67,\n 48.9167\n ],\n [\n -114.67,\n 48.1667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-20","publicationStatus":"PW","scienceBaseUri":"5b46e54ee4b060350a15d0bd","contributors":{"authors":[{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":738982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harper, Joel T.","contributorId":173392,"corporation":false,"usgs":false,"family":"Harper","given":"Joel","email":"","middleInitial":"T.","affiliations":[{"id":16951,"text":"Department of Geosciences, University of Montana, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":738983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461 dan_fagre@usgs.gov","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":2036,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel","email":"dan_fagre@usgs.gov","middleInitial":"B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":738984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Johnnie","contributorId":205965,"corporation":false,"usgs":false,"family":"Moore","given":"Johnnie","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":738985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":738986,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196224,"text":"sir20185049 - 2018 - Estimates of water use and trends in the Colorado River Basin, Southwestern United States, 1985–2010","interactions":[],"lastModifiedDate":"2018-06-27T08:36:36","indexId":"sir20185049","displayToPublicDate":"2018-06-26T00:00:00","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":"2018-5049","title":"Estimates of water use and trends in the Colorado River Basin, Southwestern United States, 1985–2010","docAbstract":"The Colorado River Basin (CRB) drains 246,000 square miles and includes parts of California, Colorado, Nevada, New Mexico, Utah, and Wyoming, and all of Arizona (Basin States). This report contains water-use estimates by category of use for drainage basins (Hydrologic Unit Code 8; HUC‑8) within the CRB from 1985 to 2010, at 5-year intervals. Estimates for public supply, domestic, commercial, industrial, irrigation, livestock, mining, aquaculture, hydroelectric and thermoelectric power, and wastewater returns are tabulated as (1) water withdrawals from groundwater or surface‑water sources of fresh or saline quality, (2) water delivered for domestic use, (3) wastewater returns and instream use (hydroelectric), and (4) consumptive use, or water that is consumed (USGS definition) and not available for immediate reuse. Water transported outside of the CRB (interbasin transfers) is not included as part of withdrawals and are not accounted for in any category of use within the CRB.
Total withdrawals in the CRB (excluding interbasin transfers) averaged about 17 million acre-feet (maf) from 1985 to 2010, peaked at about 17.76 maf in 2000, and reached their lowest levels of 16.43 maf in 1990. Interbasin transfers to serve mostly public-supply and irrigation needs outside of the CRB are reported for 2000, 2005, and 2010 only, and averaged 5.40 maf. More surface water was used in the CRB than groundwater, averaging about 78 percent of total withdrawals, and its use increased less than 2 percent from 1985 to 2010, while groundwater withdrawals decreased about 12 percent. From 1985 to 2010, surface water averaged 98 percent of withdrawals in the upper CRB, and about 59 percent in the lower CRB. Nearly all withdrawals were freshwater, but some saline groundwater was used for mining and self-supplied industrial.
Interbasin transfers have a large effect on flows in the Colorado River and are listed in this report separately with no explanation of how the water is used outside of the CRB. There are 34 interbasin transfers that conveyed an estimated 5.83, 5.20, and 5.18 maf out of the CRB in 2000, 2005, and 2010, respectively. The largest interbasin transfers are in the lower CRB and convey surface water (Colorado River water) to southern California; these accounted for 80 to 84 percent of total interbasin transfers in the CRB from 2000 to 2010. Intrabasin transfers are conveyances of surface water that cross drainage basin or State boundaries in the CRB, but the water does not leave the CRB. There are many intrabasin transfers in the CRB, but this report lists 11 that are mostly in the State of Colorado. The largest is the Central Arizona Project (CAP), through which more than 1.00 maf of water was provided to irrigate nearly 1 million acres in Maricopa, Pinal, and Pima Counties, as well as provide municipal water for Phoenix and Tucson, Arizona, during 2000, 2005, and 2010. In 2010, interbasin and intrabasin transfers accounted for 24 and 11 percent of the total water withdrawals in CRB, respectively, with the larger volumes being conveyed out of the lower CRB.
Total population in the CRB increased from 4.56 to 9.44 million people from 1985 to 2010. Most of those people were in the lower CRB, with 86 percent of the total in 1985, and 90 percent of the total in 2010. Total public-supply withdrawals in the CRB provided most people with their potable water, and averaged about 1.63 maf from 1985 to 2010, ranging from about 1.07 maf in 1985 to about 2.10 maf in 2010, when it peaked. Most of public-supply withdrawals occurred in the lower CRB, ranging from 87 to 91 percent of total public-supply withdrawals in the CRB over the 25 years. Total domestic use, comprised of public-supply deliveries and self-supply domestic withdrawals, increased more than 90 percent from 1985 to 2010, from about 0.80 maf to about 1.54 maf. Domestic daily per-capita use rates in the CRB ranged from about 144 (1985) to about 121 (2000) gallons (gal) per‑capita between 1985 and 2010. When comparing domestic daily per-capita rates for the upper and lower CRB, people in the lower CRB, on average, used less water for domestic purposes at 128 gal per-capita daily (1985–2010), while those in the upper CRB for the same time period averaged 133 gal per-capita daily. The trend in daily per-capita use rates for the entire CRB fluctuated between the reporting years, but decreased overall, indicating that more people used less water in 2010 than in 1985, likely due to improved infrastructure, conservation, and improvements to water using appliances in homes and businesses.
Irrigation accounted for most total withdrawals in the CRB, excluding instream use for hydroelectric power and interbasin transfers, averaging 85 percent from 1985 to 2010. Far more surface water than groundwater was used for irrigation in both the upper and lower CRB, but in the upper CRB, it accounted for an average of more than 98 percent of the total withdrawals (1985–2010), whereas in the lower CRB, surface-water withdrawals for irrigation averaged 61 percent of total withdrawals. On average, the upper CRB accounted for 56 percent of total irrigated acres, and the irrigation systems in the upper CRB trended towards more efficient sprinkler systems from 1985 to 2010. Long-term drought in the CRB substantially decreased the amount of streamflow available for irrigation. Increases in micro-irrigation acres, which can have efficiencies that exceed 90 percent and require 20–50 percent less water than sprinkler systems, likely contributed to reduced withdrawals in the lower CRB.
For thermoelectric power, total withdrawals, including the use of reclaimed wastewater, were greater in the upper CRB from 1985 through 2005. In 2010, the lower CRB exceeded the upper by only 11,000 acre-feet. On average, thermoelectric consumptive use accounted for about 80 percent of the total withdrawals; however, consumptive-use data in the upper CRB was incomplete. Surface water was the primary source in the upper CRB and groundwater was the primary source in the lower CRB. In the CRB overall, water withdrawals for thermoelectric generation has decreased since 2000, except for groundwater withdrawals in the lower CRB. Power generation at thermoelectric plants was greater in the upper CRB from 1985 to 2000, and after 2005 the difference in power generation was small; however, the upper CRB continued to have more power generation. In both the upper and lower CRB, power generation increased from 1985 to 2005.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185049","usgsCitation":"Maupin, M.A., Ivahnenko, T., and Bruce, B., 2018, Estimates of water use and trends in the Colorado River Basin, Southwestern United States, 1985–2010: U.S. Geological Survey Scientific Investigations Report 2018–5049, 61 p., https://doi.org/10.3133/sir20185049.","productDescription":"Report: ix, 61 p.; Data release","numberOfPages":"75","onlineOnly":"Y","ipdsId":"IP-074683","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":354013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5049/sir20185049.pdf","text":"Report","size":"18.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5049"},{"id":354012,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5049/coverthb.jpg"},{"id":355338,"rank":3,"type":{"id":30,"text":"Data 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U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
In Switzerland, nearly all historical Mw ~ 6 earthquakes have induced damaging landslides, rockslides and snow avalanches that, in some cases, also resulted in damage to infrastructure and loss of lives. We describe the customisation to Swiss conditions of a globally calibrated statistical approach originally developed to rapidly assess earthquake-induced landslide likelihoods worldwide. The probability of occurrence of such earthquake-induced effects is modelled through a set of geospatial susceptibility proxies and peak ground acceleration. The predictive model is tuned to capture the observations from past events and optimised for near-real-time estimates based on USGS-style ShakeMaps routinely produced by the Swiss Seismological Service. Our emphasis is on the use of high-resolution geospatial datasets along with additional local information on ground failure susceptibility. Even if calibrated on historic events with moderate magnitudes, the methodology presented in this paper yields sensible results also for low-magnitude recent events. The model is integrated in the Swiss ShakeMap framework. This study has a high practical relevance to many Swiss ShakeMap stakeholders, especially those managing lifeline systems, and to other global users interested in conducting a similar customisation for their region of interest.
","language":"English","publisher":"Springer","doi":"10.1007/s11069-018-3248-5","usgsCitation":"Cauzzi, C., Fah, D., Wald, D.J., Clinton, J., Losey, S., and Wiemer, S., 2018, ShakeMap-based prediction of earthquake-induced mass movements in Switzerland calibrated on historical observations: Natural Hazards, v. 92, no. 2, p. 1211-1235, https://doi.org/10.1007/s11069-018-3248-5.","productDescription":"25 p.","startPage":"1211","endPage":"1235","ipdsId":"IP-095789","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468635,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/20.500.11850/250020","text":"External Repository"},{"id":355312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Switzerland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 6,\n 45.75\n ],\n [\n 10.5,\n 45.75\n ],\n [\n 10.5,\n 47.75\n ],\n [\n 6,\n 47.75\n ],\n [\n 6,\n 45.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"92","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","scienceBaseUri":"5b46e552e4b060350a15d0d1","contributors":{"authors":[{"text":"Cauzzi, Carlo","contributorId":205898,"corporation":false,"usgs":false,"family":"Cauzzi","given":"Carlo","email":"","affiliations":[{"id":37189,"text":"Swiss Seismological Service at ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":738801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fah, Donat","contributorId":205899,"corporation":false,"usgs":false,"family":"Fah","given":"Donat","email":"","affiliations":[],"preferred":false,"id":738802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clinton, John","contributorId":205900,"corporation":false,"usgs":false,"family":"Clinton","given":"John","affiliations":[],"preferred":false,"id":738804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Losey, Stephane","contributorId":205901,"corporation":false,"usgs":false,"family":"Losey","given":"Stephane","email":"","affiliations":[],"preferred":false,"id":738805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wiemer, Stefan","contributorId":205902,"corporation":false,"usgs":false,"family":"Wiemer","given":"Stefan","email":"","affiliations":[],"preferred":false,"id":738806,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197822,"text":"70197822 - 2018 - Stability of mercury concentration measurements in archived soil and peat samples","interactions":[],"lastModifiedDate":"2018-06-21T09:41:40","indexId":"70197822","displayToPublicDate":"2018-06-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1226,"text":"Chemosphere","active":true,"publicationSubtype":{"id":10}},"title":"Stability of mercury concentration measurements in archived soil and peat samples","docAbstract":"Archived soil samples can provide important information on the history of environmental contamination and by comparison with recently collected samples, temporal trends can be inferred. Little previous work has addressed whether mercury (Hg) concentrations in soil samples are stable with long-term storage under standard laboratory conditions. In this study, we have re-analyzed using cold vapor atomic adsorption spectroscopy a set of archived soil samples that ranged from relatively pristine mountainous sites to a polluted site near a non-ferrous metal smelter with a wide range of Hg concentrations (6 - 6485 µg kg-1). Samples included organic and mineral soils and peats with a carbon content that ranged from 0.2 to 47.7%. Soil samples were stored in polyethylene bags or bottles and held in laboratory rooms where temperature was not kept to a constant value. Mercury concentrations in four subsets of samples were originally measured in 2000, 2005, 2006 and 2007, and re-analyzed in 2017, i.e. after 17, 12, 11 and 10 years of storage. Statistical analyses of either separated or lumped data yielded no significant differences between the original and current Hg concentrations. Based on these analyses, we show that archived soil and peat samples can be used to evaluate historical soil mercury contamination.","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemosphere.2018.06.033","usgsCitation":"Navratil, T., Burns, D., Novakova, T., Kana, J., Rohovec, J., Roll, M., and Ettler, V., 2018, Stability of mercury concentration measurements in archived soil and peat samples: Chemosphere, v. 208, p. 707-711, https://doi.org/10.1016/j.chemosphere.2018.06.033.","productDescription":"4 p.","startPage":"707","endPage":"711","ipdsId":"IP-092247","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":355241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Czech Republic","volume":"208","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e554e4b060350a15d0dd","contributors":{"authors":[{"text":"Navratil, Tomas","contributorId":205848,"corporation":false,"usgs":false,"family":"Navratil","given":"Tomas","email":"","affiliations":[{"id":37176,"text":"Institute of Geology of the Czech Academy of Science","active":true,"usgs":false}],"preferred":false,"id":738664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":738663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Novakova, Tereza","contributorId":205849,"corporation":false,"usgs":false,"family":"Novakova","given":"Tereza","email":"","affiliations":[{"id":37176,"text":"Institute of Geology of the Czech Academy of Science","active":true,"usgs":false}],"preferred":false,"id":738665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kana, Jiri","contributorId":205850,"corporation":false,"usgs":false,"family":"Kana","given":"Jiri","email":"","affiliations":[{"id":37177,"text":"Biology Centre of the Czech Academy of Science","active":true,"usgs":false}],"preferred":false,"id":738666,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohovec, Jan","contributorId":205851,"corporation":false,"usgs":false,"family":"Rohovec","given":"Jan","email":"","affiliations":[{"id":37176,"text":"Institute of Geology of the Czech Academy of Science","active":true,"usgs":false}],"preferred":false,"id":738667,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roll, Michal","contributorId":205852,"corporation":false,"usgs":false,"family":"Roll","given":"Michal","email":"","affiliations":[{"id":37176,"text":"Institute of Geology of the Czech Academy of Science","active":true,"usgs":false}],"preferred":false,"id":738668,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ettler, Vojtech","contributorId":205853,"corporation":false,"usgs":false,"family":"Ettler","given":"Vojtech","email":"","affiliations":[{"id":37178,"text":"Charles University","active":true,"usgs":false}],"preferred":false,"id":738669,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70197821,"text":"70197821 - 2018 - Use of Bank Swallow (Riparia riparia) burrows as shelter by Common Tern (Sterna hirundo) chicks","interactions":[],"lastModifiedDate":"2018-06-21T09:19:49","indexId":"70197821","displayToPublicDate":"2018-06-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Use of Bank Swallow (Riparia riparia) burrows as shelter by Common Tern (Sterna hirundo) chicks","title":"Use of Bank Swallow (Riparia riparia) burrows as shelter by Common Tern (Sterna hirundo) chicks","docAbstract":"The availability of shelter to avoid predation and ameliorate physiologically stressful conditions is often important to the survival of avian hatchlings. However, as changes in habitat availability force birds to nest in nontraditional locations, young must quickly adapt to using novel sources of shelter. Two Common Tern (Sterna hirundo) colonies (one vegetated and one barren) were observed during the 2017 breeding season on a remote island habitat restoration project during data collection for a larger associated study. While chicks within the vegetated colony sought shade under vegetation, those in the barren colony were frequently found under anthropogenically constructed chick shelters. The first reported instance of Common Tern chicks using Bank Swallow (Riparia riparia) burrows for shelter was also observed in the barren colony. This behavior, when paired with other similar reports, suggests that this species is able to recognize beneficial shelters, both natural and anthropogenic, and use them at a young age, an important ability if they are to successfully reproduce in atypical habitats","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.041.0210","usgsCitation":"McGowan, P.C., Reintsma, K., Sullivan, J.D., DeVoss, K.P., Wall, J.L., Zimnik, M.D., Callahan, C.R., Schultz, B., and Prosser, D.J., 2018, Use of Bank Swallow (Riparia riparia) burrows as shelter by Common Tern (Sterna hirundo) chicks: Waterbirds, v. 41, no. 2, p. 179-182, https://doi.org/10.1675/063.041.0210.","productDescription":"4 p.","startPage":"179","endPage":"182","ipdsId":"IP-089905","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":355240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Poplar Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.41883850097656,\n 38.73212548425921\n ],\n [\n -76.34193420410156,\n 38.73212548425921\n ],\n [\n -76.34193420410156,\n 38.79690830348427\n ],\n [\n -76.41883850097656,\n 38.79690830348427\n ],\n [\n -76.41883850097656,\n 38.73212548425921\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e554e4b060350a15d0df","contributors":{"authors":[{"text":"McGowan, Peter C.","contributorId":13867,"corporation":false,"usgs":false,"family":"McGowan","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":738656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reintsma, Kaitlyn","contributorId":205843,"corporation":false,"usgs":true,"family":"Reintsma","given":"Kaitlyn","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":738655,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Jeffery D.","contributorId":202910,"corporation":false,"usgs":false,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":738657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeVoss, Katie P.","contributorId":205844,"corporation":false,"usgs":false,"family":"DeVoss","given":"Katie","email":"","middleInitial":"P.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":738658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wall, Jennifer L.","contributorId":205845,"corporation":false,"usgs":false,"family":"Wall","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":738659,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zimnik, Mia D.","contributorId":205846,"corporation":false,"usgs":false,"family":"Zimnik","given":"Mia","email":"","middleInitial":"D.","affiliations":[{"id":37175,"text":"Hood College","active":true,"usgs":false}],"preferred":false,"id":738660,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Callahan, Carl R.","contributorId":205289,"corporation":false,"usgs":false,"family":"Callahan","given":"Carl","email":"","middleInitial":"R.","affiliations":[{"id":37073,"text":"USFWS, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":738661,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schultz, Bill","contributorId":205847,"corporation":false,"usgs":false,"family":"Schultz","given":"Bill","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":738662,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":738654,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70196576,"text":"sir20185058 - 2018 - Shoreline erosion at selected areas along Lake Sharpe on the Lower Brule Reservation in South Dakota, 1966–2015","interactions":[],"lastModifiedDate":"2018-06-22T10:07:16","indexId":"sir20185058","displayToPublicDate":"2018-06-21T00:00:00","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":"2018-5058","title":"Shoreline erosion at selected areas along Lake Sharpe on the Lower Brule Reservation in South Dakota, 1966–2015","docAbstract":"The Lower Brule Reservation in central South Dakota is losing land because of shoreline erosion along Lake Sharpe, a reservoir on the Missouri River, which has caused detrimental effects for the Lower Brule Sioux Tribe including losses of cultural sites, recreation access points, wildlife habitat, irrigated cropland, and landmass. To better understand and quantify shoreline erosion, the Lower Brule Sioux Tribe and the U.S. Geological Survey cooperated on a series of data-collection efforts and study of shoreline erosion along Lake Sharpe. Data collected or compiled for 1966–2015 were used to describe and quantify shoreline erosion along Lake Sharpe. The progression of shoreline erosion near the community of Lower Brule, South Dakota, was tracked by comparing current or recent aerial imagery with existing historical maps. At 33 evaluation lines along a 7-mile reach of Lake Sharpe shoreline near Lower Brule, cumulative change of shoreline from 1966 to 2010 ranged from about −224 feet of deposition to 770 feet of erosion.
Photographic and location data were collected for this study to understand the processes affecting erosion and estimate erosion rates. Photographs were collected only in the 7-mile reach near Lower Brule, but locations of the bank over time were collected at the 7-mile reach and two additional reaches within the Lower Brule Reservation. Global navigation satellite system equipment was used in real-time kinematic mode to collect bank locations along three reaches of interest. Reach-length data were collected four times between November 2011 and November 2012. A small, unmanned aerial system (drone) was used to capture digital video along the shoreline of the 7-mile reach.
Water-level fluctuations contribute to the number of wet-dry cycles experienced by the soils at the shoreline or bank. The soils present under the current (2017) location of the reservoir are predominantly terrace alluvium, consisting of sand and silt. Detailed soils data for Lyman County indicate that the dominant soil type along the southern part of the shoreline in the 7-mile reach is Bullcreek clay. Weather within the study area can affect the erosion rate. Air temperature can potentially affect erosion rates by freezing and thawing water and soils. Mean hourly wind speeds vary somewhat throughout the year but averaged 13.3 miles per hour. The direction of prevailing winds near Lower Brule indicates that there are several miles of fetch to build large waves.
Annual erosion rates calculated or measured throughout this study varied by location. Long-term annual average erosion rates of the 7-mile reach, as calculated by image analysis, ranged from −5.1 feet per year (deposition) to 17.5 feet per year (erosion). Short-term annual erosion rates measured using global navigation satellite system equipment during 2010–12 ranged from about 0 to 31.7 feet per year for the 7-mile reach. Existing scour countermeasures have been effective variably. Fieldstone rip-rap seems to have stabilized the shoreline, whereas tree strips paralleling the shoreline seem to have slowed erosion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185058","collaboration":"Prepared in cooperation with the Lower Brule Sioux Tribe","usgsCitation":"Thompson, R.F., and Stamm, J.F., 2018, Shoreline erosion at selected areas along Lake Sharpe on the Lower Brule Reservation, South Dakota, 1966–2015: U.S. Geological Survey Scientific Investigations Report 2018–5058, 29 p., https://doi.org/10.3133/sir20185058.","productDescription":"Report: vi, 29 p.; Plate: 33.11 x 46.81 inches; Data Release","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080051","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":355250,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2018/5058/sir20185058_plate01.pdf","text":"Plate 1 -","size":"7.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5058 Plate","linkHelpText":"Soil Type and Land Cover Interactions with Erosion Measurements near Lower Brule, South Dakota"},{"id":355251,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H130XV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data to document shoreline erosion at selected locations along Lake Sharpe on the Lower Brule Reservation in South Dakota, 1966–2015"},{"id":355249,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5058/sir20185058.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5058"},{"id":355248,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5058/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Lower Brule","otherGeospatial":"Lake Sharpe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.1678466796875,\n 43.81471121600004\n ],\n [\n -99.16259765625,\n 43.81471121600004\n ],\n [\n -99.16259765625,\n 44.38669150215206\n ],\n [\n -100.1678466796875,\n 44.38669150215206\n ],\n [\n -100.1678466796875,\n 43.81471121600004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
South Dakota Office
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
Airborne viruses and their relation to dust storms, as a possible route for dispersion, have not been widely investigated. There are, however, studies that have described the airborne dispersal of pathogenic viruses and their potential impact on public and agronomical health. Atmospheric samples were collected in an urban area of Tenerife during 2009, 2010, 2012 and 2013 and screened for the presence of enteric viruses using PCR and sequencing. The potential relationship of viral data with African dust storms and other climatic variables (viz., the seasonality, origin of the air mass and PM levels) was analyzed. Enteroviruses and Rotaviruses were detected in 15.4% (20/130) and 36.9% (48/130) of the samples, respectively. No significant statistical relationships were observed with African dust storms or the origin of the air masses, although higher percentages of positives were obtained for dust storm days. Enterovirus detection was significantly linked to warmer seasons, and PM2.5 levels showed an inverse correlation with a rotaviral presence. This is the first multi-year report to describe the presence of Enterovirus and Rotavirus genetic sequences in air samples collected in an outdoor urban environment. The data illustrates the need for source region sampling to determine links and the influence of the weather and climatic and regional wind patterns on long-range atmospheric dispersion of viruses in future research efforts.
","language":"English","publisher":"Taiwan Association for Aerosol Research","doi":"10.4209/aaqr.2017.11.0463","usgsCitation":"Gonzalez-Martin, C., Coronado-Alvarez, N., Teigell-Perez, N., Diaz-Solano, R., Exposito, F., Diaz, J., Griffin, D.W., and Valladares, B., 2018, Analysis of the impact of African dust storms on the presence of enteric viruses in the atmosphere in Tenerife, Spain: Aerosol and Air Quality Research, v. 18, no. 7, p. 1863-1873, https://doi.org/10.4209/aaqr.2017.11.0463.","productDescription":"11 p.","startPage":"1863","endPage":"1873","ipdsId":"IP-075080","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468637,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4209/aaqr.2017.11.0463","text":"Publisher Index Page"},{"id":422974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Spain","otherGeospatial":"Tenerife","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -16.361598997436374,\n 28.505491276599983\n ],\n [\n -16.361598997436374,\n 28.47367524103194\n ],\n [\n -16.307365298199585,\n 28.47367524103194\n ],\n [\n -16.307365298199585,\n 28.505491276599983\n ],\n [\n -16.361598997436374,\n 28.505491276599983\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez-Martin, Cristina","contributorId":331753,"corporation":false,"usgs":false,"family":"Gonzalez-Martin","given":"Cristina","affiliations":[{"id":79278,"text":"Instituto Universitario de Enfermedades Tropicales y Salud Publica de Canarias. Universidad de La Laguna. Avda. Astrofisico Francisco Sanchez, s/n, 38206, San Cristobal de La Laguna, Tenerife, Canary Islands, Spain.","active":true,"usgs":false}],"preferred":false,"id":888698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coronado-Alvarez, Nieves","contributorId":331754,"corporation":false,"usgs":false,"family":"Coronado-Alvarez","given":"Nieves","email":"","affiliations":[{"id":79278,"text":"Instituto Universitario de Enfermedades Tropicales y Salud Publica de Canarias. Universidad de La Laguna. Avda. Astrofisico Francisco Sanchez, s/n, 38206, San Cristobal de La Laguna, Tenerife, Canary Islands, Spain.","active":true,"usgs":false}],"preferred":false,"id":888699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teigell-Perez, Nuria","contributorId":331755,"corporation":false,"usgs":false,"family":"Teigell-Perez","given":"Nuria","affiliations":[{"id":79278,"text":"Instituto Universitario de Enfermedades Tropicales y Salud Publica de Canarias. Universidad de La Laguna. Avda. Astrofisico Francisco Sanchez, s/n, 38206, San Cristobal de La Laguna, Tenerife, Canary Islands, Spain.","active":true,"usgs":false}],"preferred":false,"id":888700,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diaz-Solano, Raquel","contributorId":331756,"corporation":false,"usgs":false,"family":"Diaz-Solano","given":"Raquel","email":"","affiliations":[{"id":79278,"text":"Instituto Universitario de Enfermedades Tropicales y Salud Publica de Canarias. Universidad de La Laguna. Avda. Astrofisico Francisco Sanchez, s/n, 38206, San Cristobal de La Laguna, Tenerife, Canary Islands, Spain.","active":true,"usgs":false}],"preferred":false,"id":888701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Exposito, Francisco","contributorId":331757,"corporation":false,"usgs":false,"family":"Exposito","given":"Francisco","email":"","affiliations":[{"id":79279,"text":"Grupo de Observación de la Tierra y la Atmósfera (GOTA), Universidad de La Laguna, Tenerife, Spain","active":true,"usgs":false}],"preferred":false,"id":888702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diaz, Juan","contributorId":331758,"corporation":false,"usgs":false,"family":"Diaz","given":"Juan","email":"","affiliations":[{"id":79279,"text":"Grupo de Observación de la Tierra y la Atmósfera (GOTA), Universidad de La Laguna, Tenerife, Spain","active":true,"usgs":false}],"preferred":false,"id":888703,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888704,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Valladares, Basilio","contributorId":331759,"corporation":false,"usgs":false,"family":"Valladares","given":"Basilio","affiliations":[{"id":79278,"text":"Instituto Universitario de Enfermedades Tropicales y Salud Publica de Canarias. Universidad de La Laguna. Avda. Astrofisico Francisco Sanchez, s/n, 38206, San Cristobal de La Laguna, Tenerife, Canary Islands, Spain.","active":true,"usgs":false}],"preferred":false,"id":888705,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273282,"text":"70273282 - 2018 - Landscape composition mediates movement and habitat selection in bobcats (Lynx rufus): Implications for conservation planning","interactions":[],"lastModifiedDate":"2026-01-05T14:27:26.260682","indexId":"70273282","displayToPublicDate":"2018-06-20T09:34:51","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Landscape composition mediates movement and habitat selection in bobcats (Lynx rufus): Implications for conservation planning","title":"Landscape composition mediates movement and habitat selection in bobcats (Lynx rufus): Implications for conservation planning","docAbstract":"The analysis of individual movement choices can be used to better understand population-level resource selection and inform management.
We investigated movements and habitat selection of 13 bobcats in Vermont, USA, under the assumption individuals makes choices based upon their current location. Results were used to identify “movement-defined” corridors.
We used GPS-collars and GIS to estimate bobcat movement paths, and extracted statistics on land cover proportions, topography, fine-scale vegetation, roads, and streams within “used” and “available” space surrounding each movement path. Compositional analyses were used to determine habitat preferences with respect to landcover and topography; ratio tests were used to determine if used versus available ratios for vegetation, roads, and streams differed from 1. Results were used to create travel cost maps, a primary input for corridor analysis.
Forested and scrub-rock land cover were most preferred for movement, while developed land cover was least preferred. Preference depended on the composition of the “available” landscape: Bobcats moved > 3 times more quickly through forest and scrub-rock habitat when these habitats were surrounded by agriculture or development than when the available buffer was similarly composed. Overall, forest edge, wetland edge and higher stream densities were selected, while deep forest core and high road densities were not selected. Landscape-scale connectivity maps differed depending on whether habitat suitability, preference, or selection informed the travel cost map.
Both local and landscape scale land cover characteristics affect habitat preferences and travel speed of bobcats, which in turn can inform management and conservation activities.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-018-0654-8","usgsCitation":"Abouelezz, H.G., Donovan, T.M., Mickey, R.M., Murdoch, J.D., Freeman, M., and Royar, K., 2018, Landscape composition mediates movement and habitat selection in bobcats (Lynx rufus): Implications for conservation planning: Landscape Ecology, v. 33, p. 1301-1318, https://doi.org/10.1007/s10980-018-0654-8.","productDescription":"18 p.","startPage":"1301","endPage":"1318","ipdsId":"IP-023048","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":498277,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-018-0654-8","text":"Publisher Index Page"},{"id":498208,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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M.","contributorId":364665,"corporation":false,"usgs":false,"family":"Mickey","given":"Ruth","middleInitial":"M.","affiliations":[],"preferred":false,"id":953020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murdoch, James D.","contributorId":364666,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","middleInitial":"D.","affiliations":[],"preferred":false,"id":953021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Mark","contributorId":171650,"corporation":false,"usgs":false,"family":"Freeman","given":"Mark","email":"","affiliations":[],"preferred":false,"id":953022,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Royar, Kimberly","contributorId":300053,"corporation":false,"usgs":false,"family":"Royar","given":"Kimberly","email":"","affiliations":[{"id":65007,"text":"Vermont Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":953023,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197820,"text":"70197820 - 2018 - Avian keratin disorder of Alaska black-capped chickadees is associated with Poecivirus infection","interactions":[],"lastModifiedDate":"2018-06-20T19:33:20","indexId":"70197820","displayToPublicDate":"2018-06-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3697,"text":"Virology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Avian keratin disorder of Alaska black-capped chickadees is associated with Poecivirus infection","docAbstract":"Background
Avian keratin disorder (AKD) is an epizootic of debilitating beak deformities, first documented in black-capped chickadees (Poecile atricapillus) in Alaska during the late 1990s. Similar deformities have now been recorded in dozens of species of birds across multiple continents. Despite this, the etiology of AKD has remained elusive, making it difficult to assess the impacts of this disease on wild populations. We previously identified an association between infection with a novel picornavirus, Poecivirus, and AKD in a small cohort of black-capped chickadees.
Methods
To test if the association between Poecivirus and AKD holds in a larger study population, we used targeted PCR followed by Sanger sequencing to screen 124 symptomatic and asymptomatic black-capped chickadees for Poecivirus infection. We further compared the efficacy of multiple non-terminal field sampling methods (buccal swabs, cloacal swabs, fecal samples, and blood samples) for Poecivirus screening. Finally, we used both in situ hybridization and a strand-specific expression assay to localize Poecivirus to beak tissue of AKD-positive individuals and to determine if virus is actively replicating in beak tissue.
Results
Poecivirus was detected in 28/28 (100%) individuals with AKD, but only 9/96 (9.4%) asymptomatic individuals with apparently normal beaks (p < 0.0001). We found that cloacal swabs are the most sensitive of these sample types for detecting Poecivirus in birds with AKD, but that buccal swabs should be combined with cloacal swabs in evaluating the infection status of asymptomatic birds. Finally, we used both in situ hybridization and a strand-specific expression assay to localize Poecivirus to beak tissue of AKD-positive individuals and to provide evidence of active viral replication.
Conclusion
The data presented here show a strong, statistically significant relationship between Poecivirus infection and AKD, and provide evidence that Poecivirus is indeed an avian virus, infecting and actively replicating in beak tissue of AKD-affected BCCH. Taken together, these data corroborate and extend the evidence for a potential causal association between Poecivirus and AKD in the black-capped chickadee. Poecivirus continues to warrant further investigation as a candidate agent of AKD.
","language":"English","publisher":"BMC","doi":"10.1186/s12985-018-1008-5","usgsCitation":"Zylberberg, M., Van Hemert, C.R., Handel, C.M., and DeRisi, J.L., 2018, Avian keratin disorder of Alaska black-capped chickadees is associated with Poecivirus infection: Virology Journal, v. 15, Article 100; 9 p., https://doi.org/10.1186/s12985-018-1008-5.","productDescription":"Article 100; 9 p.","ipdsId":"IP-092906","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":460885,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12985-018-1008-5","text":"Publisher Index Page"},{"id":437850,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93OCR4L","text":"USGS data release","linkHelpText":"Genetic Data Associated with Avian Keratin Disorder and Poecivirus in Black-capped Chickadees, Alaska, 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PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-15","publicationStatus":"PW","scienceBaseUri":"5b46e554e4b060350a15d0e5","contributors":{"authors":[{"text":"Zylberberg, Maxine","contributorId":181767,"corporation":false,"usgs":false,"family":"Zylberberg","given":"Maxine","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":738651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Hemert, Caroline R. 0000-0002-6858-7165 cvanhemert@usgs.gov","orcid":"https://orcid.org/0000-0002-6858-7165","contributorId":3592,"corporation":false,"usgs":true,"family":"Van Hemert","given":"Caroline","email":"cvanhemert@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":738650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Handel, Colleen M. 0000-0002-0267-7408 cmhandel@usgs.gov","orcid":"https://orcid.org/0000-0002-0267-7408","contributorId":3067,"corporation":false,"usgs":true,"family":"Handel","given":"Colleen","email":"cmhandel@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":738652,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeRisi, Joseph L.","contributorId":172863,"corporation":false,"usgs":false,"family":"DeRisi","given":"Joseph","email":"","middleInitial":"L.","affiliations":[{"id":27105,"text":"University of California San Francisco; Howard Hughes Medical Institute","active":true,"usgs":false}],"preferred":false,"id":738653,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197767,"text":"70197767 - 2018 - Analysis of mean seismic ground motion and its uncertainty based on the UCERF3 geologic slip rate model with uncertainty for California","interactions":[],"lastModifiedDate":"2018-07-03T10:58:42","indexId":"70197767","displayToPublicDate":"2018-06-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of mean seismic ground motion and its uncertainty based on the UCERF3 geologic slip rate model with uncertainty for California","docAbstract":"The Uniform California Earthquake Rupture Forecast v.3 (UCERF3) model (Field et al., 2014) considers epistemic uncertainty in fault‐slip rate via the inclusion of multiple rate models based on geologic and/or geodetic data. However, these slip rates are commonly clustered about their mean value and do not reflect the broader distribution of possible rates and associated probabilities. Here, we consider both a double‐truncated 2σ Gaussian and a boxcar distribution of slip rates and use a Monte Carlo simulation to sample the entire range of the distribution for California fault‐slip rates. We compute the seismic hazard following the methodology and logic‐tree branch weights applied to the 2014 national seismic hazard model (NSHM) for the western U.S. region (Petersen et al., 2014, 2015). By applying a new approach developed in this study to the probabilistic seismic hazard analysis (PSHA) using precomputed rates of exceedance from each fault as a Green’s function, we reduce the computer time by about 10^5‐fold and apply it to the mean PSHA estimates with 1000 Monte Carlo samples of fault‐slip rates to compare with results calculated using only the mean or preferred slip rates. The difference in the mean probabilistic peak ground motion corresponding to a 2% in 50‐yr probability of exceedance is less than 1% on average over all of California for both the Gaussian and boxcar probability distributions for slip‐rate uncertainty but reaches about 18% in areas near faults compared with that calculated using the mean or preferred slip rates. The average uncertainties in 1σ peak ground‐motion level are 5.5% and 7.3% of the mean with the relative maximum uncertainties of 53% and 63% for the Gaussian and boxcar probability density function (PDF), respectively.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170114","usgsCitation":"Zeng, Y., 2018, Analysis of mean seismic ground motion and its uncertainty based on the UCERF3 geologic slip rate model with uncertainty for California: Seismological Research Letters, v. 89, no. 4, p. 1410-1419, https://doi.org/10.1785/0220170114.","productDescription":"10 p.","startPage":"1410","endPage":"1419","ipdsId":"IP-094845","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":355189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-25","publicationStatus":"PW","scienceBaseUri":"5b46e556e4b060350a15d0f5","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738438,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70197722,"text":"70197722 - 2018 - Evolution of the 2015 Cotopaxi eruption revealed by combined geochemical & seismic observations","interactions":[],"lastModifiedDate":"2018-08-31T10:55:21","indexId":"70197722","displayToPublicDate":"2018-06-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Evolution of the 2015 Cotopaxi eruption revealed by combined geochemical & seismic observations","docAbstract":"Through integration of multiple data streams to monitor volcanic unrest scientists are able to make more robust eruption forecast and to obtain a more holistic interpretation of volcanic systems. We examined gas emission and gas geochemistry, seismic and petrologic data recorded during the 2015 unrest of Cotopaxi (Ecuador) in order to decipher the origin and temporal evolution of this eruption. Identification of families of similar seismic events and the use of seismic amplitude ratios reveals temporal changes in volcanic processes. SO2 (300 to 24000 t/d), BrO/SO2 (5-10 x10-5), SO2/HCl (5.8 ± 4.8 and 6.6 ± 3.0) and CO2/SO2 (0.6 to 2.1) measured throughout the eruption indicate a shallow magmatic source. Bulk ash and glass chemistry indicate a homogenous andesitic (SiO2 wt%=56.94 ± 0.25) magma having undergone extensive S-exsolution and degassing during ascent. These data lead us to interpret this eruption as a magma intrusion and ascend to shallow levels. The intrusion progressively interacted with the hydrothermal system, boiled off water, and produced hydromagmatic explosions. A small volume of this intrusion continued to fragment and produced episodic ash emissions until it was sufficiently degassed and rheologically stiff. Based on the 470 kt of measured SO2 we estimate that ~ 65.3 x106 m3 of magma were required to supply the emitted gases. This volume exceeds the volume of erupted juvenile material by a factor of 50. This result emphasizes the importance of careful monitoring of Cotopaxi to identify the intrusion of a new batch of magma, which could rejuvenate the non-erupted material.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018GC007514","usgsCitation":"Hidalgo, S., Battaglia, J., Arellano, S., Sierra, D., Bernard, B., Parra, R., Kelly, P.J., Dinger, F., Barrington, C., and Samaniego, P., 2018, Evolution of the 2015 Cotopaxi eruption revealed by combined geochemical & seismic observations: Geochemistry, Geophysics, Geosystems, v. 19, no. 7, p. 2087-2108, https://doi.org/10.1029/2018GC007514.","productDescription":"22 p.","startPage":"2087","endPage":"2108","ipdsId":"IP-077610","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468647,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2018gc007514","text":"External Repository"},{"id":355148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-16","publicationStatus":"PW","scienceBaseUri":"5b46e559e4b060350a15d107","contributors":{"authors":[{"text":"Hidalgo, Silvana","contributorId":205717,"corporation":false,"usgs":false,"family":"Hidalgo","given":"Silvana","email":"","affiliations":[{"id":37151,"text":"Instituto Geofísico – Escuela Politécnica Nacional, Quito, Ecuador","active":true,"usgs":false}],"preferred":false,"id":738285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Battaglia, Jean","contributorId":205718,"corporation":false,"usgs":false,"family":"Battaglia","given":"Jean","email":"","affiliations":[{"id":37152,"text":"Laboratoire Magmas et Volcans, Université Blaise Pascal - CNRS - IRD, OPGC, Clermont Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":738286,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arellano, Santiago","contributorId":205719,"corporation":false,"usgs":false,"family":"Arellano","given":"Santiago","affiliations":[{"id":37153,"text":"Department of Earth and Space Sciences – Chalmers University of Technology, Göteborg, Sweden","active":true,"usgs":false}],"preferred":false,"id":738287,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sierra, Daniel","contributorId":205720,"corporation":false,"usgs":false,"family":"Sierra","given":"Daniel","email":"","affiliations":[{"id":37151,"text":"Instituto Geofísico – Escuela Politécnica Nacional, Quito, Ecuador","active":true,"usgs":false}],"preferred":false,"id":738288,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bernard, Benjamin","contributorId":178529,"corporation":false,"usgs":false,"family":"Bernard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":738290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parra, Rene","contributorId":205721,"corporation":false,"usgs":false,"family":"Parra","given":"Rene","email":"","affiliations":[{"id":37154,"text":"Instituto de Simulación Computacional, Colegio de Ciencias e Ingenierías - Universidad San Francisco de Quito, Quito, Ecuador","active":true,"usgs":false}],"preferred":false,"id":738289,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":738284,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dinger, Florian","contributorId":205723,"corporation":false,"usgs":false,"family":"Dinger","given":"Florian","email":"","affiliations":[{"id":37156,"text":"Max-Planck Institut for Chemistry, Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":738292,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barrington, Charlotte","contributorId":205722,"corporation":false,"usgs":false,"family":"Barrington","given":"Charlotte","email":"","affiliations":[{"id":37155,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore 639798","active":true,"usgs":false}],"preferred":false,"id":738291,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Samaniego, Pablo","contributorId":205724,"corporation":false,"usgs":false,"family":"Samaniego","given":"Pablo","email":"","affiliations":[{"id":37157,"text":"Université Clermont Auvergne, CNRS, IRD, OPGC, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":738293,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70197625,"text":"sim3409 - 2018 - Bathymetric contour map, surface area and capacity table, and bathymetric difference map for Clearwater Lake near Piedmont, Missouri, 2017","interactions":[],"lastModifiedDate":"2018-09-25T08:04:18","indexId":"sim3409","displayToPublicDate":"2018-06-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3409","title":"Bathymetric contour map, surface area and capacity table, and bathymetric difference map for Clearwater Lake near Piedmont, Missouri, 2017","docAbstract":"Clearwater Lake, on the Black River near Piedmont in Reynolds County, Missouri, was constructed in 1948 and is operated by the U.S. Army Corps of Engineers for flood-risk reduction, recreation, and fish and wildlife habitat. The lake area is about 1,800 acres with about 34 miles of shoreline at the conservation pool elevation of 498 feet. Since the completion of the lake in 1948, sedimentation likely has caused the storage capacity of the lake to decrease gradually. The loss of storage capacity can decrease the effectiveness of the lake to mitigate flooding, and excessive sediment accumulation also can reduce aquatic habitat in some areas of the lake. Many lakes operated by the U.S. Army Corps of Engineers have periodic bathymetric and sediment surveys to monitor the status of the lake. The U.S. Geological Survey completed one such survey of Clearwater Lake in 2008 during a period of high lake level using bathymetric surveying equipment consisting of a multibeam echosounder, a singlebeam echosounder, 1/3 arc-second National Elevation Dataset data (used outside the multibeam echosounder survey extent), and the waterline derived from 2008 aerial light detection and ranging (lidar) data. In May 2017, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, surveyed the bathymetry of Clearwater Lake to prepare an updated bathymetric map and a surface area and capacity table. The 2008 survey was contrasted with the 2017 survey to document the changes in the bathymetric surface of the lake.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3409","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Richards, J.M., and Huizinga, R.J., 2018, Bathymetric contour map, surface area and capacity table, and bathymetric difference map for Clearwater Lake near Piedmont, Missouri, 2017: U.S. Geological Survey Scientific Investigations Map 3409, 1 sheet, https://doi.org/10.3133/sim3409.","productDescription":"Sheet: 36.0 x 36.0 inches; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095869","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":355036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3409/coverthb2.jpg"},{"id":355141,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3409/sim3409.pdf","text":"Map","size":"9.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3409"},{"id":355142,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN44BJ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetric data for Clearwater Lake near Piedmont, Missouri, 2017"}],"country":"United States","state":"Missouri","city":"Piedmont","otherGeospatial":"Clearwater Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.9,\n 37.33\n ],\n [\n -90.67,\n 37.33\n ],\n [\n -90.67,\n 37.0833\n ],\n [\n -90.9,\n 37.0833\n ],\n [\n -90.9,\n 37.33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center, Missouri Office
U.S. Geological Survey
400 Independence Road
Rolla, MO 65401
There are an estimated 16,000 nature related remote web cameras that provide users around the world with an opportunity to view wildlife. Because there is no monetary price to view the webcams, we utilise variations in the viewers’ opportunity cost of time to estimate consumer surplus. We apply this model to a large sample (n = 2649) of the more than 10 million viewers of Alaska's Katmai National Park and Preserve brown bear webcams. The resulting consumer surplus is around \\$11 per hour of viewing. When applied to the 2.42 million viewer hours, this yields a benefit of \\$27 million annually. Since there are limits on the number of visitors as well as high costs of visiting this remote site, the aggregate webcam viewing value is more than twice the aggregate on-site viewing value. With minimal survey data required to apply this model, we believe it has broad applicability to other nature-related webcams around the world.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/21606544.2018.1483842","usgsCitation":"Loomis, J.B., Richardson, L., Huber, C., Skibins, J., and Sharp, R., 2018, A method to value nature-related webcam viewing: The value of virtual use with application to brown bear webcam viewing: Journal of Environmental Economics and Policy, v. 7, no. 4, p. 452-462, https://doi.org/10.1080/21606544.2018.1483842.","productDescription":"11 p.","startPage":"452","endPage":"462","ipdsId":"IP-090967","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":358820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-18","publicationStatus":"PW","scienceBaseUri":"5c10a99ae4b034bf6a7e5359","contributors":{"authors":[{"text":"Loomis, John B.","contributorId":197268,"corporation":false,"usgs":false,"family":"Loomis","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":749758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richardson, Leslie","contributorId":197525,"corporation":false,"usgs":false,"family":"Richardson","given":"Leslie","affiliations":[],"preferred":false,"id":749759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huber, Christopher 0000-0001-8446-8134 chuber@usgs.gov","orcid":"https://orcid.org/0000-0001-8446-8134","contributorId":127600,"corporation":false,"usgs":true,"family":"Huber","given":"Christopher","email":"chuber@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":749757,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skibins, Jeffrey","contributorId":210077,"corporation":false,"usgs":false,"family":"Skibins","given":"Jeffrey","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":749760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharp, Ryan","contributorId":168598,"corporation":false,"usgs":false,"family":"Sharp","given":"Ryan","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":749761,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204848,"text":"70204848 - 2018 - A framework for identifying and characterising coral reef “oases” against a backdrop of degradation","interactions":[],"lastModifiedDate":"2020-09-01T14:08:16.952561","indexId":"70204848","displayToPublicDate":"2018-06-18T08:10:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A framework for identifying and characterising coral reef “oases” against a backdrop of degradation","docAbstract":"This paper examines adaptation responses to climate change through adjustment of agricultural land use. The climate drivers we examine are changes in long-term climate normals (e.g., 10-year moving averages) and changes in inter-annual climate variability. Using US county level data over 1982 to 2012 from Census of Agriculture, we find that impacts of long-term climate normals are as important as that of inter-annual climate variability. Projecting into the future, we find projected climate change will lead to an expansion in crop land share across the northern and interior western United States with decreases in the south. We also find that grazing land share increases in southern regions and Inland Pacific Northwest and declines in the northern areas. However, the extent to which the adaptation potential would be is dependent on the climate model, emission scenario and time horizon under consideration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.landusepol.2018.05.057","usgsCitation":"Mu, J.E., McCarl, B.A., Sleeter, B.M., Abatzoglou, J.T., and Zhang, H., 2018, Adaptation with climate uncertainty: An examination of agricultural land use in the United States: Land Use Policy, v. 77, p. 392-401, https://doi.org/10.1016/j.landusepol.2018.05.057.","productDescription":"10 p.","startPage":"392","endPage":"401","ipdsId":"IP-076144","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468652,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.landusepol.2018.05.057","text":"Publisher Index Page"},{"id":355131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e559e4b060350a15d10b","contributors":{"authors":[{"text":"Mu, Jianhong E.","contributorId":75840,"corporation":false,"usgs":true,"family":"Mu","given":"Jianhong","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":738230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarl, Bruce A.","contributorId":58173,"corporation":false,"usgs":true,"family":"McCarl","given":"Bruce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":738228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":738226,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abatzoglou, John T.","contributorId":191729,"corporation":false,"usgs":false,"family":"Abatzoglou","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":33345,"text":" University of Idaho","active":true,"usgs":false}],"preferred":false,"id":738227,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Hongliang","contributorId":205709,"corporation":false,"usgs":false,"family":"Zhang","given":"Hongliang","email":"","affiliations":[{"id":37150,"text":"University of Neuchâtel","active":true,"usgs":false}],"preferred":false,"id":738229,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221454,"text":"70221454 - 2018 - Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model","interactions":[],"lastModifiedDate":"2021-06-16T14:14:34.261411","indexId":"70221454","displayToPublicDate":"2018-06-16T08:52:24","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model","docAbstract":"We test the use of a mixed-effects model for estimating lag to peak for small basins in Maine (drainage areas from 0.8 to 78 km2). Lag to peak is defined as the time between the center of volume of the excess rainfall during a storm event and the resulting peak streamflow. A mixed-effects model allows for multiple observations at sites without violating model assumptions inherent in traditional ordinary least squares models, which assume each observation is independent. The mixed model includes basin drainage area and maximum 15-min rainfall depth for individual storms as explanatory features. Based on a remove-one-site cross-validation analysis, the prediction errors of this model ranged from 42% to +73%. The mixed model substantially outperformed three published models for lag to peak and one published model for centroid lag for estimating lag to peak for small basins in Maine. Lag to peak estimates are a key input to rainfallrunoff models used to design hydraulic infrastructure. The improved accuracy and consistency with model assumptions indicates that mixed models may provide increased data utilization that could enhance models and estimates of lag to peak in other regions.","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.12653","usgsCitation":"Lombard, P.J., and Holtschlag, D., 2018, Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model: Journal of the American Water Resources Association (JAWRA), v. 54, no. 4, p. 949-961, https://doi.org/10.1111/1752-1688.12653.","productDescription":"13 p.","startPage":"949","endPage":"961","ipdsId":"IP-089128","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437859,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PK0F3D","text":"USGS data release","linkHelpText":"Precipitation and streamflow data for computing lag to peak at selected stations in Maine"},{"id":386535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.08203125,\n 47.517200697839414\n ],\n [\n -70.0048828125,\n 46.558860303117164\n ],\n [\n -70.9716796875,\n 45.27488643704891\n ],\n [\n -70.751953125,\n 43.100982876188546\n ],\n [\n -66.8408203125,\n 44.84029065139799\n ],\n [\n -67.3681640625,\n 45.82879925192134\n ],\n [\n -67.9833984375,\n 47.368594345213374\n ],\n [\n -69.08203125,\n 47.517200697839414\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"4","noUsgsAuthors":false,"publicationDate":"2018-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":205225,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holtschlag, David 0000-0001-5185-4928","orcid":"https://orcid.org/0000-0001-5185-4928","contributorId":215360,"corporation":false,"usgs":true,"family":"Holtschlag","given":"David","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817755,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70197650,"text":"70197650 - 2018 - Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity","interactions":[],"lastModifiedDate":"2021-08-11T18:52:48.609974","indexId":"70197650","displayToPublicDate":"2018-06-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity","docAbstract":"High-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance.
We introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.
We used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.
Simulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corroborating previous findings for this species.
We show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting migratory connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-018-0637-9","usgsCitation":"van Toor, M.L., Kranstauber, B., Newman, S.H., Prosser, D.J., Takekawa, J., Technitis, G., Weibel, R., Wikelski, M., and Safi, K., 2018, Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity: Landscape Ecology, v. 33, no. 6, p. 879-893, https://doi.org/10.1007/s10980-018-0637-9.","productDescription":"15 p.","startPage":"879","endPage":"893","ipdsId":"IP-084732","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-018-0637-9","text":"Publisher Index Page"},{"id":355084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-26","publicationStatus":"PW","scienceBaseUri":"5b46e566e4b060350a15d117","contributors":{"authors":[{"text":"van Toor, Marielle L.","contributorId":205670,"corporation":false,"usgs":false,"family":"van Toor","given":"Marielle","email":"","middleInitial":"L.","affiliations":[{"id":37137,"text":"Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology","active":true,"usgs":false}],"preferred":false,"id":738069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranstauber, Bart","contributorId":205671,"corporation":false,"usgs":false,"family":"Kranstauber","given":"Bart","email":"","affiliations":[{"id":37138,"text":"Department of Evolutionary Biology and Environmental Studies, University of Zurich","active":true,"usgs":false}],"preferred":false,"id":738070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newman, Scott H.","contributorId":199129,"corporation":false,"usgs":false,"family":"Newman","given":"Scott","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":738071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":738068,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Takekawa, John Y. 0000-0003-0217-5907","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":203805,"corporation":false,"usgs":false,"family":"Takekawa","given":"John Y.","affiliations":[{"id":36724,"text":"Audubon California, Richardson Bay Audubon Center and Sanctuary, Tiburon, CA","active":true,"usgs":false}],"preferred":false,"id":738072,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Technitis, Georgios","contributorId":205672,"corporation":false,"usgs":false,"family":"Technitis","given":"Georgios","email":"","affiliations":[{"id":37139,"text":"Department of Geography, University of Zurich","active":true,"usgs":false}],"preferred":false,"id":738073,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weibel, Robert","contributorId":205673,"corporation":false,"usgs":false,"family":"Weibel","given":"Robert","email":"","affiliations":[{"id":37139,"text":"Department of Geography, University of Zurich","active":true,"usgs":false}],"preferred":false,"id":738074,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wikelski, Martin","contributorId":205674,"corporation":false,"usgs":false,"family":"Wikelski","given":"Martin","email":"","affiliations":[{"id":37137,"text":"Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology","active":true,"usgs":false}],"preferred":false,"id":738075,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Safi, Kamran","contributorId":205675,"corporation":false,"usgs":false,"family":"Safi","given":"Kamran","email":"","affiliations":[{"id":37137,"text":"Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology","active":true,"usgs":false}],"preferred":false,"id":738076,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70198069,"text":"70198069 - 2018 - DDT and related compounds in pore water of shallow sediments on the Palos Verdes Shelf, California, USA","interactions":[],"lastModifiedDate":"2018-07-16T11:06:02","indexId":"70198069","displayToPublicDate":"2018-06-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2662,"text":"Marine Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"DDT and related compounds in pore water of shallow sediments on the Palos Verdes Shelf, California, USA","docAbstract":"For nearly two and a half decades following World War II, production wastes from the world's largest manufacturer of technical DDT (1-chloro-4-[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene) were discharged into sewers of Los Angeles County. Following treatment, the wastes were released via a submarine outfall system to nearshore coastal waters where a portion accumulated in shallow sediments of the Palos Verdes Shelf (PVS). An investigation of the pore-water geochemistry of DDT-related compounds (DDX) was undertaken in an effort to understand factors controlling the rate of reductive dechlorination (RDC) of the major DDT degradate, 4,4′-DDE (1-chloro-4-[2,2-dichloro-1-(4-chlorophenyl)ethenyl]benzene). Equilibrium matrix-solid phase microextraction (matrix-SPMEeq) combined with automated thermal desorption-gas chromatography/mass spectrometry (TDGC/MS) was used to determine freely dissolved concentrations of ten DDX analytes in sediment cores collected from three locations on the PVS (stations 3C, 6C, 8C, which are 7 km, 2 km, and 0 km, respectively, downcurrent from the outfall system). Pore-water concentrations (pM) of the principal DDX compounds involved in RDC were: 3C-DDE: 6.0–24, DDMU (1-chloro-4-[2-chloro-1-(4-chlorophenyl)ethenyl]benzene): 11–160, DDNU (1-chloro-4-[1-(4-chlorophenyl)ethenyl]benzene): 1.8–68; 6C-DDE: 5.6–170, DDMU: 5.6–177, DDNU: 1.7–87; 8CDDE: 27–212, DDMU: 31–403, DDNU: 5.5–89. Variations in the spatial distribution of DDX analytes in pore water reflect several factors including proximity to the outfalls, RDC reaction rates, and natural variability in sedimentation and post-depositional transport processes. A comparison of pore-water data produced using matrix-SPMEeq/TD-GC/MS and whole-core squeezing/solvent extraction/liquid injection-GC/MS indicates that the majority of the DDE in the upper sediment column (≤about 10 cm) is associated with dissolved/colloidal organic matter. Below that depth, freely-dissolved DDE predominates. The principal organic geochemical phase controlling sorption of DDE in PVS sediments are residual hydrocarbons, the vast majority of which originated from petroleum refinery wastes. Organic carbon-normalized sediment-water distribution coefficients (KOC) were calculated from solid-phase and pore-water concentrations of DDX and organic carbon. Log KOC values (L/kg) were relatively invariant across the shelf and with depth in the sediment column. Shelf-wide compound-specific coefficients (log KOC) were: DDE: 7.5 ± 0.11, DDMU: 6.92 ± 0.13, DDNU: 6.37 ± 0.19. The spatial uniformity of KOC means that biological exposure and availability of the DDX compounds can, in principle, be estimated from solid-phase chemical measurements.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marchem.2018.05.003","usgsCitation":"Eganhouse, R.P., DiFilippo, E.L., Pontolillo, J., Orem, W.H., Hackley, P.C., and Edwards, B., 2018, DDT and related compounds in pore water of shallow sediments on the Palos Verdes Shelf, California, USA: Marine Chemistry, v. 203, p. 78-90, https://doi.org/10.1016/j.marchem.2018.05.003.","productDescription":"13 p.","startPage":"78","endPage":"90","ipdsId":"IP-088771","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marchem.2018.05.003","text":"Publisher Index Page"},{"id":355658,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Palos Verdes Shelf","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -118.35111111111111,33.66777777777777 ], [ -118.35111111111111,33.7175 ], [ -118.28444444444445,33.7175 ], [ -118.28444444444445,33.66777777777777 ], [ -118.35111111111111,33.66777777777777 ] ] ] } } ] }","volume":"203","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc430e4b0f5d57878ea13","contributors":{"authors":[{"text":"Eganhouse, Robert P. 0000-0002-2075-5908 eganhous@usgs.gov","orcid":"https://orcid.org/0000-0002-2075-5908","contributorId":206243,"corporation":false,"usgs":true,"family":"Eganhouse","given":"Robert","email":"eganhous@usgs.gov","middleInitial":"P.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":739877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiFilippo, Erica L.","contributorId":90449,"corporation":false,"usgs":true,"family":"DiFilippo","given":"Erica","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":739878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pontolillo, James 0000-0002-1075-1313 jpontoli@usgs.gov","orcid":"https://orcid.org/0000-0002-1075-1313","contributorId":206244,"corporation":false,"usgs":true,"family":"Pontolillo","given":"James","email":"jpontoli@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":739879,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":739880,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":739881,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, Brian 0000-0002-4655-8208 bedwards@usgs.gov","orcid":"https://orcid.org/0000-0002-4655-8208","contributorId":206245,"corporation":false,"usgs":true,"family":"Edwards","given":"Brian","email":"bedwards@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739882,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197656,"text":"70197656 - 2018 - Quantifying anthropogenic contributions to century-scale groundwater salinity changes, San Joaquin Valley, California, USA","interactions":[],"lastModifiedDate":"2018-06-18T11:04:24","indexId":"70197656","displayToPublicDate":"2018-06-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying anthropogenic contributions to century-scale groundwater salinity changes, San Joaquin Valley, California, USA","docAbstract":"Total dissolved solids (TDS) concentrations in groundwater tapped for beneficial uses (drinking water, irrigation, freshwater industrial) have increased on average by about 100 mg/L over the last 100 years in the San Joaquin Valley, California (SJV). During this period land use in the SJV changed from natural vegetation and dryland agriculture to dominantly irrigated agriculture with growing urban areas. Century-scale salinity trends were evaluated by comparing TDS concentrations and major ion compositions of groundwater from wells sampled in 1910 (Historic) to data from wells sampled in 1993-2015 (Modern). TDS concentrations in subregions of the SJV, the southern (SSJV), western (WSJV), northeastern (NESJV), and southeastern (SESJV) were calculated using a cell-declustering method. TDS concentrations increased in all regions, with the greatest increases found in the SSJV and SESJV. Evaluation of the Modern data from the NESJV and SESJV found higher TDS concentrations in recently recharged (post-1950) groundwater from shallow (< 50 m) wells surrounded predominantly by agricultural land uses, while premodern (pre-1950) groundwater from deeper wells, and recently recharged groundwater from wells surrounded by mainly urban, natural, and mixed land uses had lower TDS concentrations, approaching the TDS concentrations in the Historic groundwater. For the NESJV and SESJV, inverse geochemical modeling with PHREEQC indicated that weathering of primary silicate minerals accounted for the majority of the increase in TDS concentrations, contributing more than nitrate from fertilizers and sulfate from soil amendments combined. Bicarbonate showed the greatest increase among major ions, resulting from enhanced silicate weathering due to recharge of irrigation water enriched in CO2 during the growing season. The results of this study demonstrate that large anthropogenic changes to the hydrologic regime, like massive development of irrigated agriculture in semi-arid areas like the SJV, can cause large changes in groundwater quality on a regional scale.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.05.333","usgsCitation":"Hansen, J.A., Jurgens, B., and Fram, M.S., 2018, Quantifying anthropogenic contributions to century-scale groundwater salinity changes, San Joaquin Valley, California, USA: Science of the Total Environment, v. 642, p. 125-136, https://doi.org/10.1016/j.scitotenv.2018.05.333.","productDescription":"12 p.","startPage":"125","endPage":"136","ipdsId":"IP-083514","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":460889,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2018.05.333","text":"Publisher Index Page"},{"id":437861,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7319T3K","text":"USGS data release","linkHelpText":"Groundwater-quality data and ancillary data for selected wells in the San Joaquin Valley, California, 1900-2015"},{"id":355083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley, San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.71728515624999,\n 40.195659093364654\n ],\n [\n -122.51953124999999,\n 39.791654835253425\n ],\n [\n -122.3876953125,\n 39.487084981687495\n ],\n [\n -122.2119140625,\n 39.198205348894795\n ],\n [\n -122.08007812499999,\n 38.92522904714054\n ],\n [\n -121.92626953124999,\n 38.496593518947584\n ],\n [\n 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